Patent Publication Number: US-10776398-B2

Title: Platform data lifecycle management

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims priority to U.S. Provisional Patent Application Ser. No. 62/206,078 filed on Aug. 17, 2015, titled “Super-Platform,” which is hereby incorporated by reference in the entirety. 
    
    
     BACKGROUND 
     With the rapid growth of ubiquitous computing and the Internet of Things (IoT), users are interacting with many different computing systems in the course of their lives. In addition to interacting with devices whose primary function is computing, such as home computers, mobile computing devices, server computers, and so forth, users may also interact with devices that include computers as components, such as smart appliances, automotive computers, or any number of IoT devices. The various computing devices may generate data regarding users, and such data may be generated and delivered using different formats, models, or communications channels. 
     SUMMARY 
     Implementations of the present disclosure are generally directed to managing data during various stages of a data lifecycle. More specifically, implementations are directed to receiving and aggregating, at a super-platform, data generated by multiple individual platforms of various types, and applying restrictions to the data, aggregate data, analysis results, and/or other information through the various stages of a data lifecycle managed by the super-platform. 
     In general, innovative aspects of the subject matter described in this specification can be embodied in methods that includes actions of: receiving restrictions governing the handling of data during multiple stages of a data lifecycle, wherein the data is generated at a plurality of individual platforms associated with a super-platform; and applying a respective set of the restrictions to data handling actions performed during each of at least two different stages of the data lifecycle. 
     Implementations can optionally include one or more of the following features: the multiple stages include a first stage in which the data handling actions include one or more of generation of the data at the plurality of individual platforms, or collection of the data at the super-platform; the multiple stages include a second stage in which the data handling actions include storage of the data; the multiple stages include a third stage in which data handling actions include one or more of sale of the data to at least one data consumer, distribution of the data to the at least one data consumer, aggregation of the data, or analysis of the data; the multiple stages include a fourth stage in which data handling actions include one or more of archiving the data, or deletion of the data; the restrictions are provided by one or more of at least one end-user of the plurality of individual platforms; at least one of the plurality of individual platforms, or the super-platform; and/or the plurality of individual platforms include at least two of an e-commerce platform configured to provide an e-commerce service, a retail platform configured to support retail operations of a physical store, a media platform configured to provide an online media service, a mobile computing device, an Internet of Things (IoT) computing device, or an in-vehicle computer. 
     Other implementations of any of the above aspects include corresponding systems, apparatus, and computer programs that are configured to perform the actions of the methods, encoded on computer storage devices. The present disclosure also provides a computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations in accordance with implementations of the methods provided herein. The present disclosure further provides a system for implementing the methods provided herein. The system includes one or more processors, and a computer-readable storage medium coupled to the one or more processors having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations in accordance with implementations of the methods provided herein. 
     Implementations provide the following advantages. By applying a particular set of restrictions to data handling actions performed in each of multiple stages of a data lifecycle, implementations provide a technical advantage over traditional systems that may apply restrictions less discriminately. For example, a traditional system that redundantly applies the same restrictions during each stage of a data lifecycle may consume more processing power, memory, storage, network bandwidth, and/or other computing resources compared to a system that selectively applies different sets of restrictions during different stages according to the implementations described herein. Implementations provide more granular control over automation at different stages of the lifecycle, compared to traditional systems. For example, when examining data storage, a different set of ethical questions may be asked at each stage. If data is stored in a global cloud, the super-platform may confirm (e.g., for a user) the geography and/or export controls of the data source. A determination may be made whether the data may be reassembled for analysis that allows storage to stay within a country of origin. A determination may be made whether distributed analysis techniques are to be provided, depending on the country of origin, and/or whether insights may still be retrieved. Data deletion may also have its own particular requirements, such as an expiry period with automatic deletion, notification to a party, secure wipes, and so forth. Other ethical questions may also be applicable and/or answerable at the various stages of the lifecycle, as described further below. 
     It is appreciated that aspects and features in accordance with the present disclosure can include any combination of the aspects and features described herein. That is, aspects and features in accordance with the present disclosure are not limited to the combinations of aspects and features specifically described herein, but also include any combination of the aspects and features provided. 
     The details of one or more implementations of the present disclosure are set forth in the accompanying drawings and the description below. Other features and advantages of the present disclosure will be apparent from the description and drawings, and from the claims. 
    
    
     
       DESCRIPTION OF DRAWINGS 
         FIG. 1  depicts an example system for collecting data from multiple data generating platforms and processing the data in multiple stages of a data lifecycle, in accordance with implementations of the present disclosure. 
         FIG. 2  depicts a schematic of an example data lifecycle including multiple stages, in accordance with implementations of the present disclosure. 
         FIG. 3  depicts an example process for managing data handling in multiple stages of a data lifecycle, in accordance with implementations of the present disclosure. 
         FIG. 4  depicts an example computing system architecture, in accordance with implementations of the present disclosure. 
     
    
    
     Like reference symbols in the various drawings indicate like elements. 
     DETAILED DESCRIPTION 
     Implementations of the present disclosure include systems, devices, methods, and computer-readable media for receiving data generated by multiple platforms of different types, and managing the data in multiple stages of a data lifecycle associated with a super-platform. An end-user may interact with multiple individual (e.g., siloed) platforms of different types or that support different business purposes or industries. The individual platforms may generate data describing, and/or resulting from, these interactions with end-user(s). The data from the various individual platforms may be received, ingested, stored, analyzed, aggregated, and/or otherwise processed by a super-platform during various stages of a data lifecycle. A data consumer may interact with a marketplace that is a component of, or in communication with, the super-platform. In some examples, the data consumer may request one or more data set(s), and the marketplace may assemble and provide the data set(s) in response to the request. The super-platform may support a data lifecycle including multiple stages for handling the data. In some implementations, the end-user, the super-platform, and/or the individual platform(s) may provide one or more restrictions on how the data may be handled in each of the stages of the data lifecycle. 
     As used herein, super-platform refers to a computing system that includes any number of computing devices executing any number of software modules to receive, analyze, aggregate, store, distribute, and/or otherwise process data generated by any number of individual platforms. In some instances, the super-platform may be separate from the individual platform(s) and configured to communicate with the platform(s) using one or more networks. In some instances, the super-platform may share computing hardware and/or software resources with at least one of the individual platforms that is generating data. The super-platform may be configured to process data generated by multiple individual platforms that provide different types of services, as described further below. 
