Patent Application
20250013642
The present invention pertains in general to the collection and analysis of data using an established data network, and in particular to supports for configurable data collection and analysis operations.
Networks, such as fifth generation (5G) networks as defined by the 3rd Generation Partnership Project (3GPP™), utilizing data analytics may include infrastructure devices (e.g. access points or base stations, core network devices, gateways, etc.) as well as client devices (e.g. mobile devices, user equipment (UE) devices, Internet of things (IoT) end devices, etc.).
Data analytics functions can be used by a network to provide a data service. Such a data service may include a data collection service, a data privacy protection service, a data analytics service and a data delivery service. The network can collect, via data analytics functions, data from data sources and deliver the collected data to data consumers. Data consumers can use the data to perform tasks such as data analytics, artificial intelligence (AI) training and AI inference.
It is expected that, particularly in future networks such as 6th Generation (6G) networks, a large number of data sources and data items may be scattered across the network. For example, in addition to the network functions (NFs), User Equipment (UEs), radio access networks (RANs), mobile edge computing (MEC) nodes, routers, and edge nodes, there may be other data sources. Such other data sources may involve vertical service providers such as IoT service providers and automatic driving service providers. These vertical service providers can be parts of network functions or a 3rd party. Each data source may possess datasets collected from its connected network devices (e.g. identified by device IDs, UE IDs, etc.). For example, each vertical service provider may possess its data (e.g. sensing data) collected from its subscribers. Data consumer may require the data from such data sources to perform tasks such as data analytics, AI training and AI inference.
In a future sixth generation (6G) network, data analytics functions may be intermediate network functions used to connect data sources and data consumers. Accordingly, data consumer and data source may be transparent to each other and each may not necessarily be aware of the existence of the other. However, existing proposals for implementing data analytics functions (e.g. in a future 6G network) are subject to improvement in various ways. For example, in large networks, existing implementations or strategies (e.g. as deployed in a 5G network) may be inefficient or even ineffective in their capability to collect data to support a specific task in a future 6G network, especially when a data consumer has limited information.
Therefore, there is a need for a method, apparatus and system for configurable data collection in a networked data analytics and management context, that obviates or mitigates one or more deficiencies of the prior art.
This background information is provided to reveal information believed by the applicant to be of possible relevance to the present invention. No admission is necessarily intended, nor should be construed, that any of the preceding information constitutes prior art against the present invention.
An object of embodiments of the present invention is to provide method, apparatus and system for configurable data collection in a networked data analytics and management (DAM) context. Embodiments may facilitate some or all of: automatic registration and organization of data sources, configurable task-specific data collection, data preprocessing, or both, automatic detection of correlations between different obtainable data, and interactive support for discovering appropriate data collection actions.
In accordance with an embodiment of the present disclosure, there is provided a networked computerized system including a data representation manager (DRM) module, a data collection and preprocessing (DCP) module, a correlation manager (CM) module, and a data source discovery (DSD) module. It is noted that, in other embodiments, one or more of the DRM module, the DCP module, the CM module, and the DSD module may be omitted. The DRM module is configured to obtain at least one representation of one or more data sources, each representation corresponding to a data source of said one or more data sources, and the representation used to represent a characteristic of said data source. The DCP module is configured to interact with members of the one or more data sources to obtain data therefrom in accordance with a set of data collection actions. The CM module is configured to determine at least one correlation between different data obtainable from said one or more data sources, said at least one correlation being based at least in part on said at least one representation. The DSD module is configured to: interact with a data consumer component and the CM module to determine the set of data collection actions to be performed in support of a request from the data consumer; and configure the DCP module to perform the set of data collection actions.
In accordance with an embodiment of the present disclosure, the set of data collection actions may include: collecting raw data from the one or more data sources; or collecting raw data from the one or more data sources and preprocessing the raw data collected from the one or more data sources.
In accordance with an embodiment of the present disclosure, preprocessing the raw data may include one or more of merging the raw data; filtering the raw data; cleaning the raw data; and normalizing the raw data.
In accordance with an embodiment of the present disclosure, said at least one representation may include one or more of: information indicative of said data source; and information indicative of data that said data source is capable of providing. The information indicative of said data source may include one or more of: data source type; an application category that data from said data source is usable for; a location of said data source; a service that can be provided by said data source; an ability of said data source; and contextual information of said data source. The information indicative of data that said data source is capable of providing may include one or more of: metadata; a data name; a data title; a data tag; a key word; data semantics; a data attribute; a data feature; a data schema; a data format; and an application category that the data can be used for.
In accordance with an embodiment of the present disclosure, the DRM module may be configured to receive the at least one representation. The DRM module may be configured to generate said at least one representation at least in part by: requesting a report from said data source; and receiving and processing the report to obtain the at least one representation. Requesting the report from said data source may include transmitting representation configuration information to said data source, said representation configuration information indicating a representation of data to be provided by said data source to the DRM module.
In accordance with an embodiment of the present disclosure, the DRM module may be configured to receive data from said data source and process the received data into the at least one representation. The DRM module may be further configured to send the at least one representation and said received data from said one or more data sources to the CM module. The CM module may evaluate at least one correlation between parts of said received data by parsing said received data and the at least one representation.
In accordance with an embodiment of the present disclosure, at least one correlation between different data obtainable from said one or more data sources may be an indication of coherence between two or more members of said different data. Said coherence may reflect one or more of: a degree of equality or inequality; a degree of similarity or dissimilarity; a degree of inclusion dependency or exclusion dependency; and a degree of transitive correlation.
In accordance with an embodiment of the present disclosure, the DRM module may be configured to interact with the CM module to initiate the CM module to perform said determining at least one correlation between said different data obtainable from said one or more data sources. Said interacting with the CM module may include the DRM module sending an evaluation request to the CM module. The evaluation request may include one or more of: one or more of said at least one representation; a correlation type to be used in said determining at least one correlation; an indication that the CM module is to evaluate correlation information for the one or more of said at least one representation; an indication that the CM module is to perform said determining at least one correlation based on the correlation type; an application category identifier indicative of an application category that said at least one representation belong to, or indicative that the CM module is to determine at least one correlation between said at least one representation and the application category, or a combination thereof; a data source identifier identifying one or more members of the one or more data sources which are providing an associated one or more of said at least one representation; and one or more computer memory addresses holding raw data of one or more of said at least one representation. Said interacting with the data consumer component may include generating a query plan indicative of data to be collected and members of the one or more data sources from which said data is to be collected.
In accordance with an embodiment of the present disclosure, configuring the DCP module to perform the set of data collection actions may include selecting, based on the set of data collection actions, one or more of a plurality of DCP module instances, and configuring said selected one or more of the plurality of DCP module instances. The DCP module may be configured to provide results of said set of data collection actions to the data consumer.
In accordance with an embodiment of the present disclosure, the DSD module interacting with the data consumer component may include receiving and processing contents of the request from the data consumer component. The contents of the request from the data consumer component may include parameters indicative of one or more of: an application category identifier indicative of a category that results of the data collection actions are to be used for; an indication of one or more features of data required by the data consumer; a number of correlated datasets required by the data consumer; an indication of dependencies of datasets required by multiple parties and indicating a required correlation between the datasets required by multiple parties; an address of a device of the data consumer which is designated to receive results of the set of data collection actions; and one or more final target data requirements for results of the set of data collection actions to be transmitted to the data consumer. The networked computerized system may be further configured to perform said configuring of the DCP module to perform the set of data collection actions based at least in part on said parameters that may be included in the request from the data consumer component.
In accordance with an embodiment of the present disclosure, the DCP module may include a DCP controller and a plurality of DCP point devices. The DCP point devices may be responsive to configuration instructions by the DCP controller. The configuration instructions may cause the DCP point devices to collectively perform the set of data collection actions. The DCP controller may be deployed in a control plane of a network, and the DCP point devices may be deployed in a user plane or a data plane of the network. One of the DCP point devices may be configured, due to said configuring of the DCP module, to operate as an anchor device operative to provide results of said set of data collection actions to one or more devices of the data consumer. The DCP controller may be configured to perform one or more of: determining or optimizing one or more rules for preprocessing said obtained data; selecting ones of the DCP point devices to perform the set of data collection actions; configuring and activating ones of the DCP point devices; and optimizing resource scheduling in support of performing the set of data collection actions. The DCP controller may be configured to send, to at least one of the DCP point devices, a data collection and preprocessing requirement message, the data collection and preprocessing requirement message causing said at least one of the DCP point devices to perform one or more data collection tasks, data preprocessing tasks, or both, said tasks configured based on contents of the data collection and preprocessing requirement message. Parameters of the data collection and preprocessing and requirement message may include one or more of: an identifier of one of the DCP point devices to be configured and activated; an indication of whether or not data preprocessing is required; a data query statement indicating types of raw data to be collected from specified ones of the set of networked data sources; an address of a device to which said at least one of the DCP point devices is to forward output toward; a requirement on final target data to be transmitted to the data consumer; and a data preprocessing rule indicating how collected raw data is to be preprocessed.
In accordance with an embodiment of the present disclosure, the DCP module may include one or more devices each configured to provide an indication of capabilities thereof to the DSD module, the DSD module performing said configuring the DCP module based in part on said indication of capabilities.
In accordance with an embodiment of the present disclosure, the DSD module interacting with the CM module may include sending a correlation information request to the CM module, the correlation information request specifying one or more types of correlations and the correlation information request being a request for the CM module to identify correlations, of said specified one or more types, between members of said different data obtainable from said set of networked data sources. The correlation information request may include one or more parameters for specifying required correlation information, including one or more of: an application category identifier indicating an application which the required correlation information is to be related to; one of the representations of data sources which the required correlation information is related to; and a correlation type which the required correlation information is related to.
