In the context of data analysis, clustering refers to the techniques used to group objects in such a way that objects in the same group (e.g., a cluster) are more similar to each other than to those in other groups (e.g., other clusters). Clustering is applicable to many technical areas. Examples of technical areas include image processing, data compression, computer graphics, machine learning, artificial intelligence, bioinformatics, etc. There are many different types of techniques that may be used to cluster objects together. For instance, some approaches utilize a centroid-based method (e.g., a k-means clustering method) where different centroids are defined for different clusters. The centroids are used to assign objects to clusters. Another type of technique is a density-based clustering method (e.g., a density-based spatial clustering of applications with noise (DBSCAN) method). These types of clustering methods define clusters based on areas with higher density of data objects relative to other areas of data objects. Yet another type of clustering technique is hierarchical clustering (e.g., a single-linkage clustering method, a complete linkage clustering method, etc.). With these types of clustering methods, distance between data objects. Many other types of clustering techniques exist.
In some embodiments, a non-transitory machine-readable medium stores a program executable by at least one processing unit of a device. The program determines a plurality of data objects. Each data object in the plurality of data objects includes a first attribute and a second attribute. The program further sorts values of the first attribute of the plurality of data objects. The program also sorts values of the second attribute of the plurality of data objects. The program further determines a first distance value based on the sorted values of the first attribute of the plurality of data objects. The program also determines a second distance value based on the sorted values of the second attribute of the plurality of data objects. The program further defines a plurality of clusters based on the sorted values of the first attribute of the plurality of data objects, the first distance value, the sorted values of the second attribute of the plurality of data objects, and the second distance value.
In some embodiments, defining the plurality of clusters may include defining a first set of ranges of values based on the first plurality of values and the first distance value; defining a second set of ranges of values based on the second plurality of values and the second distance value; and determining a set of permutations of a range of values in the first set of ranges of values and a range of values in the second set of ranges of values. Each cluster in the plurality of clusters may be defined based on a permutation in the set of permutations of a range of values in the first set of ranges of values and a range of values in the second set of ranges of values. The program may further assign data objects in the plurality of clusters based on the values of the first and second attributes of the data objects.
In some embodiments, the program may further remove outliers from the sorted values of the first attribute of the plurality of data objects; and remove outliers from the sorted values of the second attribute of the plurality of data objects. The program may further receive, from each source in a plurality of sources, a set of answers to a set of questions in a questionnaire regarding a data object; and determine the values of the first attribute and the second attribute of the plurality of data objects based on the set of answers received from the plurality of sources. Determining the values of the first attribute and the second attribute of the plurality of data objects may include translating the set of answers from each source in the plurality of sources to a set of numerical values. The program may further provide, for each cluster in the plurality of clusters, the range of values of the first attribute used to define the cluster and the range of values of the second attribute used to define the cluster.
In some embodiments, a method determines a plurality of data objects. Each data object in the plurality of data objects includes a first attribute and a second attribute. The method further sorts values of the first attribute of the plurality of data objects. The method also sorts values of the second attribute of the plurality of data objects. The method further determines a first distance value based on the sorted values of the first attribute of the plurality of data objects. The method also determines a second distance value based on the sorted values of the second attribute of the plurality of data objects. The method further defines a plurality of clusters based on the sorted values of the first attribute of the plurality of data objects, the first distance value, the sorted values of the second attribute of the plurality of data objects, and the second distance value.
In some embodiments, defining the plurality of clusters may include defining a first set of ranges of values based on the first plurality of values and the first distance value; defining a second set of ranges of values based on the second plurality of values and the second distance value; and determining a set of permutations of a range of values in the first set of ranges of values and a range of values in the second set of ranges of values. Each cluster in the plurality of clusters may be defined based on a permutation in the set of permutations of a range of values in the first set of ranges of values and a range of values in the second set of ranges of values. The method may further assign data objects in the plurality of clusters based on the values of the first and second attributes of the data objects.
In some embodiments, the method may further remove outliers from the sorted values of the first attribute of the plurality of data objects; and remove outliers from the sorted values of the second attribute of the plurality of data objects. The method may further receive, from each source in a plurality of sources, a set of answers to a set of questions in a questionnaire regarding a data object; and determine the values of the first attribute and the second attribute of the plurality of data objects based on the set of answers received from the plurality of sources. Determining the values of the first attribute and the second attribute of the plurality of data objects may include translating the set of answers from each source in the plurality of sources to a set of numerical values. The method may further present, for each cluster in the plurality of clusters, the range of values of the first attribute used to define the cluster and the range of values of the second attribute used to define the cluster.