     In some implementations, various types of entities (e.g., individuals, computing devices, processes, etc.) may interact with the super-platform. Such entities may include data disclosers, data manipulators, and data consumers. A data discloser may be any entity that generates and/or provides the data to the super-platform. A data discloser may also be any entity that causes the data to be generated and/or provided to the super-platform. For example, an end-user of individual platform(s) may be a data discloser. An individual platform may also, itself, be a data discloser. A data manipulator may be any entity that analyzes, aggregates, and/or otherwise processes the data. For example, the super-platform and/or the various processes executing on the super-platform may be described as data manipulators. In some instances, the individual platforms and/or end-users may be data manipulators as well as possibly data disclosers, e.g., in scenarios where the data discloser manipulates the data in some way prior to disclosing the data to the super-platform. A data consumer may be any entity that receives the data and/or any information that is generated through manipulation of the data (e.g., aggregate data, analysis results, etc.). A data consumer may purchase or otherwise acquire information from the super-platform. In some instances, a data consumer may also be a data manipulator and/or data discloser. For example, a data consumer may purchase a set of data from the super-platform, analyze (e.g., manipulate) the data to produce analysis result(s) and/or other data set(s), and sell (e.g., disclose) the analysis result(s) and/or other data set(s) to the super-platform, individual platform(s), and/or other entities. Accordingly, a particular entity may operate in accordance with one or more of the roles of data discloser, data manipulator, and/or data consumer depending on the particular circumstances. 
       FIG. 1  depicts an example system for collecting data from multiple data generating platforms, in accordance with implementations of the present disclosure. An end-user  116  may interact with multiple, different individual platforms  102 . Each of the individual (e.g., siloed) platforms  102  may generate data  104  describing the end-user&#39;s interactions with the platform  102 . In some implementations, the individual platforms  102  may provide different types of services, and/or may be different types of platforms that support businesses or other organizations in different types of industries. The platforms  102  may include, but are not limited to, one or more of the following: 
     A computing device, which may be a mobile or portable computing device such as a smartphone, tablet computer, notebook computer, personal data assistant (PDA), wearable computer (e.g., watch device or glasses device), and so forth; 
     An in-vehicle (e.g., automotive) computer that is a component of a commercial or personal-use vehicle or otherwise in communication with a vehicle, the in-vehicle computer configured to collect telematics data, data regarding the operations (e.g., speed, braking, gas mileage, etc.) of the vehicle, characteristics of the vehicle (e.g., temperature, fuel status, fluid status, etc.), location information, environment information (e.g., temperature, air pressure, etc.), and so forth; 
     Online services for streaming or otherwise delivering content over a network (e.g., the internet), such as video content, audio content (e.g., music), audio-visual content (e.g., films, television, etc.), games, graphics, images, or other media; 
     Online shopping or other e-commerce services that offer products or services through a web site, a native application, or other type of interface; 
     Computing systems, such as point-of-sale terminals, servers, data storage devices, and so forth that are associated with physical (e.g., brick-and-mortar) stores or other commercial enterprises, the computing systems collecting or otherwise generating data based on in-store customer activities; 
     Internet-of-things (IoT) computing devices, such as smart appliances, home entertainment devices, or other devices present in a home, business setting, or elsewhere, and configured to include processing, memory, and communications capabilities in a device used for other purposes, such as smart washing machines, heating and air conditioning systems, audio or video presentation systems, and so forth; or 
     An analytics cloud or other distributed computing system, such as a social media analytics cloud (SMAC). 
     A platform  102  may include a technical (e.g., hardware and/or software) architecture, governance rules, and one or more technology services that support the creation and execution of applications. Such applications may be specific to a particular industry associated with the platform  102 . For example, platforms  102  that are vehicle computing systems, brick-and-mortar retail support systems, health care provider systems, or health insurance portals may provide and support applications associated with vehicle operations, retail operations, health care operations, or health insurance operations respectively. A platform  102  may provide a blueprint for how a business or other organization may build, connect, and deliver applications specific to problems in their associated industry. A platform  102  may serve as a pool of reusable functionality and capabilities to make building and evolving applications fast and easy, and may help companies or other organizations achieve better business outcomes. A platform  102  may also serve as a business model strategy that creates competitive differentiation. In a platform-based business, others outside a company may create value for the enterprise and, in some examples, enable new digital models for the company. 
     A platform  102  may include any number of applications (e.g., apps), and service layers or plug-in modules that support one or more features. Example features include financial transactions, authentication, logon, navigation, search, advertisement, marketplaces, or any other appropriate feature. An individual platform  102  may include hardware, software, or both hardware and software components. For example, a platform  102  that is an in-vehicle (e.g., telematics) or other type of computing system may include hardware components such as processor(s), memory, network interfaces, and so forth, as well as software components such as an operating system, firmware, or any number of applications or processes executing on the hardware component(s). As another example, a platform  102  that is an e-commerce or media streaming service may include any number of web servers, application servers, applications, or other software component(s) that execute on one or more computing devices to enable an end-user  116  to view content or interact with the platform  102  through a user interface (UI). 
     The individual platforms  102  may each generate data  104  that is associated with end-user(s)  116 , such as data describing user identity, user characteristics, user activities, user purchases, content browsed by the user, and so forth. In some examples, the data  104  may be provided to the platform(s)  102  by the end-user  116 , or may be otherwise generated based on the end-user&#39;s interactions with the platform  102 . The data  104  may include personally identifiable information (PII) of the end-user  116 , such as user name(s), address(es), telephone number(s), login information (e.g., credentials) to access the individual platform  102 , or other information that may enable identification of the particular end-user  116  or that is particularly associated with the end-user  116 . The data  104  may also include other types of information that are associated with the particular end-user  116 , but not necessarily explicitly provided by the end-user  116 , such as information describing inferred or measured user characteristics (e.g., demographic characteristics), user activities on the platform  102 , user purchase history, browsing data, clickstream data, and so forth. In some examples, the collected data  104  may include data that is generated by an individual platform  102  but that is not specific to a particular end-user  116 . For example, an individual platform  102  that is a vehicle or in-vehicle telematics device may provide data  104  that is collected by sensors of the vehicle, such as data describing temperature, air pressure, location, velocity, vehicle operational state, or other information. As another example, a platform  102  that is a computing device may generate and provide data  104  describing the location of the device, used and available memory capacity, used and available processing capacity, used or available storage capacity, installed or executing applications, hardware configuration, software configuration, and/or other information that is not specifically associated with an end-user. 
     The data  104  generated by the platform(s)  102  may include any number of data objects. As used herein, a data object may include any size or amount of data that is arranged according to any format, or unformatted. A data object may include data of any type, and may include data that is compressed or uncompressed, encrypted or unencrypted. In some examples, a data object may include a particular amount of data and may be arranged according to a format such that the data object has a particular size when communicated. Data object(s) may also include a stream of data that continues for any duration to communicate any amount of data until the end of the stream. Data object(s) may be communicated in a sequence corresponding to an order of the data included in the data object(s). The data object(s) may also be communicated out of sequence, and may include sequence indicators or order indicators that enable a receiving process to reorder the received data object(s) into the order in which they were generated. 
