SYSTEM AND METHOD FOR ENDPOINT - SUPPLEMENTED DISTRIBUTED FINANCIAL COMPUTING

A system and method for endpoint-supplemented distributed financial computing, comprising a plurality of financial institutions, a plurality of varied endpoints, an endpoint software distributor, and a preliminary analysis system. The system performs algorithmic analysis of financial data in two parts, first a preliminary analysis on the preliminary analysis system, and then a second portion of analysis on an endpoint running software delivered by the endpoint software distributor. The financial analysis may be performed with a single or plurality of financial institutions supplying base financial data to be analyzed, and the system may involve automated algorithms such as regularly-scheduled datamining algorithms in certain implementations.

BACKGROUND

Field of the Art

The disclosure relates to the field of cloud computing, and more particularly to the field of financial analysis computing.

Discussion of the State of the Art

It is commonplace in today's technologically advanced economy for business to offload difficult, or time-consuming, computation tasks to cloud computing services such as AMAZON WEB SERVICES™ or WINDOWS AZURE™. Such tasks may include complicated 3D modelling, the training of machine learning models including various forms of neural networks, and algorithmic analysis of large or real-time data sets, such as with financial data. It is common, as shown with firms such as QUANTCONNECT™, to offload entire tasks to a cloud platform and allow their computing to be used for a task such as backtesting an algorithm used to predict and make trades on the stock market. Backtesting comprises the use of historical stock market data to evaluate the trades and overall performance an algorithm would have made if you had utilized it during a specified time period, to gauge its success.

What is not shown however, is a system that can allow users to perform ad-hoc analyses that combines the use of their computing power, and the cloud platform provider, along with data from a possible multitude of financial sources, combining multi-tenancy, single-tenancy, and arbitration between multiple external sources of data, to provide for swift, high-quality, and easily accessible cyber-financial services.

SUMMARY

Accordingly, the inventor has conceived and reduced to practice, a system and method for endpoint-supplemented distributed financial computing, comprising a plurality of financial institutions, a plurality of varied endpoints, an endpoint software distributor, and a preliminary analysis system. The system performs algorithmic analysis of financial data in two parts, first a preliminary analysis on the preliminary analysis system, and then a second portion of analysis on an endpoint running software delivered by the endpoint software distributor. The financial analysis may be performed with a single or plurality of financial institutions supplying base financial data to be analyzed, and the system may involve automated algorithms such as regularly-scheduled datamining algorithms in certain implementations.

According to one aspect, a system for endpoint-supplemented distributed financial computing, is disclosed, comprising: at least one datastore; a network endpoint comprising at least a first plurality of programming instructions stored in the at least one memory of, and operating on at least one processor of, the computer system, wherein the first plurality of programming instructions, when operating on the at least one processor, cause the computer system to: direct a web browser to a web address; wherein the web address is the Uniform Resource Locator for an endpoint software distributor; download executable software code distributed from an endpoint software distributor; execute the executable software code downloaded from an endpoint software distributor, within a web browser; send Representational State Transfer API requests over the Internet, using Hypertext Transfer Protocol and Hypertext Transfer Protocol Secure, to an endpoint software distributor; perform final analysis on received response form endpoint software distributor, using the downloaded executable software code; an endpoint software distributor comprising at least a second plurality of programming instructions stored in the at least one memory of, and operating on at least one processor of, the computer system, wherein the second plurality of programming instructions, when operating on the at least one processor, cause the computer system to: listen on Hypertext Transfer Protocol and Hypertext Transfer Protocol Secure ports for incoming requests, such as those from web browsers; serve a website to web browsers that attempt to access the endpoint software distributor's Uniform Resource Locator; serve executable software code to browsers attempting to access the endpoint software distributor's Uniform Resource Locator; listen for Representational State Transfer API requests sent by a network endpoint over the Internet; forward requests to an internal API maintained by a preliminary analysis system; forward internal responses from the preliminary analysis system to the network endpoint; and a preliminary analysis system comprising at least a third plurality of programming instructions stored in the at least one memory of, and operating on at least one processor of, the computer system, wherein the third plurality of programming instructions, when operating on the at least one processor, cause the computer system to: listen for requests from an endpoint software distributor; access data from a datastore, for the purposes of authenticating or fulfilling received requests; authenticate requests and user identities; communicate with at least one financial institution over the Internet, for requests requiring live financial data or other data from financial institutions; perform requested analysis of data; and respond to request received from endpoint software distributor.