In some embodiments, a system includes a set of processing units and a non-transitory machine-readable medium that stores instructions. The instructions cause at least one processing unit to determine a plurality of data objects. Each data object in the plurality of data objects includes a first attribute and a second attribute. The instructions further cause the at least one processing unit to sort values of the first attribute of the plurality of data objects. The instructions also cause the at least one processing unit to sort values of the second attribute of the plurality of data objects. The instructions further cause the at least one processing unit to determine a first distance value based on the sorted values of the first attribute of the plurality of data objects. The instructions also cause the at least one processing unit to determine a second distance value based on the sorted values of the second attribute of the plurality of data objects. The instructions further cause the at least one processing unit to define a plurality of clusters based on the sorted values of the first attribute of the plurality of data objects, the first distance value, the sorted values of the second attribute of the plurality of data objects, and the second distance value.
In some embodiments, defining the plurality of clusters may include defining a first set of ranges of values based on the first plurality of values and the first distance value; defining a second set of ranges of values based on the second plurality of values and the second distance value; and determining a set of permutations of a range of values in the first set of ranges of values and a range of values in the second set of ranges of values. Each cluster in the plurality of clusters may be defined based on a permutation in the set of permutations of a range of values in the first set of ranges of values and a range of values in the second set of ranges of values.
In some embodiments, the instructions may further cause the at least one processing unit to assign data objects in the plurality of clusters based on the values of the first and second attributes of the data objects. The instructions may further cause the at least one processing unit to remove outliers from the sorted values of the first attribute of the plurality of data objects; and remove outliers from the sorted values of the second attribute of the plurality of data objects. The instructions may further cause the at least one processing unit to receive, from each source in a plurality of sources, a set of answers to a set of questions in a questionnaire regarding a data object; and determine the values of the first attribute and the second attribute of the plurality of data objects based on the set of answers received from the plurality of sources. Determining the values of the first attribute and the second attribute of the plurality of data objects may include translating the set of answers from each source in the plurality of sources to a set of numerical values.
The following detailed description and accompanying drawings provide a better understanding of the nature and advantages of various embodiments of the present disclosure.
In the following description, for purposes of explanation, numerous examples and specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be evident, however, to one skilled in the art that various embodiment of the present disclosure as defined by the claims may include some or all of the features in these examples alone or in combination with other features described below, and may further include modifications and equivalents of the features and concepts described herein.
Described herein are techniques for clustering data objects based on data object attributes. In some embodiments, a computing system may receive data objects that each has the same attributes. The data objects can have different attribute values for a given attribute. The computing system may employ a clustering technique that uses the attribute values of the attributes of the data objects to define a set of clusters. The clustering technique involves, for each attribute of the data objects, sorting the attribute values of the attribute and then calculating a distance set based on the sorted attribute values. The computing system uses the distance sets to calculate a distance value for each attribute of the data objects. Next, the computing system defines ranges of values for each attribute of the data objects based on the corresponding distance value of the attribute. Finally, the computing system uses the ranges of values to define clusters.
The techniques described in the present application provide a number of benefits and advantages over conventional methods for clustering data objects. For example, using an unsupervised clustering method is more efficient (e.g., uses less processing, uses less memory, etc.) than conventional methods because the algorithm can be executed one time to define optimal clusters. Conventional clustering algorithms may be executed multiple times in order to optimize cluster definitions.
As illustrated in
Application 115 is a software application operating on computing system 110 configured to provide data object management services for client devices 105a-n. For example, application 115 can receive from a client device 105 a data object and values for attributes of the data object. In response to receiving this data, application 115 sends them to data object manager 120 for processing. As another example, application 115 may receive from a client device 105 a questionnaire for a type of data object. In response, application 115 stores the questionnaire in questionnaires storage 130. For some cases where application 115 receives from a client device 105 a request to send a set of users a request for a type of data objects, application 115 accesses questionnaires storage 130 to determine a questionnaire for the type of data object. Then, application 115 sends the set of users (e.g., via client devices 105 that the set of users are using) the request for the type of data object as well as the questionnaire for the type of data object. Application 115 can receive from a client device 105 a request to define clusters for a particular type of data object, which application 115 forwards to clustering engine 125. In some instances, application 115 can receive from a client device 105 a request for cluster definitions for a type of data object. In response to the request, application 115 accesses cluster definitions storage 140, retrieves the requested cluster definitions, and sends the cluster definitions to the client device 105. In some embodiments, application 115 sends the cluster definitions to the client device 105 by providing the client device 105 a GUI that includes the cluster definitions.