In accordance with an embodiment of the present disclosure, configuring the DCP module to perform the set of data collection actions may include the DSD module providing the DCP module with one or more configuration parameters including one or more of: an identifier of a DCP point device to be configured and activated; an indication of whether preprocessing on said obtained data is to be performed; an indication of raw data to be collect from specified members of the set of networked data sources; an address of a device to which the DCP module is to forward output toward; an indication of a requirement on final target data to be transmitted to the data consumer; an indication of one or more rules to be applied by said preprocessing on said obtained data; and an indication of one or more data correlations between different involved ones of said representation of data sources, said indication being used in said preprocessing on said obtained data. The indication of one or more rules to be applied by said preprocessing on said obtained data may be indicative of one or more of: one or more rules to be used for merging said obtained data; one or more rules to be used for filtering said obtained data; one or more rules to be used for cleaning said obtained data, normalizing said obtained data, or both; one or more indications of portions of said obtained data to which associated ones of the one or more rules are to be applied; and one or more conditions triggering implementation of associated ones of the one or more rules.
In accordance with an embodiment of the present disclosure, there is provided a data representation manager (DRM) networked computerized device configured to: obtain at least one representation of one or more data sources, each representation corresponding to a respective data source of one or more data sources, the representation used to represent a characteristic of said data source; and interact with one or more other devices to support determining of correlations between different data obtainable from said one or more data sources, said correlations being based at least in part on said at least one representation of data sources. In such embodiments, the DRM device may be configured in one or more ways as already described above with respect to the DRM module of the networked computerized system.
In accordance with an embodiment of the present disclosure, there is provided a data collection and preprocessing (DCP) networked computerized device configured to: interact with one or more other devices to configure the DCP device to perform a set of data collection actions; and interact with members of a set of one or more data sources to obtain data therefrom, in accordance with the set of data collection actions. The DCP module may be further configured to perform preprocessing on said obtained data in accordance with the set of data collection actions. In such embodiments, the DCP device may be configured in one or more ways as already described above with respect to the DCP module of the networked computerized system.
In accordance with an embodiment of the present disclosure, there is provided a correlation manager (CM) networked computerized device configured to: determine at least one correlation between different data obtainable from a set of networked data sources, said at least one correlation being based at least in part on at least one obtained representation of data sources belonging to the set of networked data sources; and interact with one or more other devices to determine a set of data collection actions to be performed in support of a request from a data consumer. In such embodiments, the CM device may be configured in one or more ways as already described above with respect to the CM module of the networked computerized system.
In accordance with an embodiment of the present disclosure, there is provided a data source discovery (DSD) networked computerized device configured to: interact with a data consumer component and one or more other devices to determine a set of data collection actions to be performed in support of a request from a data consumer; and configure a further device to perform the set of data collection actions. In such embodiments, the DSD device may be configured in one or more ways as already described above with respect to the DSD module of the networked computerized system.
In accordance with an embodiment of the present disclosure, there is provided a method of data management. The method may include obtaining, by a data management module, at least one representation of one or more data sources, each representation corresponding to a data source of said one or more data sources, the representation used to represent a characteristic of said data source; receiving, by the data management module, a request from a data consumer, the request used to request data; and determining, by the data management module, said data requested by the data consumer according to the request information and the at least one representation. Determining the data requested by the data consumer according to the request information and the at least one representation may include: collecting raw data from the one or more data sources according to the request information and the at least one representation; or collecting raw data from the one or more data sources and preprocessing the raw data collected from the one or more data sources according to the request information and the at least one representation. The method may include: determining one or more rules for preprocessing the raw data; or determining resource scheduling in support of performing the collecting raw data or the preprocessing raw data. The one or more rules may include at least one of: one or more rules to be used for merging the raw data; one or more rules to be used for filtering the raw data; one or more rules to be used for cleaning the raw data; one or more rules to be used for normalizing the raw data; one or more indications of portions of the raw data to which associated ones of the one or more rules are to be applied; and one or more conditions triggering implementation of associated ones of the one or more rules.
In various embodiments, the method may omit one or more actions. For example, a method can be provided which performs actions associated herein with only one of: a DRM module or device, a DCP module or device, a CM module or device, and a DSD module or device. As another example, a method can be provided which performs actions associated herein with two or more of such modules or devices.
In accordance with an embodiment of the present disclosure, said obtaining, by the data management module, at least one representation of one or more data sources may include: requesting a report from said data source; and receiving and processing the report to obtain the at least one representation. Requesting the report may include transmitting representation configuration information to said data source, said representation configuration information indicating a representation of data to be provided by said data source to the data management module.
In accordance with an embodiment of the present disclosure, said obtaining, by the data management module, at least one representation of one or more data sources may include receiving data from said data source and processing the received data into the at least one representation. The data management module may determine at least one correlation between parts of said received data by parsing said received data from said one or more data sources together with the at least one representation.
In accordance with an embodiment of the present disclosure, said obtaining, by the data management module, at least one representation of one or more data sources may include receiving, by the data management module, the at least one representation of one or more data sources.
In accordance with an embodiment of the present disclosure, the method of data management may include determining, by the data management module, at least one correlation between different data obtainable from said one or more data sources, said at least one correlation being based at least in part on said at least one representations of data sources. Said determining, by the data management module, the data requested by the data consumer according to the request and the at least one representation may include determining, by the data management module, the data requested by the data consumer according to the request, the at least one representation and the at least one correlation.
In accordance with an embodiment of the present disclosure, said determining, by the data management module, the data requested by the data consumer according to the request and the at least one representation may include: generating, by the data management module, a query plan indicative of data to be collected and members of the one or more data source from which said data is to be collected; and determining, by the data management module, the data requested by the data consumer according to said query plan.
In accordance with an embodiment of the present disclosure, the method of data management may include providing, by the data management module, the data requested by the data consumer to the data consumer.
In accordance with an embodiment of the present disclosure, the request from the data consumer may include parameters indicative of one or more of: an application category identifier indicative of a category that results of the data collection actions are to be used for; an indication of one or more features of data required by the data consumer; a number of correlated datasets required by the data consumer; an indication of dependencies of datasets required by multiple parties and indicating a required correlation between the datasets required by multiple parties; an address of a device of the data consumer which is designated to receive results of the set of data collection actions; and one or more final target data requirements for results of the set of data collection actions to be transmitted to the data consumer.
Embodiments have been described above in conjunction with aspects of the present invention upon which they can be implemented. Those skilled in the art will appreciate that embodiments may be implemented in conjunction with the aspect with which they are described but may also be implemented with other embodiments of that aspect. When embodiments are mutually exclusive, or are otherwise incompatible with each other, it will be apparent to those skilled in the art. Some embodiments may be described in relation to one aspect, but may also be applicable to other aspects, as will be apparent to those of skill in the art.
Further features and advantages of the present disclosure will become apparent from the following detailed description, taken in combination with the appended drawings, in which:
It will be noted that throughout the appended drawings, like features are identified by like reference numerals.
Embodiments of the present disclosure provide for one, some or all of: representation of (e.g. networked) data sources; configurable data collection and data preprocessing; resources for detecting and recording or tracking correlations between different obtainable data; and resources for interactively determining and implementing data collection actions in support of data consumer requirements. Embodiments may be integrated, as a networked computerized system, into a network such as a 5G or 6G data and/or communication network, so that operations are performed by components of the network itself.
Embodiments of the present disclosure may serve to alleviate potential problems with data analytics implementations. For example, in certain implementations in contrast with embodiments of the present disclosure, a data consumer may be assumed to know exactly what data is required for a specific task. The data consumer would then be expected to send a data request (e.g. via subscription) to the network with accurate indications (e.g. device IDs, SUPI IDs, cell IDs, PDU session IDs, network slice IDs) to direct collection of data from the data source(s). However, it is recognized herein that that data consumers might not know, with high accuracy, what data is required and the identity of relevant data sources for a specific task (e.g. Al training). When such a data consumer requests the data analytics operations system to collect data from data sources, only limited indication information (e.g. fuzzy data features) indicative of the required data might be available. It is challenging, complex and resource-consuming to fully express and address the data consumer's requirements based on such limited information.
Another potential problem with conventional data analytics implementations, which may be addressed by embodiments of the present disclosure, is that there may be a large number of available data sources (to provide data or to be participants) for a task serving the data consumer. If all of this large amount of available data is exposed to the data consumer, it may increase data collection and delivery overhead. Furthermore, even if this large amount of data were provided to the data consumer, it may tend to increase the data consumer's processing overhead. Therefore, in some (but not necessarily all) embodiments of the present disclosure, data collection may be limited to collecting a limited amount of (e.g. most suitable) data, data processing may be performed prior to delivery to the data consumer, or both. Accordingly, a set of data collection actions may be configured, to only retrieve specific data in support of a data consumer request.
In various embodiments, the DAM supports data discovery and configurable data collection operations. This support may involve intelligently and interactively assisting a data consumer to select the suitable data sources and data items, by interacting with the data consumer. This assistance may be provided even when only limited indication information (e.g. fuzzy data features) is provided to the DAM by the data consumer. The data consumer may be a human user or an automated computing device, for example.