Further, a method for endpoint-supplemented distributed financial computing is disclosed, comprising the steps of: directing a web browser to a web address, using a network endpoint; wherein the web address is the Uniform Resource Locator for an endpoint software distributor, using a network endpoint; downloading executable software code distributed from an endpoint software distributor, using a network endpoint; executing the executable software code downloaded from an endpoint software distributor, within a web browser, using a network endpoint; sending Representational State Transfer API requests over the Internet, using Hypertext Transfer Protocol and Hypertext Transfer Protocol Secure, to an endpoint software distributor, using a network endpoint; performing final analysis on received response form endpoint software distributor, using the downloaded executable software code, using a network endpoint; listening on Hypertext Transfer Protocol and Hypertext Transfer Protocol Secure ports for incoming requests, such as those from web browsers, using a endpoint software distributor; serving a website to web browsers that attempt to access the endpoint software distributor's Uniform Resource Locator, using a endpoint software distributor; serving executable software code to browsers attempting to access the endpoint software distributor's Uniform Resource Locator, using a endpoint software distributor; listening for Representational State Transfer API requests sent by a network endpoint over the Internet, using a endpoint software distributor; forwarding requests to an internal API maintained by a preliminary analysis system, using a endpoint software distributor; forwarding internal responses from the preliminary analysis system to the network endpoint, using a endpoint software distributor; listening for requests from an endpoint software distributor, using a preliminary analysis system; accessing data from a datastore, for the purposes of authenticating or fulfilling received requests, using a preliminary analysis system; authenticating requests and user identities, using a preliminary analysis system; communicating with at least one financial institution over the Internet, for requests requiring live financial data or other data from financial institutions, using a preliminary analysis system; performing requested analysis of data, using a preliminary analysis system; and responding to request received from endpoint software distributor, using a preliminary analysis system.

DETAILED DESCRIPTION

The inventor has conceived, and reduced to practice, a system and method for endpoint-supplemented distributed financial computing.

Definitions

“Artificial intelligence” or “AI” as used herein means a computer system or component that has been programmed in such a way that it mimics some aspect or aspects of cognitive functions that humans associate with human intelligence, such as learning, problem solving, and decision-making. Examples of current AI technologies include understanding human speech, competing successfully in strategic games such as chess and Go, autonomous operation of vehicles, complex simulations, and interpretation of complex data such as images and video.

“Machine learning” as used herein is an aspect of artificial intelligence in which the computer system or component can modify its behavior or understanding without being explicitly programmed to do so. Machine learning algorithms develop models of behavior or understanding based on information fed to them as training sets, and can modify those models based on new incoming information. An example of a machine learning algorithm is AlphaGo, the first computer program to defeat a human world champion in the game of Go. AlphaGo was not explicitly programmed to play Go. It was fed millions of games of Go, and developed its own model of the game and strategies of play.

“Neural network” as used herein means a computational model, architecture, or system made up of a number of simple, highly interconnected processing elements which process information by their dynamic state response to external inputs, and is thus able to “learn” information by recognizing patterns or trends. Neural networks, also sometimes known as “artificial neural networks” are based on our understanding of the structure and functions of biological neural networks, such as the brains of mammals. A neural network is a framework for application of machine learning algorithms.

“Backtesting” as used herein refers to the practice of gathering historical financial data, and running a financial algorithm (such as an automated stock market trading algorithm) on the historical data for a set time period, and measuring the outcome of the algorithm when run over that period of time based on the actual historical data for how markets behaved in that time period. Backtesting is done by financial analysts to determine the efficacy of algorithms for a given span of time and for certain market conditions or market behaviors, often for timespans of over one year, to help determine if the algorithm is likely to perform well in the future.

“Financial institution” as used herein may refer to a bank, a stock brokerage, a broker-dealer, a market maker, a stock exchange, a credit union, an analysis firm, or any related firms, services, or people in the economy that may be commonly referred to collectively or singly as a “financial institution.”