     The data  104  generated by the individual platforms  102  may be sent to a (e.g., centralized) super-platform  108 . For each individual platform  102 , the super-platform  108  may include a gateway  110  that operates as an interface to receive the data  104  from the individual platform  102 . The super-platform  108  may store the data  104 , and may perform operations on the data  104  to process, store, analyze, aggregate, or otherwise handle the data  104  during various stages of the data lifecycle. The super-platform  108  may include any number of software modules executing on any number of computing devices. Such software module(s) may include a marketplace  112 , the gateway(s)  110 , a restriction enforcement module  126 , module(s) for managing data storage  120 , analysis module(s)  122 , and/or an end-user interface  114 . In some examples, the super-platform  108  may be implemented as a cluster or other group of server computers configured to execute the software module(s) that provide and support a super-platform service to ingest data  104 , analyze, store, aggregate, or otherwise process the data  104 , and distribute (e.g., sell) the data  104  through the marketplace  112  to data consumer(s)  124 . 
     In some implementations, the data  104  received from the platform(s)  102  may be stored in data storage  120 . The data storage  120  may be incorporated into the super-platform  108 , as shown in  FIG. 1 . In some instances, the data storage  120  may be external to the super-platform  108  and accessible by the analysis module(s)  122 , marketplace  112 , and/or other software process(es) executing on the super-platform  108 . Implementations support the use of any storage format for the data storage  120 , including relational and/or non-relational data storage formats. In some instances, the data  104  may be stored in a compressed and/or encrypted form. The data storage  120  may be secured against access by unauthorized users, processes, or other entities. 
     In some implementations, the super-platform  108  may execute one or more analysis module(s)  122  that perform various types of processing of the data  104 . For example, the analysis module(s)  122  may include module(s) that aggregate the data  104  to generate aggregate data. Data aggregation may aggregate data  104  that is associated with a single end-user  116  of one or more platforms  102 . Data aggregation may also aggregate data  104  from multiple end-users. For example, data aggregation may generate data sets for particular segments of end-users, such as individuals having particular demographic characteristics, location, demonstrated interest in products or services, purchase history, and so forth. Aggregate data may include, or be generated based on, data that describes or identifies users, such as PII for one or more users. Aggregate data may also include, or be generated based on, data that is not associated with users, such as sensor data. The super-platform  108  may aggregate data  104  received from different platforms  102  that support different types of operations or that are associated with different industries. For example, the super-platform  108  may aggregate data  104  from an end-user&#39;s smartphone, from a brick-and-mortar retail business where the end-user shops, from the end-user&#39;s in-vehicle telematics device, and/or from a streaming video service used by the end-user. 
     The analysis module(s)  122  may also perform other types of analysis regarding the data  104 , including but not limited to combination, correlation, mathematical and/or statistical analysis, analysis to identify trends and/or patterns in the data  104 , analysis that employs machine learning techniques, semantic and/or natural language based analysis of text data, image and/or audio data analysis, or other types of processing. In some instances, the analysis module(s)  122  may also analyze the data  104 , e.g., execute analysis job(s), according to analysis request(s) received from data consumer(s)  124 , as described further below. 
     In some implementations, data  104  may be tagged or otherwise associated with metadata that describes various aspects of the data  104 . For example, a temperature value in the data  104  may be associated with metadata identifying that particular portion of data  104  as a temperature value, or the metadata may indicate the unit of measure of that portion of data  104 . In some examples, metadata may describe the source of the data  104 , such as the individual platform  102  that generated the data  104 , the particular end-user whose interactions with the individual platform  102  caused the generation of the data  104 , and so forth. The metadata describing the source platform  102  may enable the super-platform  108  to perform various (e.g., unique or targeted) functions with regard to the data  104 . For example, metadata identifying the source platform  102  may enable the data  104  to be retracted from use, or recalled from distribution, if it is later determined that the data  104  from the platform  102  is inaccurate in some way. Metadata may also identify a particular hardware or software component of the platform  102  that generated the data  104 . For example, after ingestion of data  104  the individual platform  102  that supplied the data  104  may determine that a particular sensor that generated the data  104  is faulty, and the platform  102  may apply a restriction  118  indicating that the super-platform  108  prevent further use of data  104  from that particular sensor. As another example, the super-platform  108  may perform operations to remove the faulty data  104  from aggregate data, statistically correct for the inclusion of faulty data  104 , or other operations, such as to offset a calibration abnormality of the sensor. 
     For a particular portion of data  104 , the super-platform  108  may determine as much metadata as possible to describe the data  104 . In this way, the super-platform  108  may be described as a metadata-rich environment that enables the data  104  to be interrogated or otherwise analysis in various ways, even in ways not anticipated when the data  104  was generated or received at the super-platform  108 . Metadata may indicate a source of the data  104 , such as one or both of the end-user or individual platform  102  that generated the data  104 . Metadata may also indicate an owner of the data  104  at various stages in the data lifecycle of generation. The data lifecycle may include: ingestion by the super-platform  108 ; processing (e.g., analysis, aggregation, etc.); storage; distribution to data consumer(s); and deletion or retirement of the data  104 . In some implementations, the metadata  106  is formatted according to a Resource Description Framework (RDF) or any other metadata format. 
     In some implementations, an individual platform  102  may provide metadata with the data  104  that is ingested into the super-platform  108 . In examples where an individual platform  102  does not provide metadata, or provides incomplete metadata, the super-platform  108  may generate metadata for the ingested data  104 . This generation of metadata may be through a natural language (NL) or semantic analysis of the ingested data  104 . In some examples, the super-platform  108  may generate metadata such as location tags or time tags for the ingested data  104 , e.g., based on other information indicating a location of the platform  102  or a time when the metadata was generated or received. In some examples, the super-platform  108  may infer or otherwise determine metadata for a portion of data  104  based on a semantic or format-based analysis of the data  104  to match the data  104  with one or more known patterns or formats of data. For example, if the data matches a known pattern or format for (e.g., “looks like”) a value for air pressure, the data  104  may be tagged as air pressure data. The super-platform  108  may also add metadata based on context information that is known about the individual platform  102  or the end-user. 
     In some examples, the super-platform  108  may also flag data  104  that the super-platform  108  is unable to reliably tag (e.g., determine metadata) automatically with an adequate degree of confidence in the tagging. The flagging of data  104  that cannot be tagged may lead to a manual review and manual determination of metadata for the data  104 . In some examples, metadata tagging may be performed at a higher level or with less detail describing the data  104 . For example, it may be sufficient to know that a particular portion of data  104  is describing a movie, without knowing which particular aspect (e.g., title, genre, plot synopsis, cast, etc.) is being described by the data  104 . 