In various embodiments, the DAM supports data preprocessing. Preprocessing may be performed on obtained data when necessary, but is not necessarily performed in all scenarios. Preprocessing may be performed for example when the raw data as obtained by the DAM is not directly useable by the data consumer (component), or when the raw data includes contents which are not relevant to the data consumer's requirements (also referred to herein as “useless” or “non-relevant” data), or when the raw data includes redundancy which can be removed. For example, a data consumer may require a composite dataset (which is not available from any single data source). In such a case, the DAM may be configured to create the composite dataset based on (e.g. by merging or stitching together) data from different data sources.
It is also considered that raw data obtained from a data source may contain useless, non-relevant data, redundant data or non-directly useable data. Further, data from two data sources may be partially redundant. Accordingly, in various embodiments, the DAM may be configured to preprocess raw data and then provide results of the preprocessing (or more generally, results of data collection actions) to a data consumer. For example, different structures of datasets may be needed to support different tasks of a data consumer. Accordingly, raw data may be preprocessed into a required data structure.
It is considered that different data consumers (or multiple parties of a single data consumer) are not necessarily independent. The datasets the different consumers require may be correlated (e.g. on ID and feature alignment) and potentially interdependent. Accordingly, in various embodiments the DAM may be configured to select and prepare data for such multiple datasets together rather than select and prepare data for each of the datasets separately. This can assist with meeting a requirement of data correlations among multiple consumers (or multiple parties of a consumer). Thus, a required correlation between different data sets can be provided for.
It is also considered that, given the data needed by different data consumers (or multiple parties of a consumer) may be correlated, the DAM may be configured to maintain e.g. determine, record or track) the correlations between different data. The DAM may be configured to use this correlation information to facilitate choosing suitable data to be provided to each of the multiple data consumers (or multiple parties of a consumer). Moreover, some data may be related to specific services, applications or usages. In such scenarios, the DAM may be configured to maintain (e.g. determine, record or track) correlations between data and services, applications, usages, or combinations thereof. The DAM can then use this information to support selecting suitable data to provide to data consumers.
In various embodiments, to correlate the data from a data source, the DAM may be configured to provide functions which obtain (e.g. capture and maintain) information regarding one or more of the data sources. This information can include information regarding what data can be provided by the data sources (i.e. data which the data sources are capable of providing), for example. There may be a variety of data sources (e.g. NFs, RANs, UEs, network sensors, vertical service providers). Furthermore, such data sources may dynamically register to and leave a network. Therefore, in embodiments of the present disclosure, the DAM is configured to capture, track and maintain information regarding the data sources, so that data discovery, collection and selection can be done by the DAM in an orderly manner based on the data source information.
According to embodiments of the present disclosure, a DAM is configured to provide one or more modules to implement the foregoing functions. One or more of the foregoing functions may be implemented by one or more modules. A correspondence between a function and a module is not limited in this application. As an example, a data representation manager (DRM) module to manage data source information (especially what data the data source can provide), and a correlation manager (CM) module to determine (e.g. capture, detect and maintain) data correlation information. Data correlation information may include information regarding correlations between data, correlations between data and specific service/application/usage, etc. Data correlation information may include information regarding obtainable data (e.g. which has not yet been obtained), where the data correlation information is based for example on the data source information. Based on the data source information and correlation information, the DAM may be configured to discover and select suitable (e.g. most suitable) data to provide to a data consumer. Then the DAM may collect the useful data from data sources, optionally preprocess the data (e.g. via data filtering, data stitching, data cleaning, etc.) and deliver the preprocessed data to the data consumer.
As used herein, the term “module” or “device” may refer to a computerized device, or functional aspect thereof, which may be provided using a networked computing device, whether dedicated or virtualized. A module may be a network function, for example. Modules may operate together with other modules to provide an overall system of networked computerized (e.g. electronic) devices.
Accordingly, embodiments of the present disclosure pertain to a method, system and apparatus to facilitate data discovery and preprocessing, in order to intelligently assist a data consumer to select and prepare suitable data for use.
Current 5G networks dedicated to providing communication service include a network repository function (NRF) to manage what kinds of services can be provided by network functions (NFs). For example, NFs register to NRF, and NRF maintains information regarding kinds of service can be provided by NFs. According to embodiments of the present disclosure, a network providing data (e.g. for AI tasks) may include the DRM module configured to manage what kinds of data can be provided by the data sources (e.g. NFs, UEs, RANs, MECs, database, vertical service providers). For example, Data sources may register to the DRM, and the DRM may maintain information regarding what kinds of data can be provided by data sources. The DRM may be configured to obtain information (e.g. data name, data title, metadata, key word, data attribute, data feature, application category) indicative of the data which can be provided by each of one or more data sources.
According to embodiments of the present disclosure, for example to support providing data for AI tasks, factors such as data attributes and data features may be used as a basis for correlating the data from multiple data sources. According to embodiments, the CM module is configured to manage such data correlations. The correlations may be evaluated by the CM considering various factors (e.g. data attributes) in addition or alternatively to identifiers associated with the data. Such identifiers (IDs) may include SUPI, UE IP, PDU session ID managed by NF UDM and AUSF, etc.
In various embodiments, it is considered that a data consumer does not necessarily know, or is in fact prohibited from knowing, the exact identities of data sources which may provide appropriate data. Accordingly, embodiments of the present disclosure are configured to assist data consumers in discovering and selecting suitable data (for a given task) from potentially large amounts of available data items and from potentially large numbers of data sources. Accordingly, a data source discovery (DSD) module is provided to interactively facilitate discovery of suitable data (and corresponding data collection actions). This discovery may be performed with the joint consideration of data consumers' requirement, data correlation information, data access overhead, etc. The DSD module may be configured to generate data query statements indicating the data to be collected and the data source to collect the data from. That is, the DSD module may configure other devices, such as the Data Collection and Preprocessing (DCP) module, to perform a set of data collection actions, which has been determined by the DSD module to support a request from the data consumer.
It is considered that raw data from data sources may not be directly or immediately usable, and may contain irrelevant (with respect to a given request) or redundant data. In 5G networks dedicated to providing communication service, the session management function (SMF) is used to select user plane function (UPF) instance, and configure communication rules to UPF for communication data transmission. The UPF executes the transmission of communication data based on the communication rules, under the control of SMF. According to embodiments of the present disclosure, for example to support providing data for AI tasks, a module or functionality is provided to select data preprocessing instances, construct data preprocessing rules for one or more of the instances, and configure the data preprocessing rules. A data preprocessing instance may execute data preprocessing tasks, under the control of DSD module. The DSD module may construct the data preprocessing rules (e.g. rules to modify and clean non-directly usable data, filter and delete irrelevant or redundant data) so as to indicate how collected raw data is to be preprocessed into target final data, for example as required by the data consumer.
The DCP module may be configured to execute data pre-processing (e.g., merge, stitch, filter or clean data, or a combination thereof), under the control or direction of the DSD module. Furthermore, the DSD module may select one or more of several instances of the DCP module, and configure these instances of the DCP module to perform operations according to specified data preprocessing rules.
Accordingly, in various embodiments of the present disclosure, the DSD module may assist a data consumer to discover and select suitable data (e.g. correlated datasets) with the joint consideration of the data consumers' requirement and data correlation information. The CM function may evaluate and maintain data correlation information (e.g. to maintain a data correlation library), and deliver the data correlation information to DSD for use. Information on data may be captured by DRM module. The DRM module may deliver the captured information on data to the CM module for correlation evaluation. Besides the ability of data discovery, DSD function may the ability to construct data preprocessing rules indicating how the collected raw data (e.g. non-directly usable, irrelevant or redundant data) is to be preprocessed into the final target data required by the data consumer. The DSD module may configure the data preprocessing rule to DCP which executes data preprocessing.
The DRM module 200 is configured to obtain a representation of data sources belonging to a set 010 of (e.g. networked) data sources, e.g. 010a, 010b, 010c. At least one representation is obtained, and each representation corresponds to a particular data source. The representation of a data source can be used to represent one or more characteristics of the data source. Characteristics can correspond to information indicative of data which can be provided by the data sources, or other information regarding the data or the data source itself. For example, the information can include indications of data collection capabilities.
In various embodiments, the representation of a given data source can include characteristics such as information indicative of the given data source itself (data source parameters), information indicative of data that the given data source is capable of providing (data parameters), or both. Such information indicative of the given data source may include one or more data source parameters, non-limiting examples of which are described below.
The data source parameters may include data source type. The data source type may include one or more of: a UE, a NF, a RAN node, a vertical service provider, a 3rd party, a mobile edge computing (MEC) node, a router, and an edge node.
The data source parameters may include an application category (e.g. application category ID) that data from the given data source is usable for. The application category may indicate a purpose the data from the given data source can be used for, which may be one or more of: federated learning, AI training, federated AI inference, network resource management, UE mobility management, network operation and management, network policy management, network access management, slice management, session management, enhanced mobile broadband (eMBB) service, ultra-reliable low latency communications (URLLC) service, Massive Machine-Type Communications (mMTC) service, vertical service, vehicle service, and sensing service.
The data source parameters may include a location of the given data source, which may include one or more of: a cell or a tracking area the data source is located in, a network the data source belongs to, and the data source's geographical location.
The data source parameters may include a service that can be provided by the given data source, which may include one or more of: a raw data sharing service, and a raw data privacy protection service where the data source can be alone or can cooperate with a network to protect the privacy included in or inferred from the raw data.
The data source parameters may include an ability of the given data source. The ability of the given data source may be one or more of: an energy which can be used to report the data to be collected, a maximum transmit power which can be used to report the data to be collected, a maximum activate time of the data source, a quality of data that the data source can provide, an amount of data that the data source can provide.