Conceptual Architecture

FIG.1is a system diagram illustrating an exemplary system architecture for endpoint-supplemented distributed financial computing, according to a preferred aspect. A preliminary analysis system110exists as a computing device, or collection of computing devices, such as a server or multiple servers, such as those that may operate in a datacenter. It may utilize an operating system such as any of the WINDOWS™ operating systems, one of the many LINUX™ operating systems, or other operating systems. A Local Area Network (“LAN”)113connects the preliminary analysis system110to an endpoint software distributor115. An endpoint software distributor may further exist as a separate server or other computing device, or the endpoint software distributor115and preliminary analysis system110may be hosted on the same device, with no LAN between them. The endpoint software distributor115may receive communications from endpoint devices that users may use to access web services, including but not limited to a smartphone125, laptop130, tablet computer135, and other endpoint devices140such as non-mobile computing devices. Such endpoints125,130,135,140may individually access the endpoint software distributor115over the Internet120, with a web browser, at which point the distributor115will upload executable software code, such as a combination of WEBASSEMBLY™, JAVASCRIPT™, and other code, aside from or in addition to regular HTML and CSS markup for the rendering of webpages. The code will be executed in the endpoint's125,130,135,140web browser, as a web application. The preliminary analysis system may, when needed, communicate and make requests over the Internet120to at least one of a possible plurality of financial institutions145,150,155, which may be any of a combination of stock brokerages, brokerage-dealers, banks, credit unions, market makers, or other financial institutions. A preliminary analysis system110may be used to gather data for fulfilling requests by user endpoints125,130,135,140, whether on a regular schedule (such as regularly scheduled datamining algorithms) or whether data gathering is performed on a per-request basis, from a possible plurality of financial institutions145,150,155. Such information may include user account data, stock market data, data on stock derivatives, data on traded commodities, fundamental data such as company or business metrics and reports (such as those published by the United States Securities and Exchange Commission), or other financial data.

Data sent from financial institutions145,150,155to a preliminary analysis system110may be analyzed partially or fully based on user requests, or may only partially analyze data, before sending the result of the fulfilled requests back to the endpoint software distributor115over a LAN113, which then sends results through the Internet120to the endpoint that originated the API request125,130,135,140. The endpoints may then either render the results of the request, finish the partially-completed analysis, or perform some other function, using the software distributed to the endpoints by the endpoint software distributor115.

FIG.2is a system diagram illustrating components and inner workings of a preliminary analysis system and an endpoint-accessible server, according to a preferred aspect. A collection of endpoints205, may individually or together send requests through the Internet120to an endpoint software distributor115over HTTP or HTTPS protocols, for software such as a web service or web application hosted210by the endpoint software distributor120. Software is then distributed by the endpoint software distributor120, and downloaded by the endpoint(s)205. This software executes in the browser, and may be a combination of WEBASSEMBLY™ compiled or intermediate code, JAVASCRIPT™ code, HTML and CSS stylings, or other web technologies designed to deliver software to the browser for execution. As this software executes and interacts with the user or users of the endpoint(s), the endpoint(s) send requests over HTTP, HTTPS, or TCP/IP protocols to the endpoint software distributor120, to a forward-facing Application Programming Interface (“API”)220. The requests may be for user data or other user interactions within the system, or for financial analysis and data. Regardless of the nature of the requests received by the forward-facing API220, if they are deemed valid and authorized requests, they may be forwarded over a LAN113to the preliminary analysis system110for processing, via an internally accessible API230in the preliminary analysis system110. The internally accessible API230may receive the requests forwarded from the endpoint software distributor115, and use an analysis engine240to process them, either completely using already-stored data within a datastore or database250, or with the addition of financial data that may be gathered from financial institutions. The analysis may comprise filtering of financial assets according to user specifications, datamining algorithms operating on lists of assets or entities, finding entities that match certain criteria, performing normalization on large datasets for graphing or use of the dataset in machine learning algorithms, and machine learning algorithms themselves. The results of the analysis may be stored in the database250, and may be forwarded back through the internal API230as the result of the initial call made by the endpoint software distributor115, which then relays the result of the request and analysis back to the endpoint(s)205. Such endpoints may execute the software downloaded from the endpoint software distributor115to finalize or alter the analysis or results received from the preliminary analysis system110as needed, allowing for distributed and decentralized processing of financial data. The database250need not be part of the same physical system as other components in the preliminary analysis engine110, it would be sufficient for a network connection to exist between a database host and the preliminary analysis system110, which is common in the art for networked software services.