     In some implementations, data analysis by the analysis module(s)  122  includes the normalization of data  104  received from different individual platforms  102 . Such normalization may impose a common format for the metadata, or for the data  104  itself, in situations where different individual platforms  102  provide data  104  or metadata in different formats, styles, data types, and so forth. Normalization may also include determination that different sets of data  104  from different individual platforms  102  are associated with the same end-user, in examples where the different individual platforms  102  employ a different user identifier for the end-user. In such examples, normalization may generate or alter metadata to use a same user identifier to identify the same end-user. Normalization may normalize data  104  received from different countries or other geographical regions, to normalize the data  104  or metadata to employ a same system of measurement, language, or other format. 
     In some implementations, analysis module(s)  122  may determine and/or employ a semantic data model that describes the ingested data  104  for a single platform  102  or for multiple platforms  102 . The semantic data model may sit on top of the other types of metadata and provide a framework or set of relationships that describes multiple portions of data  104  having different metadata. For example, a semantic data model may describe a movie and may describe relationships between differently metadata-tagged portions of data, such as a relationship between movie title, description, cast, studio, format, length, genre, and so forth. The semantic data model may be described as a structure or map of the data  104 , and may be determined based on the metadata. In some examples, the semantic data model may be hierarchical. For example, data  104  describing a refinery may include multiple gauges, valves, or other components, and those components may have sub-components, which may themselves have sub-components, and so forth. 
     The super-platform  108  may be configured to dynamically adapt to the addition of new individual platforms  102  by determining how the data  104  provided by a new individual platform  102  fits into an existing semantic data model that describes previously ingested data  104  from other platform(s)  102 . Capabilities of the newly added individual platform  102  may be determined by analogizing or otherwise comparing to the new platform  102  to existing, similar individual platforms. For example, a new platform  102  may be associated with the semantic data model of a currently supported platform  102  that is similar to the new platform  102  with regard to type of platform, hardware or software configuration, type of service provided by the platform, format of the data, mechanism(s) for accessing and/or receiving the data from the platform, or other characteristics of the platforms. In some implementations, machine learning may be employed to determine the semantic data model and/or other capabilities of a newly available platform  102 . Machine learning algorithm(s) may examine data sets that are sufficiently described by metadata  106  and, using the metadata  106 , may apply a semantic data model to a new data set from a new platform  102  that might extend, augment, improve, or stand alongside an existing data set from current platform(s)  102 . The determination of metadata and/or models for data  104  may be performed by the analysis module(s)  122 . 
     The super-platform  108  may also execute one or more software modules to provide a marketplace  112  that enables potential data consumer(s)  124  to request various data set(s) of the collected data  104 , set(s) of aggregate data, result(s) of data analysis job(s), and/or other information. The marketplace  112  may provide such information in exchange for a fee paid by the data consumer(s)  124 . In some instances, the marketplace  112  may distribute information without charging a fee. 
     The end-user  116  may access an end-user interface  114  provided by the super-platform  108 . In some instances, the end-user  116  may employ the end-user interface  114  to specify pricing information indicating a price for one or more portions of data  104  associated with the end-user  116 . Through the end-user interface  114 , the end-user  116  may specify one or more restrictions  118  on the handling of the data  104  associated with the end-user  116 . The restriction(s)  118  may also be specified by one or more individual platform(s)  102  and/or the super-platform  108  itself. In some implementations, the end-user(s)  116 , platform(s)  102 , and/or super-platform  108  may specify restriction(s)  118  that govern the handling of the data  104  during one or more stages of a multi-stage data lifecycle. In some implementations, a restriction enforcement module  126  (e.g., an agent process) may execute on the super-platform  108  to enforce restriction(s)  118  during one or more stages of the data lifecycle, as described further with reference to  FIG. 2 . 
     To account for the variety of restrictions on data use, the data and/or its permissions for use may be stored in an immutable block chain and/or other immutable data storage. This allows the data to retain its complete provenance from initial creation through each the data takes along its journey. The stored provenance information may indicate the reasons for the data sharing and use among various systems. By storing data and/or restrictions based on disclosure (e.g., human or machine) preferences in this way, implementations provide auditing as a feature of this system. Using a block chain also allows data owners to dynamically change permissions on existing data in the system. While the use of data in this block chain can be openly audited, the data assets themselves may not be retrievable outside of the super-platform without a client-side daemon executing on the data consumer&#39;s computing device (e.g., analytics appliance). The daemon may specify the conditions under which the data is to be used, download the data assets from the super-platform, decrypt the data assets, and/or append the metadata for each data asset on the block chain with the specific use. To audit for use compliance of specific data assets, a query of the block chain may return this information. Data disclosers (e.g., those who publish data to the system) may be able to modify their own data assets, but others may not be able to make such modifications. 
       FIG. 2  depicts a schematic of an example data lifecycle  200  including multiple stages  202 , in accordance with implementations of the present disclosure. Although  FIG. 2  depicts an example data lifecycle  200  that includes four stages  202 , implementations support any number of stages  202  in the data lifecycle  200 , with any number of action(s)  204  performed during each stage  202 . As used herein, handling of the data  104  may include any action(s) performed with regard to the data  104 . In some examples, the data  104  may pass through four different stages  202  of the data lifecycle  200 , including: 
     Stage  202 ( 1 ) in which action(s)  204 ( 1 ) are performed, including the generation or creation of the data  104  by the platform(s)  102 , and the collecting or receiving of the data  104  from the individual platforms  102  through the gateway(s)  110 ; 
     Stage  202 ( 2 ) in which action(s)  204 ( 2 ) are performed, including the storing of the data  104 , and/or metadata, in the data storage  120 ; 
     Stage  202 ( 3 ) in which action(s)  204 ( 3 ) are performed, including various uses of the data  104  such as processing, aggregating, data modeling, determining metadata for the data  104 , and/or other analysis of the data  104  by the analysis module(s), the storage of aggregate data and/or analysis results (e.g., in the data storage  120 ), the sale or other distribution of the data  104 , aggregate data, analysis results, or other information to data consumer(s)  124  through the marketplace  112 ; and 
     Stage  202 ( 4 ) in which action(s)  204 ( 4 ) are performed, including the archiving, deletion, and/or disposal of the data  104 , aggregate data, analysis results, and/or other information, such that the information is no longer available for use, sale, or other types of handling. 
     The restriction(s)  118 ( 1 ) on the action(s)  204 ( 1 ) performed during the stage  202 ( 1 ) may include restriction(s) on which particular data  104  may (or may not) be collected by the gateway(s)  110  of the super-platform  108 . The restriction(s)  118 ( 1 ) may also indicate types of data that may (or may not) be collected, such as PII. 
