Patent Publication Number: US-11048505-B2

Title: Approach to summarize code usage

Description:
BACKGROUND 
     The subject disclosure relates to summarizing code usage, and more specifically, to summarizing usage associated with one or more web application programming interface requests. 
     SUMMARY 
     The following presents a summary to provide a basic understanding of one or more embodiments of the invention. This summary is not intended to identify key or critical elements, or delineate any scope of the particular embodiments or any scope of the claims. Its sole purpose is to present concepts in a simplified form as a prelude to the more detailed description that is presented later. In one or more embodiments described herein, systems, computer-implemented methods, apparatuses and/or computer program products that can summarize code usage associated with one or more web application programming interface requests are described. 
     According to an embodiment, a computer-implemented method is provided. The computer-implemented method can comprise evaluating, by a system operatively coupled to a processor, data from a data repository, wherein the evaluating can be based on a defined machine learning process. The computer-implemented method can also comprise generating, by the system, a usage summary of the data, wherein the usage summary can be based on a statistic derived from a web application programming interface request, and the web application programming interface request can be associated with the data. 
     According to another embodiment, another computer-implemented method is provided. The computer-implemented method can comprise evaluating, by a system operatively coupled to a processor, data from a data repository, wherein the evaluating can be based on a defined machine learning process. The computer-implemented method can also comprise generating, by the system, a usage summary of the data, wherein the usage summary can comprise a cluster of web application programming interface requests from a plurality of web application programming interface requests, and the cluster of web application programming interface requests can be associated with the data. 
     According to another embodiment, a computer program product is provided. The computer program product can be for summarizing data usage associated with a web application programming interface request. The computer program product can comprise a computer readable storage medium having program instructions embodied therewith. The program instructions can be executable by a processor to cause the processor to evaluate data from a data repository based on a defined machine learning process. Also, the program instructions can cause the processor to generate a usage summary of the data. The usage summary can comprise a statistic derived from a web application programming interface request associated with the data. The usage summary can also comprise a cluster of web application programming interface requests from a plurality of web application programming interface requests, wherein the cluster of web application programming interface requests can be associated with the data. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  depicts a cloud computing environment in accordance with one or more embodiments described herein. 
         FIG. 2  depicts abstraction model layers in accordance with one or more embodiments described herein. 
         FIG. 3  illustrates a block diagram of an example, non-limiting system that can summarize code usage associated with one or more web application programming interface requests in accordance with one or more embodiments described herein. 
         FIG. 4  illustrates a flow diagram of an example, non-limiting process that can be implemented by a system, computer program product, and/or computer-implemented method to summarize code usage associated with one or more web application programming interface requests in accordance with one or more embodiments described herein. 
         FIG. 5  illustrates a block diagram of an example, non-limiting system that can summarize code usage associated with one or more web application programming interface requests in accordance with one or more embodiments described herein. 
         FIG. 6  illustrates a flow diagram of an example, non-limiting process that can be implemented by a system, computer program product, and/or computer-implemented method to summarize code usage associated with one or more web application programming interface requests in accordance with one or more embodiments described herein. 
         FIG. 7  illustrates a block diagram of an example, non-limiting system that can summarize code usage associated with one or more web application programming interface requests in accordance with one or more embodiments described herein. 
         FIG. 8  illustrates a flow chart of an example, non-limiting computer-implemented method that can facilitate summarizing code usage associated with one or more web application programming interface requests in accordance with one or more embodiments described herein. 
         FIG. 9  illustrates another flow chart of an example, non-limiting computer-implemented method that can facilitate summarizing code usage associated with one or more web application programming interface requests in accordance with one or more embodiments described herein. 
         FIG. 10  illustrates a block diagram of an example, non-limiting operating environment in which one or more embodiments described herein can be facilitated. 
     
    
    
     DETAILED DESCRIPTION 
     The following detailed description is merely illustrative and is not intended to limit embodiments and/or application or uses of embodiments. Furthermore, there is no intention to be bound by any expressed or implied information presented in the preceding Background or Summary sections, or in the Detailed Description section. 
     One or more embodiments are now described with reference to the drawings, wherein like referenced 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 more thorough understanding of the one or more embodiments. It is evident, however, in various cases, that the one or more embodiments can be practiced without these specific details. 
     It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
     Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
     Characteristics are as follows: 
     On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service&#39;s provider. 
     Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). 
     Resource pooling: the provider&#39;s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). 
     Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. 
     Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. 
     Service Models are as follows: 
     Software as a Service (SaaS): the capability provided to the consumer is to use the provider&#39;s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. 
     Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. 
     Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
     Deployment Models are as follows: 
     Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. 
     Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises. 
     Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. 
     Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds). 
     A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes. 
     Referring now to  FIG. 1 , illustrative cloud computing environment  50  is depicted. As shown, cloud computing environment  50  includes one or more cloud computing nodes  10  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  54 A, desktop computer  54 B, laptop computer  54 C, and/or automobile computer system  54 N may communicate. Nodes  10  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment  50  to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices  54 A-N shown in  FIG. 1  are intended to be illustrative only and that computing nodes  10  and cloud computing environment  50  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). 
     Referring now to  FIG. 2 , a set of functional abstraction layers provided by cloud computing environment  50  ( FIG. 1 ) is shown. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity. It should be understood in advance that the components, layers, and functions shown in  FIG. 2  are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity. 