Data object manager 120 handles the management of data objects. For instance, data object manager 120 can receive from application 115 a data object and values for attributes of the data object. In response to receiving these data, data object manager 120 stores the data object and the values for its attributes in data objects storage 135. As another example, data object manager 120 may receive from clustering engine 125 a request for a particular type of data objects. In response to the request, data object manager 120 accesses data objects storage, retrieves data objects having the particular type, and sends them to clustering engine 125.
Clustering engine 125 is configured to define clusters for different types of data objects. In some embodiments, clustering engine 125 defines clusters for all the different types of data objects stored in data objects storage 135 at defined intervals (e.g., once every five hours, once a day, once a week, etc.). Clustering engine 125 can receive from application 115 a request to define clusters for a particular type of data object. To define clusters for a type of data object, clustering engine 125 sends data object manager 120 a request for data objects having the type. Once clustering engine 125 receives the requested data objects from data object manager 120, clustering engine 125 sorts the attribute values of each of the attributes of the data objects and then determines a distance set for each attribute of the data objects. Next, clustering engine 125 removes any outliers from each distance set. In some embodiments, clustering engine 125 determines outliers in a distance set using a Tukey's fences technique. For each attribute of the data objects, clustering engine 125 calculates a distance value based on the distance set associated with the attribute. In some embodiments, clustering engine 125 calculates such a distance value by calculating a mean value of the values in the distance set, calculating a standard deviation value based on the values in the distance set, and then adding the mean value and the standard deviation value to form the distance value. Next, clustering engine 125 uses the distance value for the attribute of the data objects to determine ranges of values. Clustering engine 125 determines ranges of values for each of the other attributes the data objects. Finally, clustering engine 125 defines clusters for the type of data objects based on the ranges of values determined for each of the attributes of the data objects and then stores the cluster definitions in cluster definitions storage 140. In some embodiments, clustering engine 125 uses different permutations of the ranges of values of each of the attributes of the data objects to define different clusters.
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Once clustering engine 125 calculates distance values for each attribute of data objects 300, clustering engine 125 uses the distance value for each attribute of data objects 300 to determine ranges of values. In this example, clustering engine 125 determines a set of ranges of values for attribute X of data objects 300 by iterating through the sorted attribute values 400 and calculating a difference between adjacent values. The first value in sorted attribute values 400 is used as the first value in a first range of values. When the difference is greater than the calculated distance value, clustering engine 125 uses the smaller value in the adjacent values as the second value in the first range of values. Clustering engine 125 uses the larger value in the adjacent values as the first value in the second range of values. Clustering engine 125 continues this process to define additional ranges of values.
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In this example, at some point after clustering engine 125 has defined cluster definitions 1000-1054, a user of client device 105b sends, at 220, application 115 a request for cluster definitions for red wine data objects. In response to receiving the request, application 115 accesses, at 225, cluster definitions storage 140 and retrieves the requested cluster definitions. Then, application 115 generates a GUI that includes the cluster definitions and provides, at 230, the GUI to client device 105b.
Next, process 1300 sorts, at 1320, values of the first attribute of the plurality of data objects. Referring to
At 1340, process 1300 determines a first distance value based on the sorted values of the first attribute of the plurality of data objects. Referring to
Next, process 1300 determines, at 1350, a second distance value based on the sorted values of the second attribute of the plurality of data objects. Referring to
Finally, process 1300 defines, at 1360, a plurality of clusters based on the sorted values of the first attribute of the plurality of data objects, the first distance value, the sorted values of the second attribute of the plurality of data objects, and the second distance value. Referring to
The examples and embodiments described above by reference to
Bus subsystem 1426 is configured to facilitate communication among the various components and subsystems of computer system 1400. While bus subsystem 1426 is illustrated in
Processing subsystem 1402, which can be implemented as one or more integrated circuits (e.g., a conventional microprocessor or microcontroller), controls the operation of computer system 1400. Processing subsystem 1402 may include one or more processors 1404. Each processor 1404 may include one processing unit 1406 (e.g., a single core processor such as processor 1404-1) or several processing units 1406 (e.g., a multicore processor such as processor 1404-2). In some embodiments, processors 1404 of processing subsystem 1402 may be implemented as independent processors while, in other embodiments, processors 1404 of processing subsystem 1402 may be implemented as multiple processors integrate into a single chip or multiple chips. Still, in some embodiments, processors 1404 of processing subsystem 1402 may be implemented as a combination of independent processors and multiple processors integrated into a single chip or multiple chips.