     The restriction(s)  118 ( 2 ) on the action(s)  204 ( 2 ) performed during the stage  202 ( 2 ) may include restriction(s) on how and/or where the data  104  is stored. For example, restriction(s)  118 ( 2 ) may indicate that the data  104  is to be stored in an encrypted or otherwise secure form to prevent unauthorized access. The restriction(s)  118 ( 2 ) may also specify requirements for the security of the data storage  120  where the data  104  is stored. 
     The restriction(s)  118 ( 3 ) on the action(s)  204 ( 3 ) performed during the stage  202 ( 3 ) may include restriction(s) on which data consumer(s)  124  are to be given access to the data  104 , aggregate data  104  based on the data  104 , analysis result(s) based on the data  104 , or other information. Such restriction(s) may include a white list, or black list, of particular data consumer(s) to be given access or barred from access. The restriction(s) may also include a white list, or black list, of types, classes, or categories of data consumer(s). The restriction(s) may restrict particular uses, or types of uses, of the data  104 . The restriction(s) may also govern other types of action(s)  204 ( 3 ) performed during the stage  202 ( 3 ). 
     The restriction(s)  118 ( 4 ) on the action(s)  204 ( 4 ) performed during the stage  202 ( 4 ) may include restriction(s) on how, when, and/or whether the data  104  is retired, archived, deleted, destroyed, and/or otherwise made unavailable for further processing. 
     In some examples, the various stages  202  of the data lifecycle  200  may be performed in a time order. For example, stage  202 ( 2 ) may occur after stage  202 ( 1 ), stage  202 ( 3 ) may occur after stage  202 ( 2 ), and stage  202 ( 4 ) may occur after stage  202 ( 3 ), and so forth, such that the action(s)  204  in various stage(s)  202  are performed serially. In some examples, the timing of the stage(s)  202  may at least partly overlap such that stage(s)  202  occur in parallel and such that the action(s)  204  of different stages  202  are performed contemporaneously, at least in part. For example, the action(s)  204 ( 2 ) in stage  202 ( 2 ) (e.g., data storage) and/or stage  202 ( 3 ) (e.g., data analysis) may be performed during a time period that at least partly overlaps with a time period during which the action(s)  204 ( 1 ) in stage  202 ( 1 ) (e.g., data collection) are performed. Implementations support a data lifecycle  200  that includes any number of stages  202  that are performed in any order, serially and/or in parallel. 
     As shown in  FIG. 2 , during each stage  202  a set of restriction(s)  118  may be applied that govern the action(s)  204  performed for handling the data  104  during that stage  202 . Restriction(s)  118  may be specified by the end-user(s)  116 , the individual platform(s)  102 , and/or the super-platform  108 . The restriction enforcement module  126  (e.g., agent process) may operate to ensure that restriction(s)  118  are complied with during each stage  202 . During the stage  202 ( 1 ), the restriction enforcement module  126  may interact with the gateway(s)  110  to enforce restriction(s)  118 ( 1 ) on the collection and/or ingest of the data  104  from the platform(s)  102 . During the stage  202 ( 2 ), the restriction enforcement module  126  may interact with the data storage  120  to enforce restriction(s)  118 ( 2 ) on the storage of the data  104  and/or metadata. During the stage  202 ( 3 ), the restriction enforcement module  126  may interact with the analysis module(s)  122  to enforce restriction(s)  118 ( 3 ) on the analysis of the data  104 , and/or the marketplace  112  to enforce restriction(s)  118 ( 3 ) on the sale or other distribution of the data  104 , aggregate data based on the data  104 , analysis result(s) based on analysis of the data  104 , and/or other information. During the stage  202 ( 4 ), the restriction enforcement module  126  may enforce restriction(s)  118 ( 4 ) on the archiving, deletion, destruction, or other operations performed at or near the end of the data lifecycle  200  by the data storage  120  and/or other module(s). 
     In some examples, an end-user  116  may specify restriction(s)  118  on how their data  104  may be employed by data consumers  124 , and/or restriction(s)  118  on which data consumers  124  may be allowed access to the data  104 . A data consumer  124 , when requesting data  104  through the marketplace  112 , may include a description of how they intend to use the data  104 . The end-user  116  may access the end-user interface  114  to approve or disapprove that particular use by the particular data consumer  124 . Data  104  provided to data consumers  124  may identify the end-user  116  associated with the data  104 , or may be anonymized or abstracted to not identify the end-user  116 . In some examples, the various entities (e.g., end-users, individual platforms, and/or data consumers) may have full transparency into the change, use, and/or ownership of the data  104  during the various stages  202 . 
     Depending on which stage of the data supply chain is being analyzed, implementations may examine different relevant aspects at each stage. Implementations may maintain provenance within the data store at each stage. The provenance metadata, maintained on a block chain, may also include information describing what entities have rights to use the data, what those entities have done to obtain those rights, and/or an accounting of what the entities have done with the data. Implementations may enable various questions to be answered at each stage of the data supply chain. Non-limiting examples of such questions are described below. 
     With respect to the acquire/store stage of the data lifecycle, implementations may provide a data discloser with answers to the following questions: Are data disclosers aware that data about them has been acquired? Are additional data subjects captured due to network associations in the data? Will data disclosers be able to access the data they have disclosed? Are disclosers aware of where or how their data is being stored? (e.g. outside of the country, through a cloud provider) Are data disclosers able to opt-out of their data being used? 
     With respect to the acquire/store stage of the data lifecycle, implementations may provide a data consumer with answers to the following questions: What methods were used to collect data sources? If data is coming from an external source, do data collection methods align with the organization&#39;s best practices? In regards to the different data types being used, are best practices for handling and using that type of data known throughout the organization/team? Are data techniques used during analysis appropriate with those used during production? Is data being stored without an intended use? 
     With respect to the aggregate/analyze stage of the data lifecycle, implementations may provide a data discloser with answers to the following questions: Can aggregated data misconstrue relevant information about a data subject? Would the aggregation of sources come as a surprise to data subjects? What is the most harm a bad actor or group of actors could cause if they had access to the aggregated data sources? Could the aggregated data be used to draw outside conclusions, irrelevant to the work at hand, about an individual&#39;s identity, personality, or way of living? Could the results of the proposed analysis have negative consequences for the data discloser outside of the application or service? 
     With respect to the aggregate/analyze stage of the data lifecycle, implementations may provide a data consumer with answers to the following questions: What would the reputational impact be on the organization if data were misused? Does the summation of aggregated data overstep the intended use of the data? Would research methodologies receive a favorable reaction if they were widely shared? Are there plans to revisit analyses and see how well they aligned with organizational code of ethics and governance models? 
     With respect to the use/application of insights stage of the data lifecycle, implementations may provide a data discloser with answers to the following questions: Did the data discloser provide consent to this specific data use? Do consent agreements make it clear that data could be used in the proposed way? Are there mechanisms in place to alert data disclosers their data is being used? 