     Hardware and software layer  60  includes hardware and software components. Examples of hardware components include: mainframes  61 ; RISC (Reduced Instruction Set Computer) architecture based servers  62 ; servers  63 ; blade servers  64 ; storage devices  65 ; and networks and networking components  66 . In some embodiments, software components include network application server software  67  and database software  68 . 
     Virtualization layer  70  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  71 ; virtual storage  72 ; virtual networks  73 , including virtual private networks; virtual applications and operating systems  74 ; and virtual clients  75 . 
     In one example, management layer  80  may provide the functions described below. Resource provisioning  81  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  82  provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal  83  provides access to the cloud computing environment for consumers and system administrators. Service level management  84  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  85  provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  90  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation  91 ; software development and lifecycle management  92 ; virtual classroom education delivery  93 ; data analytics processing  94 ; transaction processing  95 ; and summarizing code usage  96 . Various embodiments of the present invention can utilize the cloud computing environment described with reference to  FIGS. 1 and 2  to summarize code usage associated with one or more web application programming interface requests. 
     As used herein, the term “web application programming interface (web API)” can refer to a programmatic interface that can facilitate exposing, analyzing, collecting, and/or sending data over hypertext transfer protocols (HTTP). Web APIs can be used to build services compatible with a broad range of devices (e.g., servers, personal computers, smart phones, smart wearables such as smart watches, and/or tablets) and/or software (e.g., web browsers, operating systems such as ANDROID® and/or IOS®, websites, and/or web applications). Also, web APIs can follow representational state transfer (REST) principals. 
     Web APIs have become ubiquitous in recent years and continue to grow in popularity. The majority of commercialized software applications offer some form of a web API, and many web APIs interact with one or more cloud services. When invoking such a web API, a proven type of effective web API documentation can include one or more code examples and/or one or more pieces of code usage information. However, code examples and/or code usage information are traditionally widely dispersed and difficult to locate. For example, a code example and/or code usage information may: not be available in a formal type of documentation such as on official API documentation; and/or may be buried in code stored in a data repository and thus, unavailable to search engines and/or aggregation software. 
     Various embodiments of the present invention can be directed to computer processing systems, computer-implemented methods, apparatus and/or computer program products that facilitate the efficient, effective, and autonomous (e.g., without direct human guidance) summarization of code usage information associated with one or more web API requests. One or more embodiments described herein can aggregate one or more web API requests to: determine statistics regarding the subject requests; and/or generate lessons, utilizing machine learning processes, based on the subject requests. Further one or more embodiments described herein can cluster similar web API requests to identify one or more representative web API requests indicative of a subject topic and/or parameter. Moreover, various embodiments described herein can filter one or more web API requests to identify one or more web API requests that meet a quality standard. 
     The computer processing systems, computer-implemented methods, apparatus and/or computer program products employ hardware and/or software to solve problems that are highly technical in nature (e.g., collecting and/or analyzing code from a data repository to summarize code usage associated with one or more web API requests), that are not abstract and cannot be performed as a set of mental acts by a human. For example, a human, or a plurality of humans, cannot efficiently analyze all the code in a data repository nor stay up-to-date with new code being added to the data repository on a constant basis. In contrast, various embodiments of the computer processing systems, computer-implemented methods, apparatus and/or computer program products employing hardware and/or software described herein can analyze insurmountable amounts of code in a data repository and generate a summary of code usage in association with one or more web API requests. The summary can include, but is not limited to: statistics regarding the web API requests and their parameters, lessons learned from the web API requests using machine learning process (e.g., via artificial intelligence systems), and/or one or more web API request representatives that can be indicative of similarities between web API requests. Moreover, various embodiments described herein can filter web API requests prior to generating the summary so as to ensure that the summary is based on one or more web API requests that meet a predefined criterion. 
     One or more embodiments may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
       FIG. 3  illustrates a block diagram of an example, non-limiting system  300  that can summarize code usage associated with one or more web APIs. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity. Aspects of systems (e.g., system  300  and the like), apparatuses or processes in various embodiments of the present invention can constitute one or more machine-executable components embodied within one or more machines, e.g., embodied in one or more computer readable mediums (or media) associated with one or more machines. Such components, when executed by the one or more machines, e.g., computers, computing devices, virtual machines, etc. can cause the machines to perform the operations described. 
     As shown in  FIG. 3 , the system  300  can comprise one or more servers  302 , one or more networks  304 , one or more web API requests  306 , and one or more data repositories  308 . The server  302  can comprise usage summary component  310 . The usage summary component  310  can further comprise reception component  312 , aggregation component  314 , clustering component  316 , and display component  318 . Also, the server  302  can comprise or otherwise be associated with at least one memory  320 . The server  302  can further comprise a system bus  322  that can couple to various components such as, but not limited to, the usage summary component  310  and associated components, memory  320  and/or a processor  324 . While a server  302  is illustrated in  FIG. 3 , in other embodiments, multiple devices of various types can be associated with or comprise the features shown in  FIG. 3 . Further, the server  302  can communicate with the cloud environment depicted in  FIGS. 1 and 2  via the one or more networks  304 . 