In some embodiments, processing subsystem 1402 can execute a variety of programs or processes in response to program code and can maintain multiple concurrently executing programs or processes. At any given time, some or all of the program code to be executed can reside in processing subsystem 1402 and/or in storage subsystem 1410. Through suitable programming, processing subsystem 1402 can provide various functionalities, such as the functionalities described above by reference to process 1300, etc.
I/O subsystem 1408 may include any number of user interface input devices and/or user interface output devices. User interface input devices may include a keyboard, pointing devices (e.g., a mouse, a trackball, etc.), a touchpad, a touch screen incorporated into a display, a scroll wheel, a click wheel, a dial, a button, a switch, a keypad, audio input devices with voice recognition systems, microphones, image/video capture devices (e.g., webcams, image scanners, barcode readers, etc.), motion sensing devices, gesture recognition devices, eye gesture (e.g., blinking) recognition devices, biometric input devices, and/or any other types of input devices.
User interface output devices may include visual output devices (e.g., a display subsystem, indicator lights, etc.), audio output devices (e.g., speakers, headphones, etc.), etc. Examples of a display subsystem may include a cathode ray tube (CRT), a flat-panel device (e.g., a liquid crystal display (LCD), a plasma display, etc.), a projection device, a touch screen, and/or any other types of devices and mechanisms for outputting information from computer system 1400 to a user or another device (e.g., a printer).
As illustrated in
As shown in
Computer-readable storage medium 1420 may be a non-transitory computer-readable medium configured to store software (e.g., programs, code modules, data constructs, instructions, etc.). Many of the components (e.g., application 115, data object manager 120, and clustering engine 125) and/or processes (e.g., process 1300) described above may be implemented as software that when executed by a processor or processing unit (e.g., a processor or processing unit of processing subsystem 1402) performs the operations of such components and/or processes. Storage subsystem 1410 may also store data used for, or generated during, the execution of the software.
Storage subsystem 1410 may also include computer-readable storage medium reader 1422 that is configured to communicate with computer-readable storage medium 1420. Together and, optionally, in combination with system memory 1412, computer-readable storage medium 1420 may comprehensively represent remote, local, fixed, and/or removable storage devices plus storage media for temporarily and/or more permanently containing, storing, transmitting, and retrieving computer-readable information.
Computer-readable storage medium 1420 may be any appropriate media known or used in the art, including storage media such as volatile, non-volatile, removable, non-removable media implemented in any method or technology for storage and/or transmission of information. Examples of such storage media includes RAM, ROM, EEPROM, flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disk (DVD), Blu-ray Disc (BD), magnetic cassettes, magnetic tape, magnetic disk storage (e.g., hard disk drives), Zip drives, solid-state drives (SSD), flash memory card (e.g., secure digital (SD) cards, CompactFlash cards, etc.), USB flash drives, or any other type of computer-readable storage media or device.
Communication subsystem 1424 serves as an interface for receiving data from, and transmitting data to, other devices, computer systems, and networks. For example, communication subsystem 1424 may allow computer system 1400 to connect to one or more devices via a network (e.g., a personal area network (PAN), a local area network (LAN), a storage area network (SAN), a campus area network (CAN), a metropolitan area network (MAN), a wide area network (WAN), a global area network (GAN), an intranet, the Internet, a network of any number of different types of networks, etc.). Communication subsystem 1424 can include any number of different communication components. Examples of such components may include radio frequency (RF) transceiver components for accessing wireless voice and/or data networks (e.g., using cellular technologies such as 2G, 3G, 4G, 5G, etc., wireless data technologies such as Wi-Fi, Bluetooth, ZigBee, etc., or any combination thereof), global positioning system (GPS) receiver components, and/or other components. In some embodiments, communication subsystem 1424 may provide components configured for wired communication (e.g., Ethernet) in addition to or instead of components configured for wireless communication.
One of ordinary skill in the art will realize that the architecture shown in
Processing system 1502, which can be implemented as one or more integrated circuits (e.g., a conventional microprocessor or microcontroller), controls the operation of computing device 1500. As shown, processing system 1502 includes one or more processors 1504 and memory 1506. Processors 1504 are configured to run or execute various software and/or sets of instructions stored in memory 1506 to perform various functions for computing device 1500 and to process data.