The data source parameters may include contextual information of the given data source.
The information indicative of data that the given data source is capable of providing may include one or more data parameters, non-limiting examples of which are described below.
The data parameters may include metadata. Metadata may provide information about the data obtainable from the given data source. Metadata may describe one or more of: the source, size, format, abstract, overview, and other characteristics of the data. Metadata contributes to providing information on the content of a data or dataset.
The data parameters may include a data name. The data name may be one or more of: a string of the data, an index of the data, Hash of the data (e.g. Hash code, root of hash), and may be indicated with a Named Data Networking (NDN) scheme, an information centric networking (ICN) scheme, or other schemes.
The data parameters may include a data title. The data title may be one or more of: a string of the data, an index of the data, Hash of the data (e.g. Hash code, root of hash), and may be indicated with a Named Data Networking (NDN) scheme, an information centric networking (ICN) scheme, or other schemes.
The data parameters may include a data tag. The data tag may be one or more of: a string of the data, an index of the data, Hash of the data (e.g. Hash code, root of hash), and may be indicated with a Named Data Networking (NDN) scheme, an information centric networking (ICN) scheme, or other schemes.
The data parameters may include a key word, which may, for example, be a specific word abstracted from the data.
The data parameters may include data semantics, which may, for example, be a sentence or a paragraph which describes the data.
The data parameters may include a data attribute. For example, in the relational database which organizes data into one or more table of columns and rows, the column of the table may be a data attribute representing values attributed to an object.
The data parameters may include a data feature. The data feature may be abstracted from the data. The data feature may correspond to a feature used to describe the input data of an AI model in AI training or AI inference procedure. The data feature can be obtained from the data via one or more of: embedding, feature engineering, and represented learning method.
The data parameters may include a data schema, which may be a statistical distribution of data and/or a diagram of the data.
The data parameters may include a data format. The data format may be a normalized or standardized data structure of video (e.g. avi, flv), audio (e.g. MPEG-4, MIDI), image (e.g. jpg, png), database, and text.
The data parameters may include an application category that the data can be used for. Application category can also be referred to as service category or usage category. The application category may indicate a purpose the data from the given data source can be used for, which may be one or more of: federated learning, AI training, federated AI inference, network resource management, UE mobility management, network operation and management, network policy management, network access management, slice management, session management, enhanced mobile broadband (eMBB) service, ultra-reliable low latency communications (URLLC) service, Massive Machine-Type Communications (mMTC) service, vertical service, vehicle service, and sensing service.
Thus, the DRM module may act as a repository for information regarding available data sources, the data they can provide, and related details.
The data collection and preprocessing (DCP) module 300 is configured to collect data from data sources and deliver data to the data consumer. Thus, the DCP module interacts with data sources to obtain data therefrom in accordance with data collection actions. The DCP module 300 may further be configured, when required, to execute data preprocessing. The data collection, preprocessing and delivery actions may be configured in response to instructions provided by the DSD module 500. The DCP module 300 may be configured to deliver collected or processed data to the data consumer 030.
For further certainty, as used herein, data collection actions can include actions such as: collecting raw data from data sources; and collecting raw data from data sources and preprocessing the collected raw data. The preprocessing can include tasks such as merging, filtering, cleaning and normalizing raw data. Such tasks may be performed on raw data as collected or on data resulting from other preprocessing actions.
The CM module 400 is configured to determine (e.g. detect and maintain or track) at least one correlation between different data obtainable from the set of data sources. The correlations can be maintained in a correlation library 450, database, or other data structure. The correlations are based at least in part on the representations of data sources as provided by the DRM module 200.
The DSD module 500 is configured to interact with a data consumer component 030 and the CM module 400 to determine the set of data collection actions to be performed in support of a request from the data consumer. The DSD module 500 is further configured to configure the DCP module 300 to perform a specified set of data collection actions. The DSD module 500 may select an appropriate or best set of data sources from the set 010 for use in performing the determined set of data collection actions. Such selection may be made with the joint consideration of the data correlation information, data access overhead, and data consumer's requirement.
The DSD module 500 may be further configured to perform data query statement generation, for example as part of or following determining the set of data collection actions. Such a query statement may be generated to indicate the raw data to be collected and the data sources to collect such raw data from. The DSD module 500 may further trigger a data query request to the DCP module 300.
In various embodiments, the DSD module 500 may further determine data collection and preprocessing rules for implementation by the DCP module 300. This determination may be performed in order to indicate how the final target data is to be obtained from raw data. For example, preprocessing rules may be determined for modifying and cleaning non-directly useable data, filtering irrelevant or redundant data, or a combination thereof.
The DSD module 500 may further be configured to select instances of DCP module(s) or components (e.g. point(s) or instance(s)) thereof to be used to perform data collection and preprocessing. Such selection may be based for example on access overhead, data consumer mobility, communication link states, DCP ability, or the like, or a combination thereof.
Access overhead may relate to the DSD selecting one or more DCP module components located nearest to the data source and/or the data consumer so that the data access overhead (e.g. data collection and/or data delivery overhead) can be potentially reduced.
Data consumer mobility may relate to the DSD selecting one or more DCP module components whose coverage can include areas where the data consumer is typically located (e.g. moveably or stationarily), potentially contributing to the DCP module's or data consumer's switch and handover.
Communication link state may relate to the DSD selecting one or more DCP module components that have established a communication link with the data source or the data consumer, so that the link setup delay and overhead can be potentially reduced. Additionally or alternatively, the DSD module may select one or more DCP module components that have a better (e.g. more reliable, more stable, stronger signal, etc.) communication link with the data source or the data consumer, which may result in higher link transmission speed.
DCP ability may relate to the DSD selecting one or more DCP module components that have the corresponding preprocessing ability to preprocess the raw data. Additionally or alternatively, the DSD may select one or more DCP module components that have sufficient ability (e.g. enough computing resources, lower load, etc.) to execute the one or more data collection and (if needed) preprocessing tasks.
Once selected, the DSD module 500 may configure and activate the DCP module 300 or selected instances or components thereof, in accordance with the determined data collection and preprocessing rules.
The above-described modules may be deployed into different network layers flexibly. For example, the DRM module 200, CM module 400 and DSD module 500, and at least a DCP controller portion of the DCP module 300 may be deployed into but not limited to the control plane (CP) 021. DCP point device portions of the DCP module 300 may be deployed into but not limited to user plane (UP) 022 or data plane. The DRM, CM, DSD and DCP modules can be deployed on a user equipment (UE) side, a network side, or as part of a network function (NF), or with a 3rd party. The network side may include a radio access network (RAN) node or a core network (CN) function, for example.
In various embodiments, a representation of data source can be provided in the form of a Hash code, root of hash, or named with Named Data Networking (NDN) scheme.
Different application categories may be related with different sets of data representations. The DRM module may obtain the corpus of data representations, and may select and maintain the useful data representations for each application category. The mapping between representations and raw data can be reported 243 by the data source 010 to the DRM module 200. The raw data can be indexed by its representation. If the data source cannot provide related data indexed by the representation, the data source may refrain from performing the report 243.
In the second mode 250, if the raw data can be parsed and analyzed by the DRM module 200, the data representations can be extracted by the DRM module using approaches such as representation learning (embedding) or data profiling (e.g. via auto-encoder). The DRM module 200 may accordingly have the ability to read different formats of data (e.g. RDBMS, HDFS files, and CSV files, text, image, sound, audio, video, sending data, wireless signal data, radio wave signal data) from different data sources, and to extract the corresponding data representations from raw data.
According to the second mode 250, the data source 010 transmits 251 raw data to the DRM module. The DRM module 200 then extracts data representations from the raw data according to the data profiling or representation learning 252. The trained representation learning model or data profiling model can be learned by DRM itself or preconfigured to DRM by other parties. Thus, the DRM module can receive raw data and process this received raw data into a representation.
Subsequently to the first or second mode, the DRM module 200 may send a correlation management request, including the data representations it has collected, to the CM module 400 in the form of a correlation management request 260. The message 260 may include a data source ID along with a representation of the data source. The CM module may construct 451 or modify its data correlation library based at least in part on information in the correlation management request 260.
It is further noted that the DRM module 200 may obtain data source representations with the assistance of the DCP module. For example, the DCP module may interact with and collect data from a data source and then send the data to the CM module.
According to various embodiments, after obtaining the data representations (representations of data source), the DAM correlates the data representations. In order to facilitate this, the CM module operates to detect correlations in the data representations and maintain (track) the detected correlations, for example within a data correlation information library (e.g. correlation library 450). More particularly, the CM module may determine (e.g. track) correlations between different data obtainable from data sources, based at least in part on the data representations collected by the DRM module. In various embodiments, the correlation between different data indexed by the data representations can be indicated by or further evaluated with the correlation of data representations.
In various embodiments, the detected and maintained correlations include correlations between different data. Additionally or alternatively, the detected and maintained correlations may include correlations between data and services, applications, usages, or a combination thereof.