     One or more entities utilizing one or more web APIs can generate one or more web API requests  306 . The one or more web API requests  306  can send and/or request data to and/or from one or more data repositories  308 . The web API requests  306  can be operably coupled to the server  302 , and/or the web API requests  306  can communicate with the server  302  via one or more networks  304 . Also, the web API requests  306  can be operably coupled to the data repository  308 , and/or the web API requests  306  can communicate with the data repository  308  via one or more networks  304 . 
     The one or more data repositories  308  can comprise one or more devices that store data. The one or more data repositories  308  can be located in a cloud environment and/or can be reached via cloud technology. For example, the one or more data repositories  308  can store one or more files for one or more software projects (e.g., proprietary and/or open-source projects). One or more of the software projects stored in the data repositories  308  can be accessed by a unique universal resource locator (URL). In various embodiments described herein, the one or more data repositories  308  can comprise one or more version control systems. The data repositories  308  can be open-source repositories or proprietary repositories. Example data repositories  308  can include, but are not limited to: GIT®, GITHUB®, VERACITY®, ARX, BITKEEPER®, CODEVILLE®, DARCS®, DCVS®, FOSSIL®, GNU ARCH®, GNU BAZAAR®, MERCURIAL®, MONOTONE®, SVK®, TEAM FOUNDATION SERVER®, VISUAL STUDIO TEAM SERVICES®, and/or VAULT®. The data repositories  308  can be operably coupled to the server  302 , and/or the data repositories  308  can communicate with the server  302  via one or more networks  304 . Also, the data repositories  308  can be operably coupled to the web API requests  306 , and/or the data repositories  308  can communicate with the web API requests  306  via one or more networks  304 . 
     The one or more networks  304  can comprise wired and wireless networks, including, but not limited to, a cellular network, a wide area network (WAN) (e.g., the Internet) or a local area network (LAN). For example, the server  302  can communicate with the data repository  308  and the web API request  306  (and vice versa) using virtually any desired wired or wireless technology including for example, but not limited to: cellular, WAN, wireless fidelity (Wi-Fi), Wi-Max, WLAN, Bluetooth technology, cloud technology, a combination thereof, and/or the like. Further, although in the embodiment shown the usage summary component  310  can be provided on the one or more servers  302 , it should be appreciated that the architecture of system  300  is not so limited. For example, the usage summary component  310 , or one or more components of usage summary component  310 , can be located at another computer device, such as another server device, a client device, etc. 
     The reception component  312  can receive one or more of the web API requests  306  and/or data from the one or more data repositories  308  (e.g., code examples, and/or code usage information). The reception component  312  can be operably coupled to the server  302 , and/or the reception component  312  can communicate with the server  302  via one or more networks  304 . The reception component  312  can be operably coupled to the usage summary component  310 , and/or the reception component  312  can communicate with the usage summary component  310  via one or more networks  304 . Also, the reception component  312  can be operably coupled to the data repository  308 , and/or the reception component  312  can communicate with the data repository  308  via one or more networks  304 . 
     The aggregation component  314  can determine one or more statistics regarding the one or more web API requests  306  received by the reception component  312 . The aggregation component  314  can be operably coupled to the reception component  312 , and/or the aggregation component  314  can communicate with the reception component  312  via one or more networks  304 . Additionally, while  FIG. 3  illustrates the aggregation component  314  as comprising the server  302 , other embodiments in which the aggregation component  314  is located outside the server  302  are also envisage. 
     In one or more embodiments, the aggregation component  314  can evaluate data received by the reception component  312  from one or more data repositories  308 . The aggregation component  314  can be operably coupled to the reception component  312 , and/or the aggregation component  314  can communicate with the reception component  312  via one or more networks  304 . The aggregation component  314  can evaluate the data using one or more machine learning processes. Further, in various embodiments, the aggregation component  314  can determine one or more statistics derived from the one or more web API requests  306  received by the reception component  312 . Also, the one or more web API requests  306  can be associated with data evaluated by the aggregation component  314 . 
     Statistics determined by the aggregation component  314  can regard, but are not limited to: the type of web API used to generate the one or more web API requests  306 ; one or more resources associated with the one or more web API requests  306 ; one or more endpoints associated with the one or more web API requests  306 ; one or more queries associated with one or more web API requests  306 ; one or more payloads associated with the one or more web API requests  306 ; and one or more library specific parameters (e.g., “dataType” for JQuery) associated with the one or more web API requests  306 ; a combination thereof; and/or the like. Resources associated with one or more web API requests  306  can comprise code samples, databases, documents, protocols, websites, a combination thereof, and/or the like that can be accessed via the one or more networks  304  (e.g., the Internet and/or cloud technology). Example, resources include, but are not limited to: wide area information server (WAIS) databases, file transfer protocol (FTP) servers, and/or telnet destinations. Endpoints associated with the one or more web API requests  306  can indicate the location of one or more resources. Queries associated with the one or more web API requests  306  can comprise one or more functions that can be used to filter criteria. The query functions can accept one or more parameters and return one or more values. 
     In determining the statistics, the aggregation component  314  can aggregate a plurality of web API requests  306 . Further, the aggregation component  314  can arrange the plurality of web API requests  306  in a hierarchy based one or more of the determined statistics (e.g., type of web API, a resource, an endpoint, a query, a payload, and/or a library specific parameter). 