     With respect to the use/application of insights stage of the data lifecycle, implementations may provide a data consumer with answers to the following questions: What is being done to ensure data use aligns to regulations around that type of data? Are there restrictions in place that prevent discussing where data sources are obtained or whom data sources are being shared with? Does the data used contain enough information for a third party or law enforcement to conduct surveillance on data disclosers? Do the conclusions from the analysis replace a data discloser&#39;s ability to make independent decisions? 
     With respect to the share/sell stage of the data lifecycle, implementations may provide a data discloser with answers to the following questions: Is it in the data discloser&#39;s best interest to have their data shared among third parties? Do data disclosers have any say in whether or not their data is shared or sold? Are data disclosers aware their data is being shared or sold? Do data disclosers expect control or ownership over the data they provide? 
     With respect to the share/sell stage of the data lifecycle, implementations may provide a data consumer with answers to the following questions: What parties are designated stewards of data once data is shared or sold? Does data sharing encroach on cultural norms about privacy or regulatory standards that need to be met? Does the act of sharing or selling data enhance the experience for the data discloser? Are there alternative models that could be used for sharing/selling data that provide greater control or transparency to data disclosers? 
     With respect to the delete/removal stage of the data lifecycle, implementations may provide a data discloser with answers to the following questions: Are data disclosers given the ability to delete their data? Are stakeholders aware of the time frame that their data will be retained and processed for disposal? Are data disclosers given the right to restrict future use of their data? 
     With respect to the delete removal stage of the data lifecycle, implementations may provide a data consumer with answers to the following questions: Are systems designed with the intentions of allowing users to delete their data? Is any metadata retained even after associated data are deleted? 
     The collected data  104 , aggregate data, and/or analysis result(s) may be offered for sale or other distribution to data consumer(s)  124 , such as data purchaser(s), through the marketplace  112 . In some examples, an end-user  116  may opt in to allow their user-related data  104  to be sold or otherwise distributed through the marketplace  112 . The end-user  116  may provide a price for their data  104 . The end-user  116  may also provide restriction(s)  118  governing which data consumer(s)  124  may receive the end-user&#39;s data  104 . The end-user  116  may also provide restriction(s)  118  governing the uses of the end-user&#39;s data  104  by potential data consumer(s)  124 . For example, an end-user  116  may specify that their data may not be provided to data consumer(s)  124  that intend to use the data  104  for a particular purpose, such as research associated with a particular political position, campaign, or cause. As another example, an end-user  116  may specify that their data  104  may not be provided to particular types of organizations. For example, an end-user  116  may state that their data  104  may be sold to non-profit research organizations but not to for-profit corporations. An end-user  116  may also specify the manner in which their data  104  may be used. For example, an end-user  116  may specify that their data  104  may be used after it has been anonymized to remove and/or obfuscate any PII. 
     In some examples, the end-user  116  may specify restrictions  118  when they opt in or otherwise give permission for their data  104  to be sold or otherwise distributed, e.g., prior to the distribution or other use of the data  104 . In some examples, the end-user  116  may access information presented through the end-user interface  114 , the information indicating that their data  104  has been sold or otherwise provided for a particular purpose. Based on this information, the end-user  116  may indicate that their data  104  may not be subsequently sold for a similar purpose. Similarity may be determined based on a semantic (e.g., natural language) analysis of the stated purpose of a data consumer  124 . In some implementations, the end-user  116  (e.g., data discloser) may have access to information (e.g., in a blockchain) describing a history of the various uses to which their data  104  has been put, who it has been sold to, how it has been analyzed, how it has been processed, and so forth. Based on such information, the end-user  116  may learn that their data  104  has been sold to or shared with a data consumer  124  and the end-user  116  may request some remuneration such as a percentage of the sale price. While data disclosers can inspect where their data has gone and what it has been used for, implementations may provide that remuneration requests from data disclosers are up-front as part of the initial data ingest. 
     In some instances, the ingest process may be better suited toward data disclosers who are associated with large portfolios of data generation populations, e.g., of people and/or machines. When a data discloser adds a dataset to the platform, they may have a wide array of preferences they can select from through a form-based, interactive user experience provided by the super-platform. In addition to agreeing to disclosures on the various ways their data might be used, data disclosers may also be asked about their preferences for remuneration (and disclosers can make these conditional based on use cases). For example, a data discloser might choose to make their data free for purposes of academic research, but charge for use by private corporations. Data disclosers might allow their data to be combined with other data sets for humanitarian use cases, but require that it be used in isolation if used by defense contractors. Data disclosures can choose who may use their data, how they use that data, for what purpose, and for what cost. These conditions can be updated by the platform at any time and can also be crowd-sourced from actors (e.g., data disclosers, data manipulators, and/or data consumers) within the ecosystem. 
     In some examples, the end-user  116  may specify restriction(s)  118  to restrict the sale and/or use of their data  104  based on a static set of categories. The set of categories may also be dynamic in that the end-user  116  may be able to add new categories for possible restriction of data sale or use. Categories may be arranged hierarchically. For example, an end-user  116  may be able to restrict data usage within a broad category (e.g., political research) or within a more specific category (e.g., political research related to a particular campaign, party, or candidate). The super-platform  108  may generate push notifications or other types of messages that are sent to an end-user  116  to indicate that the end-user&#39;s data  104  has been purchased or used for a particular purpose by one or more data consumers  124 . In some implementations, the end-user  116  may access the end-user interface  114  in response to receiving the push notifications or other messages, and the end-user  116  may provide further restriction(s)  118  to prevent similar use and/or distribution in the future, or to terminate the current use and/or distribution described in the message(s). 
     The restriction(s)  118  may also include, but are not limited to: time constraints, indicating a particular time period during which the data  104  may be accessed and used; constraints indicating whether the data  104  may be used in an aggregate form with other data, or whether the data  104  may be used in its individual, originally collected, form; constraints indicating whether the data  104  is to be anonymized to remove and/or obfuscate PII prior to sale, distribution, and/or use; and so forth. 
     Although examples herein may describe the restriction(s)  118  as a black list, e.g., a set of restricted uses and/or data consumer(s)  124 , implementations are not so limited. In some implementations, the restriction(s)  118  may be a white list, in which the end-user  116  specifies a list of allowed uses and/or allowed data consumer(s)  124 . In such examples, a requested distribution of data may be blocked or a requesting data consumer  124  may be denied access to the data  104  unless the data consumer&#39;s stated use and/or the data consumer  124  is included in the white list of allowed uses and/or data consumer(s)  124 . 