In various embodiments, at least one of the correlations between different data includes an indication of coherence between two or more instances of such different data, which may be raw data as indexed by the data representations. The coherence may include, but is not necessarily limited to one or more of: a degree of equality or inequality; a degree of similarity or dissimilarity; a degree of inclusion dependency or exclusion dependency; and a degree of transitive correlation. The degree of equality or inequality may be indicative of an extent to which the different data instances (items) are equal or unequal in value. The degree of similarity or dissimilarity may be indicative of an extent to which attributes of different data instances, such as data features or data schema, are similar or dissimilar. The degree of inclusion or exclusion dependency can indicate a degree or extent to which the data attribute sets of one of the data instances is included within or not included within (e.g. excluded from) the data attribute sets of another one of the data instances. The degree of inclusion or exclusion dependency can indicate a degree to which data attribute sets of two data instances are disjoint or mutually exclusive. The degree of transitive correlation may correspond to a degree (level) of correlation which is transferred from other fields, e.g. sequence-correlated, combination-correlated. For example, two data items may be sequence-correlated because their values both represent fields of time. As another example, two data items may be combination-correlated because they can be combined to be used together for a task such as an AI training task. As another example, transitive correlation may exist between a first and a second dataset if each of them has a correlation relationship with a third dataset. That is, the two datasets can be correlated via the third dataset; the transitive correlation between the first and the second dataset exists via the third dataset.
In various embodiments, correlations between data and services, applications, usages, or a combination thereof can be or arise from indications that the data can be used for a specific service, application or usage.
In various embodiments, the CM module constructs a correlation map, such as a knowledge graph, or an equivalent data structure. This data structure indicates correlations between data sources for example as shown in
As further illustrated in
The CM module may detect (one or more) correlation levels using one or more of a variety of approaches. In some embodiments, correlation levels may be detected via content evaluation, in which the CM module parses provided raw data, data representations, or a combination thereof. The DRM module may be accordingly configured to send one or more data representations and the data received from one or more data sources to the CM module, and the CM module may evaluate the correlation level of the representations by parsing (processing) both the raw data and at least one of the data representations. Correspondingly, the data management module (i.e. the DRM) may determine correlation levels of one or more correlations of one or more data representations by parsing (processing) both the received data and at least one data representation. The correlation level between the data indexed by the data representations can be indicated by or further evaluated with the correlation level of data representations.
In some embodiments, correlation levels may be detected via a semantic-based evaluation, such as a natural language processing (NLP) or word embedding approach. This may be performed for example in instances where the CM module cannot parse data (e.g. raw data) and instead can only evaluate the data correlation based on data representation. In some embodiments, correlation levels may be detected by reading pre-configuration information. For example, the DRM module can maintain a representation library, and the CM module can be pre-configured to set correlation levels between data representations based on contents of the representation library.
For example, the first entry 455a, [(DS1, DS2); (R1, R4), (R3, R6); 0.86, 0.43] implies that there are correlated representations of data source 1 and data source 2, the correlated representation pairs are, respectively, representation 1 and representation 4,representation 3 and representation 6, the correlation levels for these two pairs are, respectively, 0.86 and 0.43, and the correlation type is similarity 456a. A corresponding description 457a is also provided to indicate that the entry is to correlate data between data source 1 and data source 2. Moreover, data sources may dynamically register to and deregister from the system. CM can incrementally introduce and update the correlation library e.g. utilizing inference rules (e.g. based on the learned model) and automatic learning methods.
As another example, the second entry 455b, [(DS1, DS3); (R3, R8); 0.25] implies that there are correlated representations of data source 1 and data source 3, the correlated representation pair is, representation 3 and representation 8, the correlation level for this pair is 0.25, and the correlation type is inclusion dependency 456b. A corresponding description 457b is also provided to indicate that the entry is to correlate data between data source 1 and data source 3. It is noted that the contents 451 reflect the situation as illustrated in
In various embodiments, data sources may dynamically register to and deregister from the system. Accordingly, the CM module may incrementally introduce and update the correlation library. This may be performed for example utilizing inference rules (e.g. based on the learned model) and automatic learning methods.
In various embodiments, the CM module may be configured to reduce higher-dimensional correlation information to lower-dimensional correlation information. This may be done in order to extract more important, efficient or useful information. The CM module may maintain different representation correlations for different application categories. For example,
For example, as shown in
Different maps may provide different levels of flexibility and accuracy for use in a subsequent data discovery procedure. This can facilitate the data discover procedure being faster and/or more efficient.
The representations or data sources which are closely correlated can be embedded into the same group or cluster.
The correlation map may contain only data sources even though their corresponding data representations are an important factor in evaluating the correlations of data sources. The reduced map may be stored to reduce storage space, and its correlation information may be easier and/or faster to retrieve when needed. Reduced mappings may also facilitate faster computation and reduced complexity.
As described above with respect to
In various embodiments, the DSD module is configured to receive a request from a data consumer, and assist the data consumer in determining data sources hosting required data. If necessary, the DSD module may also select, configure and activate one or more appropriate DCP instances, controllers, points, or a combination thereof, to perform data collection, data delivery and, if necessary, data preprocessing. The request from the data consumer may be used to request data. Accordingly, the DSD module may determine the data which is requested by the data consumer. The determination can be made according to information in the request, and at least one data representation. Determining the data can include collecting raw data, or collecting and processing raw data.
In various embodiments, the DSD module operates to perform data query statement generation. The data query statement is generated to indicate types of raw data to be collected from specified data sources. The DSD module may generate a query plan as a result of interacting with the data consumer component. The query plan may indicate the data to be collected and members of (one or more) data sources from which the data is to be collected. The DSD module may determine the involved data sources and data representations in order to satisfy data consumer's requirement. The DSD module may determine the types of raw data to be collected from and the data sources from which the types of raw data are to be collected. Based on such determinations, The DSD module may generate a query statement and trigger a data query request, including the query statement, to the DCP module.
In various embodiments, the DSD module operates to determine data collection and (where required) data preprocessing rules to be implemented. The data preprocessing rules may indicate how the target data to be provided to the data consumer can be obtained from the raw data collected from the data sources. For example, one or more data preprocessing rules can specify how to modify and clean non-directly useable data, and to filter out irrelevant or redundant data.
For example, a data preprocessing rule may include one or more rules for stitching together non-directly usable data. The rule may specify operations to find intermediate data items to be used in stitching (merging) the non-directly usable data together. This may be performed for example when a data consumer requires a composite data set that is not available from any single data source. Thus, data from two, three or more data sources can be merged together to form combined data. Rules for filtering, cleaning and normalizing data can similarly be determined.
In various embodiments, the DSD module operates to select one or more DCP module instances to invoke to perform data collection and (where required) data preprocessing. The selection may be based on one or more of: data access overhead, communication link state between data source sand data consumer, locations of DCP module and data consumer, DCP module ability (e.g. DCP module's supported category ID, DCP module's data transmission or computing load), data consumer's mobility, and DCP module's mobility. The DCP module's ability can be made known to the DSD module via a registration operation or via a notification from a DCP module instance. The selection may be based at least in part on the data collection actions to be performed.
The DSD module may further operate to configure and activate the selected DCP module (e.g. the selected instances thereof) to perform the data collection and data preprocessing actions. This may be done for example via one or more configuration instruction messages.
The DSD module 500 may send a correlation information request 551 (e.g. via a message) to the CM module 400. The correlation information request 551 may specify one or more types of correlations requested by the data consumer component 030 (e.g. via request 031). The correlation information request 551 may include a request for the CM module 400 to identify the specified one or more types of correlations between members of different data obtainable from the (e.g. a set of networked) data sources 030. The correlation information request 551 may include an application category identifier indicating an application which the required correlation information is to be related to. The correlation information request 551 may include one of the representations of data sources which the required correlation information is related to. The correlation information request 551 may include a correlation type which the required correlation information is related to.
The CM module 400 may send a correlation information response 552, which may include some or all of the corresponding data correlation information requested via the correlation information request 551, to the DSD module 500. The CM module 400 may obtain the data correlation information for example from or via the correlation library 450.
The DSD module 500 may select the most suitable data based on at least some of the following: the data correlation information received from the CM module 400 via the correlation information response 552, data access overhead 511, and data requirement of the data consumer component 030 (e.g. as specified via the (e.g. data discovery) request 031).
The DSD module 500 may check 514 whether or not the (raw) data can be used by the data consumer component 030. If yes, then data preprocessing is not needed (516a) and the DSD module 500 may generate a corresponding data query statement 517a. If no, then the raw data may be preprocessed (516b) so that it is usable by the data consumer component 030, in which case the DSD module 500 may generate a (possibly different) corresponding data query statement 517b and request data preprocessing (518) for example via constructing preprocessing rules 520 that may be used by the DCP module 300 to execute data preprocessing. The DSD module 500 may configure the DCP module 300 to perform a sct of data collection actions based at least in part on the (e.g. data discovery) request 031 received from the data consumer component 030.
Based on the set of data collection actions, the DSD module 500 may configure the DCP module 300 to select (560a) suitable (one or more) DCP module instances to perform the set of data collection actions that may include (if needed) data preprocessing. The DSD module 500 may configure (560b) said (one or more) DCP module instances to perform one or more of the set of data collection actions.
The DSD module 500 may send a data collection and (if needed) preprocessing request (message) 518 to the DCP module 300 (e.g. via a DCP controller 310) to activate a DCP module instance to collect and (if needed) preprocess the data. The data collection and (if needed) preprocessing request 518 may include one or more configuration parameters, as described below.
The configuration parameters may include an identifier of a DCP point device (i.e. a DCP module instance) to be configured and activated. The configuration parameters may include an indication of whether preprocessing on obtained data needs be performed. The configuration parameters may include an indication of raw data to be collected from specified members of the set of (e.g. networked) data sources. A data query statement (e.g. 517a or 517b) may be used to indicate what raw data should be collected from which data sources, such as (one or more) specific data representations indicating the representations of the data to be collected, a data source address indicating the address of the data source providing the data. The data query statements may for example have a format of [(representation #1, data source #A); (representation #2, data source #B); . . . ].