FIG.3is a system diagram illustrating components and inner workings of a preliminary analysis system with an automated algorithm engine and an endpoint-accessible server, according to another aspect. A collection of endpoints205, may individually or together send requests through the Internet120to an endpoint software distributor115over HTTP or HTTPS protocols, for software such as a web service or web application hosted210by the endpoint software distributor120. Software is then distributed by the endpoint software distributor120, and downloaded by the endpoint(s)205. This software executes in the browser, and may be a combination of WEBASSEMBLY™ compiled or intermediate code, JAVASCRIPT™ code, HTML and CSS stylings, or other web technologies designed to deliver software to the browser for execution. As this software executes and interacts with the user or users of the endpoint(s), the endpoint(s) send requests over HTTP, HTTPS, or TCP/IP protocols to the endpoint software distributor120, to a forward-facing Application Programming Interface (“API”)220. The requests may be for user data or other user interactions within the system, or for financial analysis and data. Regardless of the nature of the requests received by the forward-facing API220, if they are deemed valid and authorized requests, they may be forwarded over a LAN113to the preliminary analysis system110for processing, via an internally accessible API230in the preliminary analysis system110. The internally accessible API230may receive the requests forwarded from the endpoint software distributor115, and use an analysis engine240to process them, either completely using already-stored data within a datastore or database250, or with the addition of financial data that may be gathered from financial institutions. The analysis may comprise filtering of financial assets according to user specifications, datamining algorithms operating on lists of assets or entities, finding entities that match certain criteria, performing normalization on large datasets for graphing or use of the dataset in machine learning algorithms, and machine learning algorithms themselves. The results of the analysis may be stored in the database250, and may be forwarded back through the internal API230as the result of the initial call made by the endpoint software distributor115, which then relays the result of the request and analysis back to the endpoint(s)205. Such endpoints may execute the software downloaded from the endpoint software distributor115to finalize or alter the analysis or results received from the preliminary analysis system110as needed, allowing for distributed and decentralized processing of financial data. The database250need not be part of the same physical system as other components in the preliminary analysis engine110, it would be sufficient for a network connection to exist between a database host and the preliminary analysis system110, which is common in the art for networked software services.

Further, an automated algorithm engine310exists as part of a preliminary analysis system110, and may operate algorithms including regular datamining algorithms, volume alert algorithms and alerts, or other algorithms, on a regular schedule or with regular listening for data from financial institutions, to record the results in the database250upon algorithm execution. The data contained in the database250resulting from the automated algorithm engine310may be queried and viewed by authorized endpoints205based on their requests to the forward-facing API220, for instance if an authenticated and authorized user wished to see the latest results from an always-running datamining algorithm, the query would read the latest data from the database250and return it to the user, while the automated algorithm engine310has no direct contact with the users or endpoints205. The automated algorithm engine310does not need to be physically part of the same computing device or server as the other components in the preliminary analysis engine, and in fact may be a completely separate device, with a network connection to the database250and an Internet120connection utilized for the automated algorithms.