     In one or more embodiments, the aggregation component  314  can further generate information indicative of one or more lessons learned from a machine learning process (e.g., association rule mining) based on: a parameter value associated with one or more web API requests  306 , a response field associated with one or more web API requests  306 , a combination thereof, and/or the like. In one or more embodiments, the aggregation component  314  can utilize recurrent neural networks and/or any other suitable machine learning method to generate lessons learned regarding one or more web API requests  306 . Accordingly, such web API requests  306  can be sent to the workloads layer  90  of the cloud computer environment depicted in  FIG. 2 , whereby, among other things, the web API requests  306  can be recorded and further analyzed by the summarizing code usage  96  and/or the data analytics processing  94  workloads. 
     For example, the aggregation component  314  can generate quantitative lessons learned such as, but not limited to: frequently used endpoints associated with the one or more web API requests  306 ; frequently used parameters (e.g. the top three used query parameters), and their values, associated with the one or more web API requests  306 ; parameter fields associated with the one or more web API requests  306  that are frequently utilized in combination with each other (e.g., the most utilized pair of query parameters); frequently used response fields associated with the one or more web API requests  306 ; endpoint sequences that can comprise a dataflow dependency (e.g., an authentication pattern which pertains to calling an endpoint to authenticate and then calling another endpoint for an operation, the get-id pattern which pertains to calling an endpoint to obtain a client specific token and then calling another endpoint for operation); a combination thereof; and/or the like. In another example, the aggregation component  314  can generate qualitative lessons learned. Qualitative lessons learned can comprise identifying one or more samples of code from the data repository  308  that include a combination of multiple parameters that are designated as important to an entity generating one or more of the web API requests  306 . In various embodiments, the aggregation component  314  can generate quantitative lessons learned and qualitative lessons in conjunction and/or separately. 
     The clustering component  316  can generate one or more clusters of one or more web API requests  306  received by the reception component  312 . The clustering component  316  can be operably coupled to the reception component  312 , and/or the clustering component  316  can communicate with the reception component  312  via one or more networks  304 . Additionally, the clustering component  316  can be operably coupled to the aggregation component  314 , and/or the clustering component  316  can communicate with the aggregation component  314  via one or more networks  304 . Further, while  FIG. 3  illustrates the clustering component  316  as comprising the server  302 , other embodiments in which the clustering component  316  is located outside the server  302  are also envisage. In one or more embodiments, the clustering component  316  can generate the one or more clusters based on statistics determined by the aggregation component  314  and/or lesson generated by the aggregation component  314 . 
     In various embodiments, the clustering component  316  can use a clustering algorithm to designate one or more web API requests  306  as a cluster. In another example, the clustering component  316  can use a clustering algorithm to group two or more web API requests  306  from a plurality of web API requests  306  received by the reception component  312 . Also, the clustering component  316  can group two or more web API requests  306  to form a cluster based on one or more similarities regarding: composition, structure, statistics, parameters, endpoints, a combination thereof, and/or the like. Further, the clustering component  316  can designate a web API request  306  included in a cluster as the subject cluster&#39;s representative. A cluster representative can serve as an indication of the composition of the subject cluster. 
     In other words, the clustering component  316  can: designate one or more web API requests  306  as a cluster; group two or more web API requests  306  together to form a cluster; and/or designate a cluster representative for a subject cluster. Each cluster generated by the clustering component  316  can comprise one or more web API requests  306  received by the reception component  312 . The clustering component  316  can generate one or more clusters (e.g., by designating one or more web API requests  306  as one or more clusters and/or by grouping two or more web API requests  306  to form one or more clusters) based on similarities between web API requests  306 , statistics determined by the aggregation component  314 , and/or lessons generated by the aggregation component  314 . Further one or more clusters can comprise a cluster representative, designated by the clustering component  316 , that can serve as a representation of the kind of web API requests  306  that comprise a subject cluster. 
     In one or more embodiments, the clustering component  316  can use a cluster algorithm to group similar web API requests  306  for a given endpoint and control a number of representative web API requests  306 . For example, the clustering component  316  can consider web API requests  306  to be similar based on the number of parameter fields each web API request  306  has in common. Web API requests  306  with the same parameter values can be considered by the clustering component  316  to be more similar to each other than web API requests  306  with different values, and for ordinal values (e.g., integer) closer values can be considered to be similar. 
     The clustering component  316  can utilize a distance function to dictate how a grouping (e.g., a cluster) can be obtained. The distance function can combine the following dimensions: whether or not a given parameter and/or response field is being used by a subject web API request  306 ; data values for a subject web API request  306 ; how a parameter value (e.g., a location input) is obtained; the direction of the response fields (e.g., a parameter value can be used for another web API request  306  and/or for a user interface); a combination thereof; and/or the like. For example, parameter values can be obtained: from another web API request  306  (e.g., foursquare_v2_id can come from a previous call to the FourSquare API: http://api.foursquare.com/v2/venues/search); from hard-coded data (e.g., a code sample that has latitude and/or longitude values hard-coded and/or a code sample that is JavaScript Object Notation (JSON) formatted); and/or from outside a resource (e.g., a code sample that has latitude and/or longitude values provided outside a file). 