Each processor of processors 1504 may include one processing unit (e.g., a single core processor) or several processing units (e.g., a multicore processor). In some embodiments, processors 1504 of processing system 1502 may be implemented as independent processors while, in other embodiments, processors 1504 of processing system 1502 may be implemented as multiple processors integrate into a single chip. Still, in some embodiments, processors 1504 of processing system 1502 may be implemented as a combination of independent processors and multiple processors integrated into a single chip.
Memory 1506 may be configured to receive and store software (e.g., operating system 1522, applications 1524, I/O module 1526, communication module 1528, etc. from storage system 1520) in the form of program instructions that are loadable and executable by processors 1504 as well as data generated during the execution of program instructions. In some embodiments, memory 1506 may include volatile memory (e.g., random access memory (RAM)), non-volatile memory (e.g., read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, etc.), or a combination thereof.
I/O system 1508 is responsible for receiving input through various components and providing output through various components. As shown for this example, I/O system 1508 includes display 1510, one or more sensors 1512, speaker 1514, and microphone 1516. Display 1510 is configured to output visual information (e.g., a graphical user interface (GUI) generated and/or rendered by processors 1504). In some embodiments, display 1510 is a touch screen that is configured to also receive touch-based input. Display 1510 may be implemented using liquid crystal display (LCD) technology, light-emitting diode (LED) technology, organic LED (OLED) technology, organic electro luminescence (OEL) technology, or any other type of display technologies. Sensors 1512 may include any number of different types of sensors for measuring a physical quantity (e.g., temperature, force, pressure, acceleration, orientation, light, radiation, etc.). Speaker 1514 is configured to output audio information and microphone 1516 is configured to receive audio input. One of ordinary skill in the art will appreciate that I/O system 1508 may include any number of additional, fewer, and/or different components. For instance, I/O system 1508 may include a keypad or keyboard for receiving input, a port for transmitting data, receiving data and/or power, and/or communicating with another device or component, an image capture component for capturing photos and/or videos, etc.
Communication system 1518 serves as an interface for receiving data from, and transmitting data to, other devices, computer systems, and networks. For example, communication system 1518 may allow computing device 1500 to connect to one or more devices via a network (e.g., a personal area network (PAN), a local area network (LAN), a storage area network (SAN), a campus area network (CAN), a metropolitan area network (MAN), a wide area network (WAN), a global area network (GAN), an intranet, the Internet, a network of any number of different types of networks, etc.). Communication system 1518 can include any number of different communication components. Examples of such components may include radio frequency (RF) transceiver components for accessing wireless voice and/or data networks (e.g., using cellular technologies such as 2G, 3G, 4G, 5G, etc., wireless data technologies such as Wi-Fi, Bluetooth, ZigBee, etc., or any combination thereof), global positioning system (GPS) receiver components, and/or other components. In some embodiments, communication system 1518 may provide components configured for wired communication (e.g., Ethernet) in addition to or instead of components configured for wireless communication.
Storage system 1520 handles the storage and management of data for computing device 1500. Storage system 1520 may be implemented by one or more non-transitory machine-readable mediums that are configured to store software (e.g., programs, code modules, data constructs, instructions, etc.) and store data used for, or generated during, the execution of the software.
In this example, storage system 1520 includes operating system 1522, one or more applications 1524, I/O module 1526, and communication module 1528. Operating system 1522 includes various procedures, sets of instructions, software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communication between various hardware and software components. Operating system 1522 may be one of various versions of Microsoft Windows, Apple Mac OS, Apple OS X, Apple macOS, and/or Linux operating systems, a variety of commercially-available UNIX or UNIX-like operating systems (including without limitation the variety of GNU/Linux operating systems, the Google Chrome® OS, and the like) and/or mobile operating systems such as Apple iOS, Windows Phone, Windows Mobile, Android, BlackBerry OS, Blackberry 10, and Palm OS, WebOS operating systems.
Applications 1524 can include any number of different applications installed on computing device 1500. Examples of such applications may include a browser application, an address book application, a contact list application, an email application, an instant messaging application, a word processing application, JAVA-enabled applications, an encryption application, a digital rights management application, a voice recognition application, location determination application, a mapping application, a music player application, etc.
I/O module 1526 manages information received via input components (e.g., display 1510, sensors 1512, and microphone 1516) and information to be outputted via output components (e.g., display 1510 and speaker 1514). Communication module 1528 facilitates communication with other devices via communication system 1518 and includes various software components for handling data received from communication system 1518.