The configuration parameters may include an address of a (or each) data consumer component device to which the DCP module is to forward output toward. The configuration parameters may include an indication of a requirement on final target data to be transmitted to the data consumer component, for example a data type (e.g. data format), (optional) number of datasets, and (optional) dependency of datasets (e.g. on ID or feature alignment). The configuration parameters may include an indication of one or more rules (e.g. preprocessing rules 520) to be applied via data preprocessing on the obtained (raw) data.
The preprocessing rules may indicate how the obtained data is to be preprocessed to obtain the final target data. Preprocessing can include (and the associated rules can be indicative of) one or more of: merging the obtained data (e.g. stitching); filtering the obtained data (e.g. removing useless or redundant data); cleaning the obtained data, normalizing the obtained data, or both; specifying one or more indications of portions of the obtained data to which the associated (one or more) preprocessing rules are to be applied; and specifying one or more conditions triggering implementation of the associated (one or more) preprocessing rules.
The preprocessing rules may indicate one or more conditions (e.g. step sequence, timestamp, trigger event, indexing the data) which are to be met before the DCP module (or its one or more devices or instances) may perform data preprocessing. An example of a preprocessing rule can be: [step 1 (or indicated with trigger event, or timestamp): stitch the data from data source A and B; step 2 (or indicated with trigger event, or timestamp): filter the redundant data indexed by representation in the new data obtained in step 1; step 3 (or indicated with trigger event, or timestamp): clean the new data obtained in step 2; . . . ].
The configuration parameters may include an indication of one or more data correlations between different involved ones of said representation of data sources, the indication being used in the preprocessing on the obtained data.
In various embodiments, each of the one or more devices or instances of the DCP module may be configured (e.g. by the DSD module) to provide an indication of capabilities thereof to the DSD module. Such indication of capabilities may be used by the DSD module in configuring the DCP module based at least in part on the indication of capabilities.
In various embodiments, for any one of a variety of reasons (e.g. when the DCP module cannot obtain the target data successfully, or the DCP module rejects the DSD module's request for example due to data communication overload or a computing overload 531a), the DCP module 300 (or one of its devices or instances) may fail to perform data collection and (if needed) preprocessing. In such cases, the DCP module 300 (e.g. via DCP controller 310) may send a failure response (message) 531 to the DSD module 500 indicating the failure. In response to receiving the failure response 531, the DSD module 500 may initiate a repeat 532 of the data collection actions, described previously, to find suitable data and/or another available DCP module instance to perform the (one or more) data collection actions.
For example, as illustrated in
For example, as illustrated in
In an example embodiment, the data consumer component may require target data 525 which includes the Traffic data 015a and the QoS data 016a. The DSD module may configure the DCP module (or one of its devices or instances) to obtain the required target data 525 using the correlations map 520b. The Traffic data 015a can be obtained from the Data Source #2 010h and the QoS data 016a can be obtained from the Data Source #3 010i, which do not have a direct correlation according to the correlations map 520b. However, the known correlations of data source representations, as described above, can be used to combine their corresponding data to obtain the required target data 525.
Continuing with the above example, the required Traffic data 015a can be correlated with the required QoS data 016a using the correlations map 520b as follows: the Traffic data 015a can be combined (e.g. stitched) with the Position data 015b because both are representations of the same Data Source #2 010h; then the Position data 015b can be combined with the Location data 014a of the Data Source #1 010g because they have a relatively high correlation value of 0.95 220g (combining Position data 015b with the Location data 014a is preferred over combining the TA data 015c with the Cell data 014b because the former has a higher correlation level of 0.95 220g compared to the correlation level of the latter of 0.92 220h); then the Location data 014a can be combined with the Timestamp data 014c because both are representations of the same Data Source #1 010g; then the Timestamp data 014c can be combined with the Time data 016b of the Data Source #3 010i because they have a relatively high correlation value of 0.95 220i; and lastly, the Time data 016b can be combined with the QoS data 016a because both are representations of the same Data Source #3 010i.
The combined raw data from all the combinations described above can be then preprocessed by the DCP module (or one of its devices or instances) configured accordingly by the DSD module, for example in a manner described above with reference to
The functions of the DCP module may include at least one of the following: acquiring the data from one or more data sources, (if needed) performing data preprocessing as configured by the DSD module via data preprocessing rules, and delivering the required target data to the data consumer component.
As illustrated in
The DCP points 320 are responsive to configuration instructions by the DCP controller 310. The configuration instructions cause or configure the DCP points 301 to collectively perform the set of data collection actions.
The DCP controller 310 may be deployed in a control plane (CP) 021 and the DCP points 320 may be deployed in a user plane (UP) 022 or a data plane. Several DCP points may operate cooperatively to serve a specific data consumer component. Along with configuring of the DCP module by the DSD module, one of the DCP points 320 is configured to operate as the DCP point anchor 322 to provide results of the set of data collection actions to one or more devices of the data consumer component. Thus, the DCP module is configured to provide results of data collection actions to a data consumer.
As further illustrated in
Based on the data collection and (if needed) preprocessing request 518, the DCP controller 310 can alternatively (to the DSD module itself) configure (e.g. determine and optimize) the preprocessing rules 520. This may occur if the preprocessing rules 520 are not included in the data collection and (if needed) preprocessing request 518 or if the included preprocessing rules 520 need to be optimized to minimize the communication and/or computing overhead, for example. Accordingly, the DCP controller or another DCP device can determine (e.g. optimize) resource scheduling in support of data collection, preprocessing, or both.
In various embodiments, in configuring the DCP points (e.g. 321 in
In various embodiments, in configuring the DCP points (e.g. 321 in
In various embodiments, in configuring the DCP points (e.g. 321 in
In various embodiments, the data collection and preprocessing requirement (message) may cause the at least one of the DCP point devices to perform one or more data collection tasks, data preprocessing tasks, or both. Such tasks may be configured based on contents of the data collection and preprocessing requirement (message).
In various embodiments, the data collection and preprocessing requirement (message) may include one or more parameters described below.
Such parameters may include an identifier of one of the DCP point devices (instances) to be configured and activated.
Such parameters may include an indication of whether or not data preprocessing is required.
Such parameters may include a data query statement indicating types of raw data to be collected from specified ones of the set of (e.g. networked) data sources, and, for example, may have a format of [(representation #1, data source #A); (representation #2, data source #B); . . . ] indicating to collect data indexed by representation 1 from data source #A and to collect data indexed by representation 2 from data source #B and so on.
Such parameters may include an address of a device to which said at least one of the DCP point devices is to forward output toward. Such device may be another DCP point device if the forwarding DCP point device is not a DCP point anchor. Such device may be a data consumer component device if the forwarding DCP point device is a DCP point anchor.
Such parameters may include a requirement on final target data to be transmitted to the data consumer component.
Such parameters may include a data preprocessing rule indicating how the collected raw data is to be preprocessed to obtain the final or required target data. Such data preprocessing rule may include one or all of: one or more specific preprocessing actions (e.g. data stitching, data filtering, data cleaning, etc.); required data representations (e.g. to index the data to be preprocessed); and a (one or more) condition which must be met before performing a given preprocessing action (e.g. step sequence, timestamp, trigger event). As an example, such data preprocessing rule can be [step 1 (or indicated with trigger event, or timestamp): stitch the data from data source A & B; step 2 (or indicated with trigger event, or timestamp): filter the redundant data indexed by representation in the new data obtained in step 1; step 3 (or indicated with trigger event, or timestamp): clean the new data obtained in step 2; . . . ].
In various embodiments, the data source can be also a DCP point device (instance).
An example of the data collection and preprocessing requirement (message) may be: [DCP point #1: collect data indexed by representation 1 from data source A, preprocessing rule 1, timestamp/trigger event/step number 1, data receiver 1 (e.g. another DCP point); . . . ; DCP point anchor: collect data indexed by representation n from data source N, preprocessing rule n, timestamp/trigger event/step number n, data receiver n (i.e. data consumer component)]. In other words, the DCP point #1 needs to collect data indexed by representation 1 from data source A, and DCP point #1 preprocesses the collected data with the preprocessing rule 1 when timestamp/trigger event/step number 1 happens, and then outputs the preprocessed data to another DCP point 1; similar or different instructions for other one or more DCP points . . . ; DCP point anchor needs to collect data indexed by representation n from data source N, and DCP point anchor preprocesses the collected data with the preprocessing rule n when timestamp/trigger event/step number n happens, and then delivers the final target data to receiver n, i.e. the data consumer component.
As further illustrated in
The DCP point and/or the DCP point anchor collects raw data from one or more data sources and (if needed) preprocesses the raw data to the final (required) target data. The DCP anchor then sends the final (required) target data to data consumer component.
According to embodiments as described above, a DAM platform includes a DRM function configured to obtain the representations (e.g. used as index of raw data) of data sources to indicate kinds of data which can be provided by different data sources, a CM function configured to manage the data source correlations, a DSD function configured to select correlated datasets for multiple parties and determine data preprocessing rules, and DCP module configured to execute the data preprocessing of non-directly useable data, irrelevant or redundant data.
Furthermore, according to the above, the DAM platform can accordingly determine correlations between obtainable data, and determine data requested by a data consumer. The determining of data requested by the data consumer can be performed according to the request itself, along with obtained and computed information, such as the representations of data sources and the determined correlations. Determining the data requested by the data consumer can also include generating a query plan and determining the data requested according to execution of the query plan. The determined data, as requested by the data consumer, can be provided by the DAM platform to the data consumer.