     In various embodiments, the clustering component  316  can generate one or more clusters by filtering out web API requests  306  that comprise one or more immaterial parameters (e.g., an identification parameter and/or an access key parameter). For example, an immaterial parameter can be a parameter that is so commonly incorporated into web API requests  306  that considering the immaterial parameter would fail to facilitate identifying varying degrees of similarity amongst the web API requests  306 . The clustering component  316  can generate a distinct cluster for web API requests  306  found to share only immaterial parameters (e.g., identification and/or access related parameters) with a subject web API request  306 . Further, the clustering component  316  can generate one or more clusters comprising the remaining web API requests  306  (e.g., web API requests  306  that share one or more query parameters with a subject web API request  306  other than immaterial parameters) based on at least a distance function as described herein. 
     For example, a web API request  306  can use one or more query parameters to specify a location input. The clustering component  316  can group web API requests  306  based on the query parameter used by a subject web API request  306 . For instance, web API requests  306  containing a longitude and/or longitude parameter to specify location can be grouped in the same cluster, whereas web API requests  306  containing another distinct location parameter can be grouped into a different cluster. Additionally, the clustering component  316  can generate descriptions regarding the composition of a subject cluster. For one or more clusters, the clustering component  316  can create an overview of a subject cluster based on an intersection of parameters within the cluster. Also, for one or more clusters, the clustering component  316  can create a list of parameters included the subject cluster and/or the frequency of each parameter. 
     In various embodiments, the aggregation component  314  can determine statistics and/or generated lessons based on one or more clusters, one or more cluster representatives, and/or one or more cluster descriptions generated by the clustering component  316 . In an embodiment, the system  300  can comprise the aggregation component  314  and not the clustering component  316 . In another embodiment, the system  300  can comprise the clustering component  316  without the aggregation component  314 . In another embodiment, the system  300  can comprise both the aggregation component  314  and the clustering component  316 . 
     In various embodiments, the display component  318  can be operably coupled to the reception component  312 , the aggregation component  314 , and/or the clustering component  316 . In various embodiments, the display component  318  can communicate with the reception component  312 , the aggregation component  314 , and/or the clustering component  316  via one or more networks  304 . Additionally, while  FIG. 3  illustrates the display component  318  as comprising the server  302 , other embodiments in which the display component  318  is located outside the server  302  are also envisage. 
     The display component  318  can display features such as: one or more statistics determined by the aggregation component  314 ; one or more lessons generated by the aggregation component  314 ; one or more clusters generated by the clustering component  316 ; one or more cluster representatives designated by the clustering component  316 ; one or more cluster descriptions generated by the clustering component  316 ; and/or a combination thereof. The display component  318  can display the features via a screen including, but not limited to, a liquid crystal display (LCD) and/or a light-emitting diode (LED) display. 
       FIG. 4  illustrates a block diagram of a process  400  that can be implemented by system  300  in accordance with one or more embodiments described herein. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity. As shown in  FIG. 4 , the aggregation component  314  and/or the clustering component  316  can receive one or more web API requests  306 . At  402  the aggregation component  314  can determine statistics regarding the web API requests  306  in accordance with various embodiments described herein. At  404  the aggregation component  314  can generate information indicative one or more lessons learned regarding the web API requests  306  in accordance with various embodiments described herein. At  406  the clustering component  316  can generate one or more clusters comprising the web API requests  306  in accordance with various embodiments described herein. At  408  the clustering component  316  can designate one or more cluster representatives from the web API requests  306  in accordance with various embodiments described herein. 
     In one or more embodiments, the aggregation component  314  and the clustering component  316  can communicate with each other and share outputs. Further, the outputs of the aggregation component  314  (e.g., statistics and/or lessons learned) and/or the clustering component  316  (e.g., clusters and/or cluster representatives) can be sent to the display component  318 . At  410 , the display component  318  can display a usage summary comprising one or more of the outputs. In one or more embodiments, the usage summary can comprise one or more outputs from only the aggregation component  314 . In various embodiments, the usage summary can comprise one or more outputs from only the clustering component  316 . In various embodiments, the usage summary can comprise one or more outputs from both the aggregation component  314  and the clustering component  316 . 
       FIG. 5  illustrates another block diagram of the system  300  further comprising a filtering component  502 . Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity. In various embodiments, the filtering component  502  can be operably coupled to the reception component  312 , the aggregation component  314 , and/or the clustering component  316 . In various embodiments, the filtering component  502  can communicate with the reception component  312 , the aggregation component  314 , and/or the clustering component  316  via one or more networks  304 . Additionally, while  FIG. 5  illustrates the filtering component  502  as comprising the server  302 , other embodiments in which the filtering component  502  is located outside the server  302  are also envisage. 
     The filtering component  502  can filter the web API requests  306  to identify one or more web API requests  306  that meet one or more quality standards. In various embodiments, the filtering component  502  can filter the web API requests  306  prior to their evaluation by the aggregation component  314  and/or the clustering component  316  to ensure that the outputs of the aggregation component  314  and/or the clustering component  316  are based on high quality web API requests  306 . 
     In one or more embodiments, the filtering component  502  can filter one or more web API requests  306  based on a reputation established by a data repository  308  associated with a subject web API request  306  and/or the web API that generated the subject web API request  306 . The reputation can regard a subject web API request  306  and/or data associated with a subject web API request  306 . The reputation can be indicated via a rating system (e.g., a number of likes, stars, thumbs-up, and/or the like) and/or use frequency (e.g., the number of forks associated with a subject web API request  306 ). 