     As described above, the analysis module(s)  122  may perform aggregation or other types of analyses. Aggregation may generate aggregate data sets based on data from multiple individual platforms  102  or multiple end-users  116 . Data  104  may be aggregated according to particular demographic characteristics, location, and/or other characteristics. For example, the marketplace  112  may collect user data from users in Iowa and aggregate the data to create a data set describing the buying habits, browsing habits, or other characteristics of users in a particular market segment defined by location, age, gender, or other criteria. In some examples, restriction(s)  118  may indicate that the aggregate data sets are to be generated such that they do not include any PII. In some examples, the aggregate data sets may be generated based on particular requests from data consumers  124 . The analysis module(s)  122  may also generate aggregate data sets prior to, and in anticipation of, receiving any particular aggregate data set request(s) and/or analysis request(s). 
     The super-platform  108  may create an adhesive experience for data consumers  124 , such that data consumer(s)  124  are incentivized to return to the super-platform  108  for their future data needs. This adhesive experience may be achieved by providing data consumers  124  with data  104 , aggregate data, analysis job(s), and/or data analysis result(s) that the data consumer(s)  124  may not have access to elsewhere. By providing, analyzing, and/or aggregating data  104  generated by multiple individual platforms  102 , the super-platform  108  may provide greater value than that provided by the individual platform(s)  102  alone. 
     Various actions may be performed in instances where the ingested data  104  is later determined to be inaccurate or fraudulent, after the data  104  has been distributed to data consumer(s)  124  and/or after aggregate data or analysis result(s) based on the data  104  have been distributed. In some examples, the data  104  may be associated with metadata that indicates a source, version, or timestamp for the data  104 . The super-platform  108  may provide an interface that enables the data consumer(s)  124  to check existing purchases and/or other data distributions for changes or alerts regarding the integrity of the data  104  generated by various platforms  102 . The super-platform  108  may notify the data consumer(s)  124  that previous distributed information may be inaccurate in some way, and may provide the data consumer(s)  124  with the option to received updated data that at least partly corrects the flaws of the previously distributed data. Such updates may be implemented using a content management system with version control. In some examples, the individual platform(s)  102  may inform the super-platform  108  that previously provided data  104  is inaccurate. In some examples, the super-platform  108  may be configured to determine that data  104  previously received at the super-platform  108  is inaccurate. 
       FIG. 3  depicts an example process for managing data handling in multiple stages  202  of a data lifecycle  200 , in accordance with implementations of the present disclosure. Operations of the process may be performed by one or more of the gateway(s)  110 , the restriction enforcement module  126 , the data storage  120 , the analysis module(s)  122 , the marketplace  112 , the end-user interface  114 , and/or other software module(s) executing on the super-platform  108 , the platform(s)  102 , or elsewhere. 
     Restriction(s)  118  may be received ( 302 ). As described above, the restriction(s)  118  may restrict the handling of data  104  at various stages  202  in the data lifecycle  200 . The restriction(s)  118  may be provided by end-user(s)  116 , platform(s)  102 , and/or the super-platform  108 . 
     Action(s)  204 ( 1 ) may be performed ( 304 ) in a first stage  202 ( 1 ) of the data lifecycle  200 , according to a first set of restriction(s)  118 ( 1 ). Such action(s)  204 ( 1 ) may include the generation of the data  104  by the platform(s)  102  and/or the collection (e.g., ingest) of the data  104  by the super-platform  108 . 
     Action(s)  204 ( 2 ) may be performed ( 306 ) in a second stage  202 ( 2 ) of the data lifecycle  200 , according to a second set of restriction(s)  118 ( 2 ). Such action(s)  204 ( 2 ) may include the storage of the data  104 . 
     Action(s)  204 ( 3 ) may be performed ( 308 ) in a third stage  202 ( 3 ) of the data lifecycle  200 , according to a third set of restriction(s)  118 ( 3 ). Such action(s)  204 ( 3 ) may include the analysis, aggregation, and/or other processing of the data  104 . Action(s)  204 ( 4 ) may also include the distribution (e.g., sale) and/or other uses of the data  104 , aggregate data, analysis result(s), and/or other information that is based on the data  104 . 
     Action(s)  204 ( 4 ) may be performed ( 310 ) in a fourth stage  202 ( 4 ) of the data lifecycle  200 , according to a fourth set of restriction(s)  118 ( 4 ). Such action(s)  204 ( 4 ) may include the generation of the data  104  by the platform(s)  102  and/or the collection (e.g., ingest) of the data  104  by the super-platform  108 . 
     In some implementations, the super-platform may distinguish between data that is specific to a particular user (e.g., user ID, user purchases, content viewed by user, etc.), and data that is non-specific (e.g., sensor data, ambient temperature measured at platform, etc.). Such distinctions may be made by the super-platform to maintain data integrity and/or accuracy. For example, in situations where data describes a person and that person is domiciled in a state with particular controls on data, that information may be maintained for individual records (e.g., for people and devices). Provenance of data may be important in such instances and the super-platforms may be configured to maintain information about where the data originated. For example, if the data is from a sensor, the super-platform may store information regarding the make, model, firmware revision, and/or OS version of the sensor. If new information is later received indicating that the sensor is buggy, the super-platform may provide that information to enable various entities decide accordingly whether, and/or to what extent, to trust the possibly inaccurate data from the sensor. Origination and/or other provenance information may also be employed for at least the reasons described above. 
     In some implementations, the super-platform may make a distinction based on whether data analysis is performed on the super-platform or on a computing device controlled by the data consumer. In some instances, sensitive data sets may only be available for analysis on the super-platform and may not be available for analysis by on-premises applications executing on a computing device controlled by other entities. Accordingly, in some implementations a daemon and/or local agent may execute on a data consumer&#39;s device to ensure that the consumer&#39;s stated purpose is consistent with the analysis performed. 
     In some implementations, the super-platform may attempt to avoid providing duplicate data to data consumers, e.g., in instances where the original user-specific data may be aggregated to generate aggregate data that is abstracted away from the user. For example, duplicate data may be cleansed (e.g., removed, deprecated, marked as duplicate, etc.). In some implementations, duplicate data may be maintained to ensure that particular data is sufficiently described within the system. This may aid in the detection of duplicate data. In some instances, the presence of duplicates within an aggregate data set can be a net advantage. For example, duplicate data may not affect the aggregation phase, but may higher confidence levels at an analysis phase. 
       FIG. 4  depicts an example computing system  400  in accordance with implementations of the present disclosure. The system  400  may be used for any of the operations described with respect to the various implementations discussed herein. For example, the system  400  may be included, at least in part, in one or more of platforms  102 , the super-platform  108 , and/or other device(s) described herein. The system  400  may include one or more processors  410 , a memory  420 , one or more storage devices  430 , and one or more input/output (I/O) devices  450  controllable through one or more I/O interfaces  440 . Two or more of the components  410 ,  420 ,  430 ,  440 , or  450  may be interconnected through at least one system bus  460 , which may enable the transfer of data between the various modules and components of the system  400 . 