Detailed Description of Exemplary Aspects

FIG.4is a method diagram illustrating steps used in the operation of an exemplary system architecture for endpoint-supplemented distributed financial computing, according to a preferred aspect. A collection of endpoints, may individually or together send requests through the Internet to an endpoint software distributor over HTTP or HTTPS protocols, for software such as a web service or web application hosted by the endpoint software distributor410. An endpoint software distributor may provide browser-readable data such as a website to render in endpoint web browser420, such as how web servers commonly operate for websites on the World Wide Web. This data may be secured using HTTPS protocol and proper encryption standards, and may be dynamically generated as opposed to static, or a combination of static and dynamic content, such as a Single-Page Application (“SPA”) or a Progressive Web Application (“PWA”). An endpoint software distributor may then, based on user interactions or as a default mode of behavior, distribute code to be executed in endpoint web browser430separate from merely website markup, such as JAVASCRIPT™, compiled WEBASSEMBLY™, and other software code that may operate within a web browser. Different code may be distributed to users of different operating systems or web browsers, or may be uniform across all platforms and web browsers. From this web-browser-executable code, the endpoint or endpoints may communicate with the endpoint software distributor via Representational State Transfer or “RESTful” API calls440, sent over one or multiple of a variety of protocols that may include HTTP, HTTPS, TCP/IP, UDP, or other protocols. The endpoint software distributor may then parse the received requests from the endpoint or endpoints, to determine their validity, safety, and/or proper encryption, before forwarding such valid requests to a preliminary analysis system450. Such forwarding may occur over a Local Area Network (LAN), or may occur over a computer bus if the preliminary analysis system and endpoint software distributor are built as a single computing device rather than separate devices. Such forwarding may be encrypted between the distributor and analysis system. A preliminary analysis system may then, based on the received request that was forwarded from the endpoint software distributor, fetch any required data from at least one financial institution, over the Internet460, for the fulfillment of the endpoint request. Data may also be gathered from a connected or local database, whether for endpoint or user authorization and verification, or for processing the received request. A preliminary analysis system may then perform preliminary data processing and analysis, such as calculating stock option Greek values, calculating 3-dimensional graphs of stock option chain data, running machine learning algorithms, or other possible data analyses470. Such analysis need may not be complete, and may comprise only a basic-level or partial analysis, such as filtering out certain financial assets or compiling financial data on a business or class of asset, before referring the results of the preliminary analysis back to the endpoint software distributor480. Such an endpoint software distributor may then respond to the initial API request from the endpoint or endpoints with the data of the response, at which point the endpoint may, if needed, complete the analysis or perform additional analyses on the received data, using the distributed endpoint software operating in the web browser490.

FIG.5is a method diagram illustrating steps used in the operation of a preliminary analysis system and an endpoint-accessible server, according to a preferred aspect. A collection of endpoints, may individually or together send requests through the Internet to an endpoint software distributor over HTTP or HTTPS protocols, for software such as a web service or web application hosted by the endpoint software distributor510. Software is then distributed by the endpoint software distributor, and downloaded by the endpoint(s)520. This software executes in the browser530, and may be a combination of WEBASSEMBLY™ compiled or intermediate code, JAVASCRIPT™ code, HTML and CSS stylings, or other web technologies designed to deliver software to the browser for execution. As this software executes and interacts with the user or users of the endpoint(s), the endpoint(s) send requests over HTTP, HTTPS, or TCP/IP protocols to the endpoint software distributor, to a forward-facing Application Programming Interface (“API”)540. The requests may be for user data or other user interactions within the system, or for financial analysis and data. Regardless of the nature of the requests received by the forward-facing API, if they are deemed valid and authorized requests550such as with a database or datastore containing user information that may be checked against to verify users, they may be forwarded over a LAN to the preliminary analysis system for processing, via an internally accessible API in the preliminary analysis system550. The internally accessible API may receive the requests forwarded from the endpoint software distributor, and use an analysis engine to process them, either completely using already-stored data within a datastore or database, or with the addition of financial data that may be gathered from financial institutions, or some combination of both560. The analysis may comprise filtering of financial assets according to user specifications, datamining algorithms operating on lists of assets or entities, finding entities that match certain criteria, performing normalization on large datasets for graphing or use of the dataset in machine learning algorithms, and machine learning algorithms themselves. The results of the analysis may be stored in the database, and may be forwarded back through the internal API as the result of the initial call made by the endpoint software distributor, which then relays the result of the request and analysis back to the endpoint(s)570,580. Such endpoints may execute the software downloaded from the endpoint software distributor to finalize or alter the analysis or results received from the preliminary analysis system as needed, allowing for distributed and decentralized processing of financial data590. The database need not be part of the same physical system as other components in the preliminary analysis engine, it would be sufficient for a network connection to exist between a database host and the preliminary analysis system, which is common in the art for networked software services.