     In one or more embodiments, the filtering component  502  can filter one or more web API requests  306  based on comparing a subject web API request  306  to one or more specifications, wherein the specifications can be defined by a user of the system  300 . In various embodiments, the filtering component  502  can filter one or more web API requests  306  based on one or more parameters contained in the web API requests  306 . For example, the filtering performed by the filtering component  502  can be based on one or more parameters regarding, but not limited to: framework of a web API request  306 ; client specific values of a web API request  306 ; and usage (e.g., frequency and/or use history) of a web API request  306 . Further, the filtering component  502  can use one or more artificial intelligence techniques to combine filtering parameters. For example, the filtering component  502  can utilized a rule-based approach to filter one or more web API requests  306 . Also, where training data is available (e.g., annotated web API requests  306  that were previously determined to be of high quality) the filtering component  502  can use one or more machine learning techniques to filter web API requests  306 . 
       FIG. 6  illustrates a block flow diagram of an example, non-limiting process  600  that can be implemented by a system  300  comprising at least filtering component  502  in conjunction with aggregation component  314 , clustering component  316 , and/or display component  318 . Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity. As shown in  FIG. 6 , the filtering component  502  can receive one or more web API requests  306 . At  602 , the filtering component  502  can perform reputation filtering to filter the one or more web API requests  306  in accordance with the embodiments described herein. For example, the filtering component  502  can filter one or more web API requests  306  based on a reputation established by a data repository  308  and/or a web API. At,  602  the filtering component  502  can perform criteria filtering to filter the one or more web API requests  306  in accordance with the embodiments described herein. For example, the filtering component  502  can filter the one or more web API requests  306  based on comparison of a subject web API request  306  with one or more specifications to determine the presence, or lack thereof, of criteria. At  606 , the filtering component  502  can perform request filtering to filter the one or more web API requests  306  in accordance with the embodiments described herein. For example, the filtering component  502  can filter the one or more web API requests  306  based on one or more parameters that may be contained in the web API requests  306 . The filtering component  502  can output filtered web API requests  608  that can be considered as having a high quality. 
     The aggregation component  314  and/or the clustering component  316  can receive one or more filtered web API requests  608 . At  610  the aggregation component  314  can determine statistics regarding the filtered web API requests  608  in accordance with various embodiments described herein. At  612  the aggregation component  314  can generate information indicative of one or more lessons learned regarding the filtered web API requests  608  in accordance with various embodiments described herein. At  614  the clustering component  316  can generate one or more clusters comprising the filtered web API requests  608  in accordance with various embodiments described herein. At  416  the clustering component  316  can designate one or more cluster representatives from the filtered web API requests  608  in accordance with various embodiments described herein. 
     In one or more embodiments, the aggregation component  314  and the clustering component  316  can communicate with each other and share outputs. Further, the outputs of the aggregation component  314  (e.g., statistics and/or lessons learned) and/or the clustering component  316  (e.g., clusters and/or cluster representatives) can be sent to the display component  318 . At  618 , the display component  318  can display a usage summary comprising one or more of the outputs. In one or more embodiments, the usage summary can comprise one or more outputs from only the aggregation component  314 . In various embodiments, the usage summary can comprise one or more outputs from only the clustering component  316 . In various embodiments, the usage summary can comprise one or more outputs from both the aggregation component  314  and the clustering component  316 . 
       FIG. 7  illustrates a block diagram of the system  300  comprising a second server  702 . Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity. The second server  702  can comprise the components described herein in regards to server  302  and can perform the features described herein in regards to server  302 . The second server  702  can be operably coupled to the server  302 , and/or the second server  702  can communicate with the server  302  via one or more networks  304 . The second server  702  can be operably coupled to one or more data repositories  308 , and/or the second server  702  can communicate with one or more data repositories  308  via one or more networks  304 . The second server  702  can directly receive one or more web API requests  306 , and/or the second server  702  can receive one or more web API requests  306  via one or more networks  304 . 
     The second server  702  and/or the server  302  can share one or more outputs identified and/or generated by a respective aggregation component  314 , clustering component  316 , and/or filtering component  502 . In one or more embodiments, the second server  702  can generate parts of a usage summary (e.g., via process  400  and/or process  600 ), while the server  302  can generate the remaining parts of the usage summary (e.g., via process  400  and/or  600 ). In other words, the system  300  can comprise a server  302  and a second server  702  that can share the workload described herein (e.g., process  400  and/or process  600 ). 
       FIG. 8  illustrates a flow chart of a computer-implemented method  800  that can facilitate generating a usage summary regarding data associated with one or more web API requests  306 . At  802 , the method  800  can comprise evaluating, by a system  300  operatively coupled to a processor  324 , data from a data repository  308 , wherein the evaluating can be based on a defined machine learning process. At  804 , the method can further comprise generating, by the system  300  (e.g., via aggregation component  314 ), a usage summary of the data, wherein the usage summary can be based on a statistic derived (e.g., via aggregation component  314 ) from a web application programming interface request  306 , and the web API request  306  can be associated with the data. For example, the generating can comprise aggregating (e.g., via aggregation component  314 ) a plurality of web API requests  306 , and the aggregating can arrange the plurality of web API requests  306  in a hierarchy based on a factor selected from a group consisting of a type of web API, a resource, an endpoint, a query, a payload, and a library specific parameter. Also, the statistic can be selected (e.g., via aggregation component  314 ) from a group consisting of an endpoint statistic for the web API request  306  and a parameter statistic for the web API request  306 . 