One of ordinary skill in the art will realize that the architecture shown in
As shown, cloud computing system 1612 includes one or more applications 1614, one or more services 1616, and one or more databases 1618. Cloud computing system 1600 may provide applications 1614, services 1616, and databases 1618 to any number of different customers in a self-service, subscription-based, elastically scalable, reliable, highly available, and secure manner.
In some embodiments, cloud computing system 1600 may be adapted to automatically provision, manage, and track a customer's subscriptions to services offered by cloud computing system 1600. Cloud computing system 1600 may provide cloud services via different deployment models. For example, cloud services may be provided under a public cloud model in which cloud computing system 1600 is owned by an organization selling cloud services and the cloud services are made available to the general public or different industry enterprises. As another example, cloud services may be provided under a private cloud model in which cloud computing system 1600 is operated solely for a single organization and may provide cloud services for one or more entities within the organization. The cloud services may also be provided under a community cloud model in which cloud computing system 1600 and the cloud services provided by cloud computing system 1600 are shared by several organizations in a related community. The cloud services may also be provided under a hybrid cloud model, which is a combination of two or more of the aforementioned different models.
In some instances, any one of applications 1614, services 1616, and databases 1618 made available to client devices 1602-1608 via networks 1610 from cloud computing system 1612 is referred to as a “cloud service.” Typically, servers and systems that make up cloud computing system 1612 are different from the on-premises servers and systems of a customer. For example, cloud computing system 1612 may host an application and a user of one of client devices 1602-1608 may order and use the application via networks 1610.
Applications 1614 may include software applications that are configured to execute on cloud computing system 1612 (e.g., a computer system or a virtual machine operating on a computer system) and be accessed, controlled, managed, etc. via client devices 1602-1608. In some embodiments, applications 1614 may include server applications and/or mid-tier applications (e.g., HTTP (hypertext transport protocol) server applications, FTP (file transfer protocol) server applications, CGI (common gateway interface) server applications, JAVA server applications, etc.). Services 1616 are software components, modules, application, etc. that are configured to execute on cloud computing system 1612 and provide functionalities to client devices 1602-1608 via networks 1610. Services 1616 may be web-based services or on-demand cloud services.
Databases 1618 are configured to store and/or manage data that is accessed by applications 1614, services 1616, and/or client devices 1602-1608. For instance, storages 130-140 may be stored in databases 1618. Databases 1618 may reside on a non-transitory storage medium local to (and/or resident in) cloud computing system 1612, in a storage-area network (SAN), on a non-transitory storage medium local located remotely from cloud computing system 1612. In some embodiments, databases 1618 may include relational databases that are managed by a relational database management system (RDBMS). Databases 1618 may be a column-oriented databases, row-oriented databases, or a combination thereof. In some embodiments, some or all of databases 1618 are in-memory databases. That is, in some such embodiments, data for databases 1618 are stored and managed in memory (e.g., random access memory (RAM)).
Client devices 1602-1608 are configured to execute and operate a client application (e.g., a web browser, a proprietary client application, etc.) that communicates with applications 1614, services 1616, and/or databases 1618 via networks 1610. This way, client devices 1602-1608 may access the various functionalities provided by applications 1614, services 1616, and databases 1618 while applications 1614, services 1616, and databases 1618 are operating (e.g., hosted) on cloud computing system 1600. Client devices 1602-1608 may be computer system 1400 or computing device 1500, as described above by reference to
Networks 1610 may be any type of network configured to facilitate data communications among client devices 1602-1608 and cloud computing system 1612 using any of a variety of network protocols. Networks 1610 may be a personal area network (PAN), a local area network (LAN), a storage area network (SAN), a campus area network (CAN), a metropolitan area network (MAN), a wide area network (WAN), a global area network (GAN), an intranet, the Internet, a network of any number of different types of networks, etc.
The above description illustrates various embodiments of the present disclosure along with examples of how aspects of the present disclosure may be implemented. The above examples and embodiments should not be deemed to be the only embodiments, and are presented to illustrate the flexibility and advantages of various embodiments of the present disclosure as defined by the following claims. Based on the above disclosure and the following claims, other arrangements, embodiments, implementations and equivalents will be evident to those skilled in the art and may be employed without departing from the spirit and scope of the present disclosure as defined by the claims.
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20220398263 A1 | Dec 2022 | US |