I. Field
The following description relates generally to an infrastructure for providing information as a service, and more particularly to systems and methods for publishing a synthesis of data to facilitate providing information as a service.
II. Background
By way of background concerning some conventional systems, computing devices have traditionally stored information and associated applications and data services locally to the device. Yet, with the evolution of on-line and cloud services, information is increasingly being moved to network providers who perform none, some or all of the services on behalf of devices. The evolution of network storage farms capable of storing terabytes of data (with potential for petabytes, exabytes, etc. of data in the future) has created an opportunity to mimic the local scenario in a cloud, with separation of the primary device and the external storage.
However, no cloud service or network storage provider has been able to effectively provide information as a service on any platform, with publishers, developers, and consumers easily publishing, specializing applications for and consuming any kind of data, in a way that can be tracked and audited for all involved. This lack of an effective tracking mechanism makes it difficult to valuate information over time since the consumption of particular information may vary and is often unpredictable. Indeed, the valuation of a particular type of data may vary according to consumption, wherein particular subsets of such data may be consumed more often, and thus be more valuable, than others. For instance, with respect to customer satisfaction surveys, responses provided by some customers are inevitably more valuable than others since they might be more thorough, for example, or from a particularly important demographic. Although companies sometimes provide compensation for participating in these surveys, such compensation is often nominal and uniform across all survey participants. Participants thus have little incentive to provide particularly thorough responses and/or from even participating in such surveys at all.
It should be further noted that data is often more valuable in the aggregate. For instance, with respect to the aforementioned customer surveys, the value of a particular survey will generally increase as more customers participate. The actual value of an individual response may thus vary depending on the ultimate comprehensiveness of the survey, as well as an eventual usage of the survey. Conventional systems, however, do not provide an adequate infrastructure for valuating individual contributions to an aggregated dataset. Indeed, unless data is particularly valuable by itself as a single data consuming experience (e.g., data provided via Westlaw®, LexisNexis®, Microsoft Virtual Earth®, the OpenGIS® Web Map Service Interface Standard (WMS), etc.), it is difficult to monetize or otherwise build on the experience beyond the four corners of that valuable data set.
The above-described deficiencies of current methods are merely intended to provide an overview of some of the problems of conventional systems, and are not intended to be exhaustive. Other problems with the state of the art and corresponding benefits of some of the various non-limiting embodiments may become further apparent upon review of the following detailed description.
A simplified summary is provided herein to help enable a basic or general understanding of various aspects of exemplary, non-limiting embodiments that follow in the more detailed description and the accompanying drawings. This summary is not intended, however, as an extensive or exhaustive overview. Instead, the sole purpose of this summary is to present some concepts related to some exemplary non-limiting embodiments in a simplified form as a prelude to the more detailed description of the various embodiments that follow.
In accordance with one or more embodiments and corresponding disclosure thereof, various aspects are described in connection with providing information as a service from any platform. In one such aspect, an apparatus configured to synthesize data to facilitate providing information as a service is disclosed. Within such embodiment, the apparatus includes a processor configured to execute computer executable components stored in memory. The computer executable components include an aggregation component, a combining component, a tracking component, and a valuation component. The aggregation component is configured to aggregate a plurality of data contributions, whereas the combining component is configured to combine a first data contribution with a second data contribution to create a data combination. For this embodiment, the tracking component is configured to track a consumption of the data combination. The valuation component is then configured to ascertain a contribution value associated with at least one contributor to the data combination based on the consumption.
Other embodiments and various non-limiting examples, scenarios and implementations are described in more detail below.
Various embodiments are now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more embodiments. It may be evident, however, that such embodiment(s) may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing one or more embodiments.
The subject specification discloses a system and method for publishing synthesized data to facilitate providing information as a service. As used herein, the term “synthesized data” refers to combined data ascertained via any of a plurality of methods including a merging of two or more values into a single merged value and/or a joining of two or more datasets into a single dataset. To this end, it is also contemplated that data combinations may be combined with another data combination and/or data contribution to create a separate data composition (i.e. chaining data combinations/contributions together).
In an aspect, a platform is provided in which content is published and monetized according to a web services application programming interface (API) model that allows tracking and auditing of information consumption transactions. Within such embodiment, even if the content is published for free, due to the auditing and tracking mechanisms described herein, an advertising and revenue engine can be layered on top of content that is actually consumed to ensure that advertising revenues are paid to content publishers in proportion to actual consumption rather than any click-through or other imperfect model. In an aspect, the consumption of a particular content contribution may include the consumption of a data combination that includes the contribution and/or is derived from the contribution. Accordingly, an ecosystem is built in which information providers, including rare publishers, can combine their data with data provided by other information providers and derive revenue based on their respective contributions to the data combination.