       FIG. 9  illustrates a flow chart of a computer-implemented method  900  that can facilitate generating a usage summary regarding data associated with one or more web API requests  306 . At  902 , the method  900  can comprise evaluating, by a system  300  operatively coupled to a processor  324 , data from a data repository  308 , wherein the evaluating can be based on a defined machine learning process. At  904 , the method  900  can further comprise generating, by the system  300  (e.g., via clustering component  316 ), a usage summary of the data, wherein the usage summary comprises a cluster of web API requests  306  from a plurality of web API requests  306 , and the cluster of web API requests  306  can be associated with the data. For example, the cluster of web API requests  306  can be formed by the system  300  (e.g., via clustering component  316 ) using a clustering algorithm o group two or more web API requests  306  from the plurality of web API requests  306 . Also, the cluster of web API requests  306  can comprise a cluster representative to represent a composition of the cluster of web API requests  306 . 
     In order to provide a context for the various aspects of the disclosed subject matter,  FIG. 10  as well as the following discussion are intended to provide a general description of a suitable environment in which the various aspects of the disclosed subject matter can be implemented.  FIG. 10  illustrates a block diagram of an example, non-limiting operating environment in which one or more embodiments described herein can be facilitated. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity. With reference to  FIG. 10 , a suitable operating environment  1000  for implementing various aspects of this disclosure can include a computer  1012 . The computer  1012  can also include a processing unit  1014 , a system memory  1016 , and a system bus  1018 . The system bus  1018  can operably couple system components including, but not limited to, the system memory  1016  to the processing unit  1014 . The processing unit  1014  can be any of various available processors. Dual microprocessors and other multiprocessor architectures also can be employed as the processing unit  1014 . The system bus  1018  can be any of several types of bus structures including the memory bus or memory controller, a peripheral bus or external bus, and/or a local bus using any variety of available bus architectures including, but not limited to, Industrial Standard Architecture (ISA), Micro-Channel Architecture (MSA), Extended ISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB), Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus (USB), Advanced Graphics Port (AGP), Firewire, and Small Computer Systems Interface (SCSI). The system memory  1016  can also include volatile memory  1020  and nonvolatile memory  1022 . The basic input/output system (BIOS), containing the basic routines to transfer information between elements within the computer  1012 , such as during start-up, can be stored in nonvolatile memory  1022 . By way of illustration, and not limitation, nonvolatile memory  1022  can include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM). Volatile memory  1020  can also include random access memory (RAM), which acts as external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM. 
     Computer  1012  can also include removable/non-removable, volatile/non-volatile computer storage media.  FIG. 10  illustrates, for example, a disk storage  1024 . Disk storage  1024  can also include, but is not limited to, devices like a magnetic disk drive, floppy disk drive, tape drive, Jaz drive, Zip drive, LS-100 drive, flash memory card, or memory stick. The disk storage  1024  also can include storage media separately or in combination with other storage media including, but not limited to, an optical disk drive such as a compact disk ROM device (CD-ROM), CD recordable drive (CD-R Drive), CD rewritable drive (CD-RW Drive) or a digital versatile disk ROM drive (DVD-ROM). To facilitate connection of the disk storage  1024  to the system bus  1018 , a removable or non-removable interface can be used, such as interface  1026 .  FIG. 10  also depicts software that can act as an intermediary between users and the basic computer resources described in the suitable operating environment  1000 . Such software can also include, for example, an operating system  1028 . Operating system  1028 , which can be stored on disk storage  1024 , acts to control and allocate resources of the computer  1012 . System applications  1030  can take advantage of the management of resources by operating system  1028  through program modules  1032  and program data  1034 , e.g., stored either in system memory  1016  or on disk storage  1024 . It is to be appreciated that this disclosure can be implemented with various operating systems or combinations of operating systems. A user enters commands or information into the computer  1012  through one or more input devices  1036 . Input devices  1036  can include, but are not limited to, a pointing device such as a mouse, trackball, stylus, touch pad, keyboard, microphone, joystick, game pad, satellite dish, scanner, TV tuner card, digital camera, digital video camera, web camera, and the like. These and other input devices can connect to the processing unit  1014  through the system bus  1018  via one or more interface ports  1038 . The one or more Interface ports  1038  can include, for example, a serial port, a parallel port, a game port, and a universal serial bus (USB). One or more output devices  1040  can use some of the same type of ports as input device  1036 . Thus, for example, a USB port can be used to provide input to computer  1012 , and to output information from computer  1012  to an output device  1040 . Output adapter  1042  can be provided to illustrate that there are some output devices  1040  like monitors, speakers, and printers, among other output devices  1040 , which require special adapters. The output adapters  1042  can include, by way of illustration and not limitation, video and sound cards that provide a means of connection between the output device  1040  and the system bus  1018 . It should be noted that other devices and/or systems of devices provide both input and output capabilities such as one or more remote computers  1044 . 