FIG.6is a method diagram illustrating steps used in the operation of a preliminary analysis system with an automated algorithm engine and an endpoint-accessible server, according to another aspect. A collection of endpoints, may individually or together send requests through the Internet to an endpoint software distributor over HTTP or HTTPS protocols, for software such as a web service or web application hosted by the endpoint software distributor. Software is then distributed by the endpoint software distributor, and downloaded by the endpoint(s)620. This software executes in the browser, and may be a combination of WEBASSEMBLY™ compiled or intermediate code, JAVASCRIPT™ code, HTML and CSS stylings, or other web technologies designed to deliver software to the browser for execution. As this software executes and interacts with the user or users of the endpoint(s), the endpoint(s) send requests over HTTP, HTTPS, or TCP/IP protocols to the endpoint software distributor, to a forward-facing Application Programming Interface (“API”)630. The requests may be for user data or other user interactions within the system, or for financial analysis and data. Regardless of the nature of the requests received by the forward-facing API, if they are deemed valid and authorized requests, they may be forwarded over a LAN to the preliminary analysis system for processing, via an internally accessible API in the preliminary analysis system640. In this use case, the requests may involve data that has been processed or gathered from automated algorithms, either fully or in part. The internally accessible API may receive the requests forwarded from the endpoint software distributor, and use an analysis engine to process them, either completely using already-stored data within a datastore or database, or with the addition of financial data that may be gathered from financial institutions, or some combination of both. The analysis may comprise filtering of financial assets according to user specifications, datamining algorithms operating on lists of assets or entities, finding entities that match certain criteria, performing normalization on large datasets for graphing or use of the dataset in machine learning algorithms, and machine learning algorithms themselves. The results of the analysis may be stored in the database, and may be forwarded back through the internal API as the result of the initial call made by the endpoint software distributor, which then relays the result of the request and analysis back to the endpoint(s). Such endpoints may execute the software downloaded from the endpoint software distributor to finalize or alter the analysis or results received from the preliminary analysis system as needed, allowing for distributed and decentralized processing of financial data. The database need not be part of the same physical system as other components in the preliminary analysis engine, it would be sufficient for a network connection to exist between a database host and the preliminary analysis system, which is common in the art for networked software services.

Further, an automated algorithm engine exists as part of a preliminary analysis system, and may operate algorithms including regular datamining algorithms, volume alert algorithms and alerts, or other algorithms, on a regular schedule or with regular listening for data from financial institutions, to record the results in the database upon algorithm execution610. The data contained in the database resulting from the automated algorithm engine may be queried and viewed by authorized endpoints based on their requests to the forward-facing API650, for instance if an authenticated and authorized user wished to see the latest results from an always-running datamining algorithm, the query would read the latest data from the database and return it to the user, while the automated algorithm engine has no direct contact with the users or endpoints. The automated algorithm engine does not need to be physically part of the same computing device or server as the other components in the preliminary analysis engine, and in fact may be a completely separate device, with a network connection to the database and an Internet connection utilized for the automated algorithms.

Hardware Architecture

In one aspect, computing device10includes one or more central processing units (CPU)12, one or more interfaces15, and one or more busses14(such as a peripheral component interconnect (PCI) bus). When acting under the control of appropriate software or firmware, CPU12may be responsible for implementing specific functions associated with the functions of a specifically configured computing device or machine. For example, in at least one aspect, a computing device10may be configured or designed to function as a server system utilizing CPU12, local memory11and/or remote memory16, and interface(s)15. In at least one aspect, CPU12may be caused to perform one or more of the different types of functions and/or operations under the control of software modules or components, which for example, may include an operating system and any appropriate applications software, drivers, and the like. CPU12may include one or more processors13such as, for example, a processor from one of the Intel, ARM, Qualcomm, and AMD families of microprocessors. In some aspects, processors13may include specially designed hardware such as application-specific integrated circuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), field-programmable gate arrays (FPGAs), and so forth, for controlling operations of computing device10. In a particular aspect, a local memory11(such as non-volatile random access memory (RAM) and/or read-only memory (ROM), including for example one or more levels of cached memory) may also form part of CPU12. However, there are many different ways in which memory may be coupled to system10. Memory11may be used for a variety of purposes such as, for example, caching and/or storing data, programming instructions, and the like. It should be further appreciated that CPU12may be one of a variety of system-on-a-chip (SOC) type hardware that may include additional hardware such as memory or graphics processing chips, such as a QUALCOMM SNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly common in the art, such as for use in mobile devices or integrated devices.

In addition, in some aspects, servers32may call external services37when needed to obtain additional information, or to refer to additional data concerning a particular call. Communications with external services37may take place, for example, via one or more networks31. In various aspects, external services37may comprise web-enabled services or functionality related to or installed on the hardware device itself. For example, in one aspect where client applications24are implemented on a smartphone or other electronic device, client applications24may obtain information stored in a server system32in the cloud or on an external service37deployed on one or more of a particular enterprise's or user's premises. In addition to local storage on servers32, remote storage38may be accessible through the network(s)31.