Referring next to
For some applications, it is contemplated that data synthesizer unit 122 may be configured to merge values received from various disparate sources. In
In another aspect, rather than merging values, individual datasets may be joined to form a larger dataset. In
Referring next to
In one aspect, processor component 410 is configured to execute computer-readable instructions related to performing any of a plurality of functions. Processor component 410 can be a single processor or a plurality of processors dedicated to analyzing information to be communicated from data synthesizer unit 400 and/or generating information that can be utilized by memory component 420, aggregation component 430, combining component 440, tracking component 450, and/or valuation component 460. Additionally or alternatively, processor component 410 may be configured to control one or more components of data synthesizer unit 400.
In another aspect, memory component 420 is coupled to processor component 410 and configured to store computer-readable instructions executed by processor component 410. Memory component 420 may also be configured to store any of a plurality of other types of data including data generated by any of aggregation component 430, combining component 440, tracking component 450, and/or valuation component 460. Memory component 420 can be configured in a number of different configurations, including as random access memory, battery-backed memory, hard disk, magnetic tape, etc. Various features can also be implemented upon memory component 420, such as compression and automatic back up (e.g., use of a Redundant Array of Independent Drives configuration).
In yet another aspect, aggregation component 430 is also coupled to processor component 410 and configured to interface data synthesizer unit 400 with information providers. For instance, aggregation component 430 may be configured to aggregate data contributions from any of a plurality of disparate information providers. Here, it should be noted that such data contributions can include qualitative data (e.g., narratives corresponding to a movie review, responses to a survey, etc.) and/or quantitative data (e.g., precipitation measurements, home value estimates, etc.). In an aspect, aggregation component 430 may be further configured to aggregate data contributions based on a search criteria. For instance, based on the search criteria, aggregation component 430 may be configured to aggregate a subset of the already aggregated data contributions (i.e., aggregate internally stored data contributions). Alternatively, aggregation component 430 may be configured to perform the original aggregation based on the search criteria (i.e., aggregate externally stored data contributions).
As shown, data synthesizer unit 400 may also include combining component 440. Within such embodiment, combining component 440 is configured to combine a first data contribution with a second data contribution to create a data combination. In an aspect, the data combination created by combining component 440 can be either a joined dataset or a merged value. For instance, with respect to joining datasets, the first and second data contributions may correspond to qualitative responses to a survey from a first and second information provider, respectively. Here, the data combination may simply include both of the qualitative responses in their entirety. With respect to merged values, however, combining component 440 may be configured to merge a first value associated with the first data contribution with a second value associated with the second data contribution to create the merged value (e.g., an average of two temperature readings from the same neighborhood at the same time). When computing merged values, combining component 440 can be further configured to determine a confidence level associated with the merged value. For instance, combining component 440 can be configured to assign a weight to at least one of the first data contribution or the second data contribution, wherein the confidence level is based on the weight (e.g., weighting the data contributions based on a reliability of their respective sources).
Data synthesizer unit 400 may also include tracking component 450 and valuation component 460, as shown. In an aspect, tracking component 450 is configured to track a consumption of the data combination created by combination component 440, whereas valuation component 460 is configured to ascertain a unique contribution value for each contributor to the data combination based on the consumption. Here, since some information providers may be more valuable than others (e.g., because they are more reliable, more popular, etc.), valuation component 460 may be further configured to assign a particular reputation value to each contributor, wherein the contribution value may vary based on the reputation value.
In a further aspect, it should be appreciated that data synthesizer unit 400 may be configured to apportion revenue generated by providing information as a service (e.g., advertising revenue, subscription revenue, etc.). To facilitate apportioning such revenue, tracking component 450 may be configured to monitor any of a plurality of revenue streams associated with a consumption of information. Moreover, tracking component 450 can be configured to determine an allocation of the revenue stream earned by each contributor of consumed information based on their respective contribution values. In an aspect, data synthesizer unity 400 may provide a centralized advertising platform, wherein advertising revenues are automatically tracked and apportioned. For instance, combining component 430 may be configured to insert an advertisement into a display of a particular data combination, wherein a revenue stream of the data combination includes an advertising portion associated with the inserted advertisement. Here, however, it should be noted that the advertisement might not necessarily be inserted into the display of the data. To this end, it should be further noted that such advertisement may affect the reputation value of one or more contributor, and that the advertisement may be combined with a data contribution to create a data combination.