     Computer  1012  can operate in a networked environment using logical connections to one or more remote computers, such as remote computer  1044 . The remote computer  1044  can be a computer, a server, a router, a network PC, a workstation, a microprocessor based appliance, a peer device or other common network node and the like, and typically can also include many or all of the elements described relative to computer  1012 . For purposes of brevity, only a memory storage device  1046  is illustrated with remote computer  1044 . Remote computer  1044  can be logically connected to computer  1012  through a network interface  1048  and then physically connected via communication connection  1050 . Further, operation can be distributed across multiple (local and remote) systems. Network interface  1048  can encompass wire and/or wireless communication networks such as local-area networks (LAN), wide-area networks (WAN), cellular networks, etc. LAN technologies include Fiber Distributed Data Interface (FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ring and the like. WAN technologies include, but are not limited to, point-to-point links, circuit switching networks like Integrated Services Digital Networks (ISDN) and variations thereon, packet switching networks, and Digital Subscriber Lines (DSL). One or more communication connections  1050  refers to the hardware/software employed to connect the network interface  1048  to the system bus  1018 . While communication connection  1050  is shown for illustrative clarity inside computer  1012 , it can also be external to computer  1012 . The hardware/software for connection to the network interface  1048  can also include, for exemplary purposes only, internal and external technologies such as, modems including regular telephone grade modems, cable modems and DSL modems, ISDN adapters, and Ethernet cards. 
     Embodiments of the present invention can be a system, a method, an apparatus and/or a computer program product at any possible technical detail level of integration. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium can also include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network can include copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. Computer readable program instructions for carrying out operations of various aspects of the present invention can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions can execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to customize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein includes an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational acts to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams can represent a module, segment, or portion of instructions, which includes one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the Figures. For example, two blocks shown in succession can, in fact, be executed substantially concurrently, or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     While the subject matter has been described above in the general context of computer-executable instructions of a computer program product that runs on a computer and/or computers, those skilled in the art will recognize that this disclosure also can or can be implemented in combination with other program modules. Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks and/or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the inventive computer-implemented methods can be practiced with other computer system configurations, including single-processor or multiprocessor computer systems, mini-computing devices, mainframe computers, as well as computers, hand-held computing devices (e.g., PDA, phone), microprocessor-based or programmable consumer or industrial electronics, and the like. The illustrated aspects can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. However, some, if not all aspects of this disclosure can be practiced on stand-alone computers. In a distributed computing environment, program modules can be located in both local and remote memory storage devices. 
     As used in this application, the terms “component,” “system,” “platform,” “interface,” and the like, can refer to and/or can include a computer-related entity or an entity related to an operational machine with one or more specific functionalities. The entities disclosed herein can be either hardware, a combination of hardware and software, software, or software in execution. For example, a component can 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 a server and the server can be a component. One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers. In another example, respective components can execute from various computer readable media having various data structures stored thereon. The components can communicate via local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the Internet with other systems via the signal). As another example, a component can be an apparatus with specific functionality provided by mechanical parts operated by electric or electronic circuitry, which is operated by a software or firmware application executed by a processor. In such a case, the processor can be internal or external to the apparatus and can execute at least a part of the software or firmware application. As yet another example, a component can be an apparatus that provides specific functionality through electronic components without mechanical parts, wherein the electronic components can include a processor or other means to execute software or firmware that confers at least in part the functionality of the electronic components. In an aspect, a component can emulate an electronic component via a virtual machine, e.g., within a cloud computing system. 
     In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless specified otherwise, or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. Moreover, articles “a” and “an” as used in the subject specification and annexed drawings should generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. As used herein, the terms “example” and/or “exemplary” are utilized 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 an “example” and/or “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. 
     As it is employed in the subject specification, the term “processor” can refer to substantially any computing processing unit or device including, but not limited to, single-core processors; single-processors with software multithread execution capability; multi-core processors; multi-core processors with software multithread execution capability; multi-core processors with hardware multithread technology; parallel platforms; and parallel platforms with distributed shared memory. Additionally, a processor can refer to an integrated circuit, an application specific integrated circuit (ASIC), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic controller (PLC), a complex programmable logic device (CPLD), a discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. Further, processors can exploit nano-scale architectures such as, but not limited to, molecular and quantum-dot based transistors, switches and gates, in order to optimize space usage or enhance performance of user equipment. A processor can also be implemented as a combination of computing processing units. In this disclosure, terms such as “store,” “storage,” “data store,” data storage,” “database,” and substantially any other information storage component relevant to operation and functionality of a component are utilized to refer to “memory components,” entities embodied in a “memory,” or components including a memory. It is to be appreciated that memory and/or memory components described herein can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. By way of illustration, and not limitation, nonvolatile memory can include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM). Volatile memory can include RAM, which can act as external cache memory, for example. By way of illustration and not limitation, RAM is available in many forms such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM). Additionally, the disclosed memory components of systems or computer-implemented methods herein are intended to include, without being limited to including, these and any other suitable types of memory. 
     What has been described above include mere examples of systems, computer program products and computer-implemented methods. It is, of course, not possible to describe every conceivable combination of components, products and/or computer-implemented methods for purposes of describing this disclosure, but one of ordinary skill in the art can recognize that many further combinations and permutations of this disclosure are possible. Furthermore, to the extent that the terms “includes,” “has,” “possesses,” and the like are used in the detailed description, claims, appendices and drawings such terms are intended to be inclusive in a manner similar to the term “comprising” as “comprising” is interpreted when employed as a transitional word in a claim. The descriptions of the various embodiments have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.