In various embodiments, possible features or advantages of the systems and methods described herein include one or more of: obtaining (e.g. capturing and managing) information on data sources and their corresponding data representations; creating and maintaining correlations of representations of data sources and using said representations and correlations to obtain (e.g. discover, collect) raw data; further using said representations and correlations to preprocess the raw data if it is not directly-useable by the data consumer component to obtain the required target data and delivering it to the data consumer component possible contributing to increasing the data usability and reducing the data collection overhead. Possible features or advantages include assisting the data consumer component in discovering and selecting (e.g. the most) suitable datasets in the following situations: situations where the required target data is not clearly defined by the data consumer component; and situations where multiple parties of the data consumer need a number of required target datasets which are correlated.
In some cases, multiple parties of a data consumer component may require a number of target datasets which are correlated. The required target datasets may not always be clearly defined by the data consumer component and, as a result, DAM may receive a limited information regarding the required target datasets.
Conventionally, target datasets may be required by multiple parties of the data consumer component for a Federated Learning (FL). A FL computing platform can be provided for example by a service provider (e.g. NET4AI) which provides (network) connection and AI computing service. There are typically multiple FL participants (i.e. parties) and a collaborator in FL. Each FL participant uses a training dataset to train sub-models. While the training dataset of each FL participant cannot be disclosed to other FL participants or to the FL collaborator, each FL participant can transmit a trained intermediate parameter (e.g. a gradient value) to the FL collaborator. The FL collaborator can then aggregate all intermediate parameters received from multiple FL participants into a one or more aggregated parameter, and then transmit the one or more aggregated parameter to each FL participant. Based on the one or more aggregated parameters received from the FL collaborator, each FL participant can update its local sub-model. The procedure described above can be repeated until the sub-model training ends.
FL can be classified into horizontal federated learning (HFL) and vertical federated learning (VFL). There can be significant constraints on the FL training datasets of the multiple FL participants. For example, in HFL, the HFL participants require target datasets with less data ID overlap and more data feature overlap; while in VFL, the VFL participants require target datasets with more data ID overlap and less data feature overlap.
There may be a number of potential data sources for FL, however systems are typically not able to determine which ones of the potential data sources to federate while achieving the most dataset intersections (e.g. correlations) that would result in optimum AI training performance. Allowing such systems to try all possible combinations of all potential data sources in order to choose the optimum data sources that would provide the required target datasets may be technically impractical and time and resource consuming.
In FL, for the purpose of obtaining the required target data, it may be beneficial to not consider multiple FL participants as independent, since their respective required target data sets may be correlated. The required target datasets are correlated (e.g. on ID and feature alignment in VFL and HFL, respectively) and not independent.
For example,
In contrast, in
In various embodiments of the present disclosure, the DAM may obtain (e.g. find, select, preprocess, deliver) the required target data, taking into consideration that the required target datasets may be preferably correlated and not independent. This may, for example, allow the DAM to meet the target data constraints on data IDs, data features, or a combination thereof.
For example, referring to
Referring to
Subsequently, the DRM module 200 sends a correlation management request 1160 to the CM module 400, to request the CM module to evaluate the correlation of representations. The correlation management request 1160 may be similar or identical to the correlation management request 260 of
The data consumer 030 sends a data discovery request 1132 to the DSD module 500 in order to discover and select suitable data for a certain use. The data discovery request 1132 may be similar or identical to the data discovery request 031 of
The one or more parameters may include an application category identifier (ID) indicative of a category that results of the data collection actions are to be used for.
The one or more parameters may include an indication of one or more features of the target data required by the multiple parties of the data consumer. Such one or more features may constitute an essential data representation (e.g. one or more data features) of the required target data. The target data required by the multiple parties of the data consumer may contain other representations in addition to such essential data representation. However, other representations cannot be known by the data consumer in advance and therefore cannot be provided to the DSD by the data consumer.
The one or more parameters may include a number of correlated datasets required by the data consumer (e.g. corresponding to the number of parties of the data consumer).
The one or more parameters may include an indication of dependencies of the target datasets required by multiple parties and indicating a required correlation between the target datasets required by multiple parties. The dependency may be indicated, for example, with AI model type (e.g. VFL model or HFL model) or a correlation type. For example, for VFL model type, the dependency is that the multiple datasets should have more data ID overlap and less data feature overlap; while for HFL model type, the dependency is that the multiple datasets should have less data ID overlap and more data feature overlap.
The one or more parameters may include an address of each device of the corresponding party of the data consumer which is designated to receive results of the set of data collection actions.
The one or more parameters may include one or more final target data requirements (e.g. data format, data structure) for results of the set of data collection actions to be transmitted to the data consumer.
The one or more parameters may include a required target dataset size indicating the amount of data required by (e.g. a specific party or multiple parties of) the data consumer.
The DSD module 500 may determine the required target data based on at least one or more of the above parameters. In various embodiments, the data consumer may provide a number of data features (maybe not all) of the target datasets needed by multiple parties (i.e. multiple FL participants) to the DSD module. For ease of explanation, the present embodiment considers that one data feature of the target datasets required by the multiple parties is provided to the DSD module. For example, if the data consumer indicates that the dependency of the target datasets is of VFL model type, then the target data selected by the DSD module for the data consumer should consist of multiple datasets, where (ideally) all the datasets have overlapped data IDs, while only one of the data features of the dataset includes the provided data feature and all other features of the datasets do not include the provided data feature. In another example, if the data consumer indicates that the dependency of the target datasets is of HFL model type, then the target data selected by the DSD module for the data consumer should consist of multiple datasets, and (ideally) none of the datasets have overlapped IDs, while all data features of the datasets include the provided data feature.
The DSD module 500 subsequently sends a correlation information request 1153 to the CM module 400. The correlation information request 1153 is to request data correlation information and may include one or more parameters for example as described elsewhere herein. The correlation information request 1153 may be similar or identical to the correlation information request 551 as described for example with respect to
The CM module 400 subsequently sends a correlation information response 1154 to the DSD module 500. The correlation information response 1154 may be similar or identical to the correlation information response 552 as described for example with respect to
The correlation information response 1154 may include (or be followed by) data correlation information as provided by the CM module in response to the correlation information request. Accordingly, the DSD module obtains correlation information from the CM module. Based on the correlation information provided by the CM module (in addition to, for example, a data requirement received, data access overhead, etc.), the DSD module may discover and select most suitable data sources which can provide (at least) the correlated data required by the multiple parties of the data consumer.
The DSD module 500 may determine the suitable data sources and data representations to fulfill the data consumer's request. The DSD module may generate a data query statement and sends the data query statement to the DCP module 300. The query statement may indicate what raw data should be collected from which data sources. For example, the DSD module may select the data sources that can provide datasets with more data ID overlap and less data feature overlap for VFL model type; while the DSD module may select the data sources with less data ID overlap and more data feature overlap for VFL model type.
As illustrated in
As further illustrated in
The parameters of the data collection and preprocessing request 1119 may include an indication information indicating that raw data can be used directly by the (e.g. multiple parties of) the data consumer and preprocessing is not needed.
The parameters of the data collection and preprocessing request 1119 may include a data query statement including data to be collected and the corresponding suitable data sources (i.e. to indicate what raw data should be collect from which data sources), including one or more of the following parameters: a data representation (e.g. data feature, data ID); a data source address, a data size. The format of such data query statement may be, for example: [(representation #1, data source #A, data size #C); (representation #2, data source #B, data size #D); . . . ].
The parameters of the data collection and preprocessing request 1119 may include corresponding addresses of the multiple parties of the data consumer to receive target data.
The parameters of the data collection and preprocessing request 1119 may include mapping between the data sources and addresses of multiple parties of the data consumer indicating that which collected data from a specific data source should be delivered to which party of the data consumer.
Subsequently and referring back to
The DCP module 300 may set up secure tunnels to collect and deliver data from data source and to data consumers. The DCP module collects data from data sources and delivers them to each party of data consumer via the secure tunnels respectively. The DCP module may, in various embodiments, only collect data related to the data representations indicated by the DSD module in the data collection and preprocessing request 1119.
The DCP module 300 may establish and configure secure tunnels to collect 1166a, 1166b and deliver 1171a, 1171b data from the first data source 010a and the second data source 010b to the party A 038a and party B 038b, respectively, of the data consumer 030. The DCP module 300 collects data indexed by the representations received in association with the data collection and preprocessing request 1119 from data sources and (if needed) preprocesses the collected raw data based on the preprocessing rules which may also be received in association with the data collection and preprocessing request 1119 or the preprocessing rules that may be constructed by the DCP module 300 itself, as described elsewhere herein.
Subsequently, the DCP module delivers 1171a, 1171b data from the first data source 010a and the second data source 010b to the party A 038a and party B 038b, respectively, of the data consumer 030 via the secure tunnels.
In various embodiments of the present disclosure, the above-described embodiment can be used to help multiple parties of a data consumer to discover and select suitable data when the multiple parties require correlated datasets, based on the maintained knowledge (i.e. data source representation and correlation) of the data sources, and limited indication information (e.g. a few of data feature, data dependency type) received from the data consumer.
Referring to
Subsequently, the DRM module 200 sends a Correlation management request 1160 to the CM module 400, to request the CM module to evaluate the correlation of representations. The correlation management request 1160 may be similar or identical to the correlation management request 260 of
The data consumer 030 sends a data discovery request 1131 to the DSD module 500 in order to discover and select suitable data for a certain use. The data discovery request 1131 may be similar or identical to the data discovery request 031 of
The data discovery request 1131 may include one or more parameters for example as described elsewhere with respect to
As an example, suppose party A 038a is Participant A 931 in the horizontal federated learning (HFL) scheme of
The DSD module 500 is configured to determine the final target data based at least in part on the provided application ID and essential data representation parameters in the data discovery request 1131.