Turning to
Referring next to
As illustrated, the method begins by establishing a communication link with information providers and information consumers at act 600. Next, at act 610, data contributions from various information providers are received. Upon receiving the data contributions, particular data combinations of the received contributions can then be inferred at act 620. Here, it should be appreciated that some types of quantitative data may be automatically merged (e.g., two people providing a recommendation rating for the same movie). Similarly, qualitative datasets that are logically related may be automatically joined (e.g., two people providing comment narratives for the same movie).
At act 630, the method then proceeds with an information request being received from an information consumer. Here, because any of a wide variety of information may be accessible, it is contemplated that such information request is specifically targeted (e.g., a search string that includes the name of a particular movie). Next, at act 640, the requested information is provided to the information consumer (e.g., recommendation ratings and/or comment narratives for a requested movie). A usage report is then generated at act 650 identifying the information providers who contributed to the requested information (e.g., the people who provided recommendation ratings and/or comment narratives for a requested movie).
To facilitate a better understanding of the numerous potential implementations of the aspects disclosed herein, the following discussion describes various non-limiting embodiments within the context of exemplary implementation scenarios. Referring first to
To overcome this lack of granularity, data can be synthesized from information providers within a selectable geographic region. For instance,
Referring next to
As stated previously, in addition to merging values, it is contemplated that individual datasets may be joined to form a larger dataset. Referring next to FIGS. 9-10, an exemplary scenario is provided in which qualitative datasets are joined within the context of gathering political data. For this particular scenario, it should be noted that political campaigns often invest a significant amount of time and energy researching the popularity of various issues. Indeed, knowing the political pulse of particular demographics is often critical to having a successful campaign. To ascertain this data, surveys which probe the public's views on various issues are utilized. However, participants of such surveys are often offered little or no compensation, which discourages many people from participating at all.
In order to provide more incentive to participate in such surveys, the aspects described herein can be implemented to identify particularly useful survey responses so that providers of those responses can be compensated accordingly. In
Here, it should be noted that survey responses from responders matching particular demographics may be more valuable than others. For example, survey responses submitted by responders in “swing” states may generally be deemed more valuable than responses from non-swing states. If so, such distinction in value can be readily quantified by monitoring the actual consumption of these responses.
In an aspect, the tracking aspects described herein can then be utilized in conjunction with this valuation to apportion revenues to survey responders based on consumption. For instance, as illustrated in
As stated previously, it may sometimes be desirable to compensate information providers according to their respective reputations. Referring next to
In an aspect, reputation values are integrated into merged value calculations by weighting data contributions accordingly. In
In another aspect, qualitative reviews from individual critics may be joined to form a larger dataset. In
As shown in the flow diagram of
In this regard, some key parties in the infrastructure include data owners, the application developers/ISVs and the consumers/information workers. In general, data owners are entities who want to charge for data, or who want to provide data for free for other reasons, or enforce other conditions over the data. In turn, application developers/ISVs are entities who want to monetize their application (e.g., through advertising, direct payments, indirect payments, etc.), or provide their application for free for some beneficial reason to such entities. Information workers and consumers are those who can use the raw data, or those who want to use an application provided by the application developers.
In this regard, various embodiments for the user friendly data platform for enabling information as a service from any platform is an infrastructure to enable consumers of data (IWs, developers, ISVs) and consumers of data to transact in a simple, cost effective and convenient manner. The infrastructure democratizes premium (private) and community (public) data in an affordable way to allow IWs to draw insights rapidly, and allows developers to build innovative apps using multiple sources of data in a creative manner and enables developers to monetize their efforts on any platform. For instance, the infrastructure supports Pay Per Use as well as Subscription Pricing for Content, Pay for Content (“retail price”—set by content owner), Pay Data Fee (“Shipping and Handling”) and BW, and further supports Data fees as a brokerage fee on a per-logical transaction basis (per report, per API, per download, etc.).
For Information Workers (e.g., Office, SQL Server, Dynamics users), the infrastructure supports subscriptions to allow for future EA integration as well as predictable spend requirements (as well as caching to support on and off-premise BI as well as “HPC” workloads). Thus, alternatives include content priced per-user per-month; which may or may not bundle to deliver content packs or per-transaction pricing, e.g., allowing cloud reporting/business intelligence on-demand pricing to eliminate the need to move large amounts of data while allowing per-usage pricing, or vertical apps via report galleries.