     The processor(s)  410  may be configured to process instructions for execution within the system  400 . The processor(s)  410  may include single-threaded processor(s), multi-threaded processor(s), or both. The processor(s)  410  may be configured to process instructions stored in the memory  420  or on the storage device(s)  430 . The processor(s)  410  may include hardware-based processor(s) each including one or more cores. The processor(s)  410  may include general purpose processor(s), special purpose processor(s), or both. 
     The memory  420  may store information within the system  400 . In some implementations, the memory  420  includes one or more computer-readable media. The memory  420  may include any number of volatile memory units, any number of non-volatile memory units, or both volatile and non-volatile memory units. The memory  420  may include read-only memory, random access memory, or both. In some examples, the memory  420  may be employed as active or physical memory by one or more executing software modules. 
     The storage device(s)  430  may be configured to provide (e.g., persistent) mass storage for the system  400 . In some implementations, the storage device(s)  430  may include one or more computer-readable media. For example, the storage device(s)  430  may include a floppy disk device, a hard disk device, an optical disk device, or a tape device. The storage device(s)  430  may include read-only memory, random access memory, or both. The storage device(s)  430  may include one or more of an internal hard drive, an external hard drive, or a removable drive. 
     One or both of the memory  420  or the storage device(s)  430  may include one or more computer-readable storage media (CRSM). The CRSM may include one or more of an electronic storage medium, a magnetic storage medium, an optical storage medium, a magneto-optical storage medium, a quantum storage medium, a mechanical computer storage medium, and so forth. The CRSM may provide storage of computer-readable instructions describing data structures, processes, applications, programs, other modules, or other data for the operation of the system  400 . In some implementations, the CRSM may include a data store that provides storage of computer-readable instructions or other information in a non-transitory format. The CRSM may be incorporated into the system  400  or may be external with respect to the system  400 . The CRSM may include read-only memory, random access memory, or both. One or more CRSM suitable for tangibly embodying computer program instructions and data may include any type of non-volatile memory, including but not limited to: semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. In some examples, the processor(s)  410  and the memory  420  may be supplemented by, or incorporated into, one or more application-specific integrated circuits (ASICs). 
     The system  400  may include one or more I/O devices  450 . The I/O device(s)  450  may include one or more input devices such as a keyboard, a mouse, a pen, a game controller, a touch input device, an audio input device (e.g., a microphone), a gestural input device, a haptic input device, an image or video capture device (e.g., a camera), or other devices. In some examples, the I/O device(s)  450  may also include one or more output devices such as a display, LED(s), an audio output device (e.g., a speaker), a printer, a haptic output device, and so forth. The I/O device(s)  450  may be physically incorporated in one or more computing devices of the system  400 , or may be external with respect to one or more computing devices of the system  400 . 
     The system  400  may include one or more I/O interfaces  440  to enable components or modules of the system  400  to control, interface with, or otherwise communicate with the I/O device(s)  450 . The I/O interface(s)  440  may enable information to be transferred in or out of the system  400 , or between components of the system  400 , through serial communication, parallel communication, or other types of communication. For example, the I/O interface(s)  440  may comply with a version of the RS-232 standard for serial ports, or with a version of the IEEE 1284 standard for parallel ports. As another example, the I/O interface(s)  440  may be configured to provide a connection over Universal Serial Bus (USB) or Ethernet. In some examples, the I/O interface(s)  440  may be configured to provide a serial connection that is compliant with a version of the IEEE 1394 standard. 
     The I/O interface(s)  440  may also include one or more network interfaces that enable communications between computing devices in the system  400 , or between the system  400  and other network-connected computing systems. The network interface(s) may include one or more network interface controllers (NICs) or other types of transceiver devices configured to send and receive communications over one or more networks using any network protocol. 
     Computing devices of the system  400  may communicate with one another, or with other computing devices, using one or more networks. Such networks may include public networks such as the internet, private networks such as an institutional or personal intranet, or any combination of private and public networks. The networks may include any type of wired or wireless network, including but not limited to local area networks (LANs), wide area networks (WANs), wireless WANs (WWANs), wireless LANs (WLANs), mobile communications networks (e.g., 3G, 4G, Edge, etc.), and so forth. In some implementations, the communications between computing devices may be encrypted or otherwise secured. For example, communications may employ one or more public or private cryptographic keys, ciphers, digital certificates, or other credentials supported by a security protocol, such as any version of the Secure Sockets Layer (SSL) or the Transport Layer Security (TLS) protocol. 
     The system  400  may include any number of computing devices of any type. The computing device(s) may include, but are not limited to: a personal computer, a smartphone, a tablet computer, a wearable computer, an implanted computer, a mobile gaming device, an electronic book reader, an automotive computer, a desktop computer, a laptop computer, a notebook computer, a game console, a home entertainment device, a network computer, a server computer, a mainframe computer, a distributed computing device (e.g., a cloud computing device), a microcomputer, a system on a chip (SoC), a system in a package (SiP), and so forth. Although examples herein may describe computing device(s) as physical device(s), implementations are not so limited. In some examples, a computing device may include one or more of a virtual computing environment, a hypervisor, an emulation, or a virtual machine executing on one or more physical computing devices. In some examples, two or more computing devices may include a cluster, cloud, farm, or other grouping of multiple devices that coordinate operations to provide load balancing, failover support, parallel processing capabilities, shared storage resources, shared networking capabilities, or other aspects. 
     The features described may be implemented in digital electronic circuitry or in computer hardware, software, or any combinations of hardware and software. The features may be implemented in at least one computer program product that is tangibly embodied in an information carrier (e.g., a machine-readable storage device) for execution by a programmable processor. The method or process steps may be performed by a programmable processor executing instructions to perform functions of the described implementations by operating on input data and generating output. The described features may be implemented in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device. A computer program may include a set of instructions that can be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result. A computer program may be written in any form of programming language, including compiled or interpreted languages, and it may be deployed in any form, including as a stand-alone program or as a module suitable for use in a computing environment. 
     The logic flows depicted in the figures do not require the particular order shown, or any particular sequential order, to achieve desirable results. In some implementations, other steps may be provided or steps may be eliminated from the described flows. Moreover, the steps may be performed in parallel or serially with respect to other steps. The systems depicted in the figures do not require the particular components, or the particular arrangement of components, shown in the figures. In some implementations, the various systems may include more or fewer components than shown in the figures, and components may be arranged differently to achieve desirable results. Accordingly, implementations other than those explicitly depicted in the figures or described herein are within the scope of the following claims. 
     A number of implementations of the present disclosure have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the present disclosure. Accordingly, other implementations are within the scope of the following claims.