The DSD module 500 subsequently sends a correlation information request 1151 to the CM module 400. The correlation information request 1151 is to request data correlation information and may include one or more parameters for example as described elsewhere herein. The correlation information request 1151 may be similar or identical to the correlation information request 551 as described for example with respect to
The CM module 400 subsequently sends a correlation information response 1152 to the DSD module 500. The correlation information response 1152 may be similar or identical to the correlation information response 552 as described for example with respect to
Based on information such as the correlation information, data requirements received in association with the data discovery request 1131, data access overhead, etc., the CM module 400 discovers and selects the most suitable data sources which can provide the data required by the data consumer. The DSD module determines the involved data sources and data representations to be used to fulfill the data consumer's request. The DSD module then generates a data query statement and subsequently sends the data query statement to the DCP module. The query statement indicates the raw data to be collect and the data sources from which this raw data is to be collected. For example, referring to
Referring back to
The data collection and preprocessing request 1118 may include one or more parameters for example as described with respect to
As an example with respect to
As an example, parameters of a data preprocessing rule, as indicated in the data collection and preprocessing request 1118, can be indicative that data is to be preprocessed in a certain manner. This manner can indicate preprocessing operations, representations (to index the data which is to be preprocessed) and conditions (e.g. step sequence, timestamp, trigger event) to execute preprocessing operation. This may indicate that the data preprocessing operation are to be executed by the DCP module when the specified conditions are met.
As an example, with respect to
As another example, with respect to
Subsequently and referring to
The DCP module 300 may establish and configure secure tunnels to collect 1165a, 1165b and deliver 1170 data from data sources 010a, 010b to the party A 038a of the data consumer 030. The DCP module 300 collects data indexed by the representations received in association with the data collection and preprocessing request 1118 from data sources and preprocesses the collected raw based on the preprocessing rules also received in association with the data collection and preprocessing request 1118 or the preprocessing rules constructed by the DCP module 300 itself.
Subsequently, the DCP module delivers 1170 the final target data to party A 038 of the data consumer 030 via the secure tunnels.
Accordingly, although the data provided by the data sources may not necessarily be directly useable by a data consumer, embodiments of the present disclosure may preprocess the data so that it becomes usable. The DAM determines the data sources to collect data from and the data representations to provide. The DAM (e.g. interactively or autonomously) assists the data consumer in generating a data query statement including the data to be collected and related data sources, to indicate the raw data to be collect and the data sources to collect the raw data from. Moreover, the DAM may determine and implement data preprocessing rules to transform the non-directly usable data into usable data. This approach may reduce the data consumer's data processing overhead, and may facilitate better utilization of raw data to improve data potential. The proposed DAM platform may be used to provide a facility or business model for companies or networks selling datasets. The cost of provided datasets may be levied on a per-access (only the useful data items is exposed and accessed) instead of selling an entire dataset.
As shown, the device 1200 includes a processor 1210, such as a Central Processing Unit (CPU) or specialized processors such as a Graphics Processing Unit (GPU) or other such processor unit, memory 1220, non-transitory mass storage 1230, I/O interface 1240, network interface 1250, and a transceiver 1260, all of which are communicatively coupled via bi-directional bus 1270. According to certain embodiments, any or all of the depicted elements may be utilized, or only a subset of the elements. Further, the device 1200 may contain multiple instances of certain elements, such as multiple processors, memories, or transceivers. Also, elements of the hardware device may be directly coupled to other elements without the bi-directional bus.
The memory 1220 may include any type of non-transitory memory such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), read-only memory (ROM), any combination of such, or the like. The mass storage element 1230 may include any type of non-transitory storage device, such as a solid state drive, hard disk drive, a magnetic disk drive, an optical disk drive, USB drive, or any computer program product configured to store data and machine executable program code. According to certain embodiments, the memory 1220 or mass storage 1230 may have recorded thereon statements and instructions executable by the processor 1210 for performing any of the aforementioned method steps described above.
An electronic device configured in accordance with the present disclosure may comprise hardware, software, firmware, or a combination thereof. Examples of hardware arc computer processors, signal processors, ASICs, FPGAs, silicon photonic chips, etc. The hardware can be electronic hardware, photonic hardware, or a combination thereof. The electronic device can be considered a computer in the sense that it performs operations that correspond to computations, e.g. receiving and processing data, receiving and processing instructions, generating and storing data, providing outputs such as instructions, queries, or reports, or the like, or a combination thereof. The electronic device can thus be provided using a variety of technologies as would be readily understood by a worker skilled in the art. The electronics device can include a computer operatively coupled to memory, such as non-transitory electronic memory. The memory may hold computer program instructions which, when executed, cause the computer to perform operations as described herein.
It will be appreciated that, although specific embodiments of the technology have been described herein for purposes of illustration, various modifications may be made without departing from the scope of the technology. The specification and drawings are, accordingly, to be regarded simply as an illustration of the disclosure as defined by the appended claims, and are contemplated to cover any and all modifications, variations, combinations or equivalents that fall within the scope of the present disclosure. In particular, it is within the scope of the technology to provide a computer program product or program element, or a program storage or memory device such as a magnetic or optical wire, tape or disc, or the like, for storing signals readable by a machine, for controlling the operation of a computer according to the method of the technology and/or to structure some or all of its components in accordance with the system of the technology.
Acts associated with the method described herein can be implemented as coded instructions in a computer program product. In other words, the computer program product is a computer-readable medium upon which software code is recorded to execute the method when the computer program product is loaded into memory and executed on the microprocessor of the wireless communication device. The computer-readable medium may be non-transitory in the sense that the information is not contained in transitory, propagating signals.
Acts associated with the method described herein can be implemented as coded instructions in plural computer program products. For example, a first portion of the method may be performed using one computing device, and a second portion of the method may be performed using another computing device, server, or the like. In this case, each computer program product is a computer-readable medium upon which software code is recorded to execute appropriate portions of the method when a computer program product is loaded into memory and executed on the microprocessor of a computing device.
Further, each step of the method may be executed on any computing device, such as a personal computer, server, or the like and pursuant to one or more, or a part of one or more, program elements, modules or objects generated from any programming language, such as C++, Java, or the like. In addition, each step, or a file or object or the like implementing each said step, may be executed by special purpose hardware or a circuit module designed for that purpose.
Through the descriptions of the preceding embodiments, the present disclosure may be implemented by using hardware only or by using software and a necessary universal hardware platform. Based on such understandings, the technical solution of the present disclosure may be embodied in the form of a software product.
It will be readily understood that, throughout the preceding discussion, the above-described network functionalities and operations may correspond to a method for use in supporting operation of a communication network, such as a 5G or 6G wireless communication network. The method may involve computer-implemented functions, namely functions which are implemented by one or more computing, communication or memory components of the network infrastructure, or a combination thereof. These components may take various forms, such as specific servers or general-purpose computing, communication or memory devices, or combinations thereof, which are configured to provide the required functionality through virtualization technologies. The method may involve the operation of one or more network components in order to improve the operation of the network. As such, with the communication network viewed as an apparatus, embodiments of the present disclosure may be directed to improving internal operations of the communication network.
Further, it will be readily understood that embodiments of the present disclosure relate to a communication network system or associated apparatus thereof, which is configured to perform the above-described network functionalities and operations. Again, the system or apparatus may comprise one or more computing, communication or memory components of the network infrastructure, or combinations thereof, which may take various forms, such as specific servers or general-purpose computing, communication or memory devices, or combinations thereof, which are configured to provide the required functionality through virtualization technologies. Various methods as disclosed herein may be implemented on one or more real or virtual computing devices, such as devices within a communication network control plane, devices operating in the data plane, or a combination thereof. Computing devices used to implement method operations may include a processor operatively coupled to memory, the memory providing instructions for execution by the processor to perform the method as described herein.
Various embodiments of the present disclosure utilize one or both of: real computer resources; and virtual computer resources. Such computer resources utilize, at a hardware level, a set of one or more microprocessors operatively coupled to a corresponding set of memory components which include stored program instructions for execution by the microprocessors. Computing resources may be used to provide virtual computing resources at one or more levels of virtualization. For example, one or more given generic computer hardware platforms may be used to provide one or more virtual computing machines. Computer hardware, such as processor resources, memory, and the like, may also be virtualized in order to provide resources from which further virtual computing machines are built. A set of computing resources which are allocatable for providing various computing resources which in turn are used to realize various computing components of a system, may be regarded as providing a distributed computing system, the internal architecture of which may be configured in various ways.
In the above, it should be noted that functions and modules may be given different names and instantiated in different ways. A given function may be merged or integrated with one or more other functions. A given function may be provided by cooperation of multiple separate functional elements.
Although the present disclosure has been described with reference to specific features and embodiments thereof, it is evident that various modifications and combinations can be made thereto without departing from the disclosure. The specification and drawings are, accordingly, to be regarded simply as an illustration of the disclosure as defined by the appended claims, and are contemplated to cover any and all modifications, variations, combinations or equivalents that fall within the scope of the present disclosure.
This application is a continuation of International Patent Application No. PCT/CN2022/093415, filed May 18, 2022, entitled “Method, Apparatus and System for Configurable Data Collection for Networked Data Analytics and Management” the contents of which are incorporated herein by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/CN2022/093415 | May 2022 | WO |
| Child | 18886327 | US |