For content providers (any data type; any cloud), using any platform, the infrastructure becomes a value proposition to incent sales within any particular desired platform; auto-scaling, higher level SLA possibilities at no additional cost. For some non-limiting examples, data can be secure and associated data in the following domains: Location aware services & data, Commercial and residential real estate, Financial data and services, etc. A non-limiting scenario may include delivery of data to top 30 non-governmental organization (NGO) datasets. In addition, the infrastructure may include the ability to showcase BI & visualization through “Bing for information as a service”, HPC, etc. Vertical application opportunities exist as well.
In one non-limiting embodiment, the data brokerage can be analogized to conventional brick and mortar strategies: For instance, capacity can be represented as shelf space (e.g., a mix of structured and unstructured/blob data), cost of goods (COGS) can be represented as square footage, (SA, platform dependency, bandwidth) and content can be represented as merchandise (e.g., optimize content providers to cover COGS, maximize profits from IWs and developers). In various embodiments, an onboarding process can be implemented with quality bars for data and services, as well as accommodation of service level agreements (SLAs).
As supplemental services to the data, billing and discovery services 1570 can include online billing 1572 (e.g., MOCP) or discovery services 1574 (e.g., pinpoint) and authentication services 1580 can include credentials management 1582 (e.g., Live ID) or content authentication 1584, e.g., authenticated content services (ACS). Accounts services 1590 can include logging/audit services 1586 or account management 1588. Management and operations services 1592 can include an operations dashboard service 1594 and network operations service 1596, e.g., Gomez.
One of ordinary skill in the art can appreciate that the various embodiments of methods and devices for an infrastructure for information as a service from any platform and related embodiments described herein can be implemented in connection with any computer or other client or server device, which can be deployed as part of a computer network or in a distributed computing environment, and can be connected to any kind of data store. In this regard, the various embodiments described herein can be implemented in any computer system or environment having any number of memory or storage units, and any number of applications and processes occurring across any number of storage units. This includes, but is not limited to, an environment with server computers and client computers deployed in a network environment or a distributed computing environment, having remote or local storage.
Each object 1710, 1712, etc. and computing objects or devices 1720, 1722, 1724, 1726, 1728, etc. can communicate with one or more other objects 1710, 1712, etc. and computing objects or devices 1720, 1722, 1724, 1726, 1728, etc. by way of the communications network 1740, either directly or indirectly. Even though illustrated as a single element in
There are a variety of systems, components, and network configurations that support distributed computing environments. For example, computing systems can be connected together by wired or wireless systems, by local networks or widely distributed networks. Currently, many networks are coupled to the Internet, which provides an infrastructure for widely distributed computing and encompasses many different networks, though any network infrastructure can be used for exemplary communications made incident to the techniques as described in various embodiments.
Thus, a host of network topologies and network infrastructures, such as client/server, peer-to-peer, or hybrid architectures, can be utilized. In a client/server architecture, particularly a networked system, a client is usually a computer that accesses shared network resources provided by another computer, e.g., a server. In the illustration of
A server is typically a remote computer system accessible over a remote or local network, such as the Internet or wireless network infrastructures. The client process may be active in a first computer system, and the server process may be active in a second computer system, communicating with one another over a communications medium, thus providing distributed functionality and allowing multiple clients to take advantage of the information-gathering capabilities of the server. Any software objects utilized pursuant to the user profiling can be provided standalone, or distributed across multiple computing devices or objects.
In a network environment in which the communications network/bus 1740 is the Internet, for example, the servers 1710, 1712, etc. can be Web servers with which the clients 1720, 1722, 1724, 1726, 1728, etc. communicate via any of a number of known protocols, such as HTTP. Servers 1710, 1712, etc. may also serve as clients 1720, 1722, 1724, 1726, 1728, etc., as may be characteristic of a distributed computing environment.
As mentioned, various embodiments described herein apply to any device wherein it may be desirable to implement one or pieces of an infrastructure for information as a service from any platform. It should be understood, therefore, that handheld, portable and other computing devices and computing objects of all kinds are contemplated for use in connection with the various embodiments described herein, i.e., anywhere that a device may provide some functionality in connection with an infrastructure for information as a service from any platform. Accordingly, the below general purpose remote computer described below in
Although not required, any of the embodiments can partly be implemented via an operating system, for use by a developer of services for a device or object, and/or included within application software that operates in connection with the operable component(s). Software may be described in the general context of computer executable instructions, such as program modules, being executed by one or more computers, such as client workstations, servers or other devices. Those skilled in the art will appreciate that network interactions may be practiced with a variety of computer system configurations and protocols.
With reference to
Computer 1810 typically includes a variety of computer readable media and can be any available media that can be accessed by computer 1810. The system memory 1830 may include computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM) and/or random access memory (RAM). By way of example, and not limitation, memory 1830 may also include an operating system, application programs, other program modules, and program data.
A user may enter commands and information into the computer 1810 through input devices 1840 A monitor or other type of display device is also connected to the system bus 1821 via an interface, such as output interface 1850. In addition to a monitor, computers may also include other peripheral output devices such as speakers and a printer, which may be connected through output interface 1850.
The computer 1810 may operate in a networked or distributed environment using logical connections to one or more other remote computers, such as remote computer 1870. The remote computer 1870 may be a personal computer, a server, a router, a network PC, a peer device or other common network node, or any other remote media consumption or transmission device, and may include any or all of the elements described above relative to the computer 1810. The logical connections depicted in
As mentioned above, while exemplary embodiments have been described in connection with various computing devices, networks and advertising architectures, the underlying concepts may be applied to any network system and any computing device or system in which it is desirable to publish, build applications for or consume data in connection with interactions with a cloud or network service.
There are multiple ways of implementing one or more of the embodiments described herein, e.g., an appropriate API, tool kit, driver code, operating system, control, standalone or downloadable software object, etc. which enables applications and services to use the infrastructure for information as a service from any platform. Embodiments may be contemplated from the standpoint of an API (or other software object), as well as from a software or hardware object that facilitates provision of an infrastructure for information as a service from any platform in accordance with one or more of the described embodiments. Various implementations and embodiments described herein may have aspects that are wholly in hardware, partly in hardware and partly in software, as well as in software.
The word “exemplary” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, for the avoidance of doubt, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.
As mentioned, the various techniques described herein may be implemented in connection with hardware or software or, where appropriate, with a combination of both. As used herein, the terms “component,” “system” and the like are likewise intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on computer and the computer can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers.
The aforementioned systems have been described with respect to interaction between several components. It can be appreciated that such systems and components can include those components or specified sub-components, some of the specified components or sub-components, and/or additional components, and according to various permutations and combinations of the foregoing. Sub-components can also be implemented as components communicatively coupled to other components rather than included within parent components (hierarchical). Additionally, it should be noted that one or more components may be combined into a single component providing aggregate functionality or divided into several separate sub-components, and any one or more middle layers, such as a management layer, may be provided to communicatively couple to such sub-components in order to provide integrated functionality. Any components described herein may also interact with one or more other components not specifically described herein but generally known by those of skill in the art.
In view of the exemplary systems described supra, methodologies that may be implemented in accordance with the disclosed subject matter will be better appreciated with reference to the flowcharts of the various figures. While for purposes of simplicity of explanation, the methodologies are shown and described as a series of blocks, it is to be understood and appreciated that the claimed subject matter is not limited by the order of the blocks, as some blocks may occur in different orders and/or concurrently with other blocks from what is depicted and described herein. Where non-sequential, or branched, flow is illustrated via flowchart, it can be appreciated that various other branches, flow paths, and orders of the blocks, may be implemented which achieve the same or a similar result. Moreover, not all illustrated blocks may be required to implement the methodologies described hereinafter.
While in some embodiments, a client side perspective is illustrated, it is to be understood for the avoidance of doubt that a corresponding server perspective exists, or vice versa. Similarly, where a method is practiced, a corresponding device can be provided having storage and at least one processor configured to practice that method via one or more components.
While the various embodiments have been described in connection with the preferred embodiments of the various figures, it is to be understood that other similar embodiments may be used or modifications and additions may be made to the described embodiment for performing the same function without deviating there from. Still further, one or more aspects of the above described embodiments may be implemented in or across a plurality of processing chips or devices, and storage may similarly be affected across a plurality of devices. Therefore, the present invention should not be limited to any single embodiment, but rather should be construed in breadth and scope in accordance with the appended claims.
This application claims the benefit of U.S. Provisional Patent Application Ser. No. 61/313,333, filed Mar. 12, 2010, which is titled “SYSTEM AND METHOD FOR PUBLISHING SYNTHESIZED DATA TO FACILITATE PROVIDING INFORMATION AS A SERVICE,” and the entire contents of which are incorporated herein by reference.
Number | Date | Country | |
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61313333 | Mar 2010 | US |