Patent Publication Number: US-11386218-B2

Title: Platform management of integrated access of public and privately-accessible datasets utilizing federated query generation and query schema rewriting optimization

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation application (“CON”) of U.S. patent application Ser. No. 15/439,911, filed Feb. 22, 2017; This application is a continuation application (“CON”) of U.S. patent application Ser. No. 16/428,456, filed May 31, 2019; This application and U.S. patent application Ser. No. 15/439,911 are a continuation-in-part application of U.S. patent application Ser. No. 15/186,514, filed Jun. 19, 2016, now U.S. Pat. No. 10,102,258; This application and U.S. patent application Ser. No. 15/439,911 are also a continuation-in-part application of U.S. patent application Ser. No. 15/186,515, filed Jun. 19, 2016; This application and U.S. patent application Ser. No. 15/439,911 are also a continuation-in-part application of U.S. patent application Ser. No. 15/186,516, filed Jun. 19, 2016; This application and U.S. patent application Ser. No. 15/439,911 are also a continuation-in-part application of U.S. patent application Ser. No. 15/186,517, filed Jun. 19, 2016, now U.S. Pat. No. 10,324,925; This application and U.S. patent application Ser. No. 15/439,911 are also a continuation-in-part application of U.S. patent application Ser. No. 15/186,519, filed Jun. 19, 2016; This application and U.S. patent application Ser. No. 15/439,911 are also a continuation-in-part application of U.S. patent application Ser. No. 15/186,520, filed Jun. 19, 2016; This application and U.S. patent application Ser. No. 15/439,911 are also a continuation-in-part application of U.S. patent application Ser. No. 15/454,923, filed Mar. 9, 2017; This application and U.S. patent application Ser. No. 15/439,911 are also a continuation-in-part application of U.S. Nonprovisional patent application Ser. No. 15/454,955, filed Mar. 9, 2017; This application and U.S. patent application Ser. No. 15/439,911 are also a continuation-in-part application of U.S. patent application Ser. No. 15/454,969, filed Mar. 9, 2017; This application and U.S. patent application Ser. No. 15/439,911 are also a continuation-in-part application of U.S. patent application Ser. No. 15/454,981, filed Mar. 9, 2017; all of which are hereby incorporated by reference in their entirety for all purposes. 
    
    
     FIELD 
     The present invention relates generally to data science, machine and deep learning computer algorithms, data graph modeling, and analysis of linked data. More specifically, techniques for management of integrated access to public and privately-accessible datasets are described. 
     BACKGROUND 
     As demand for data and data science expands rapidly, significant research into potential uses of data in various applications are also increasing at a dramatic rate. With enormous amounts of data and information becoming increasingly available, utilizing data is becoming a greater focus of both consumer and commercial activities alike. Datasets (i.e., sets or groups of logically-related data and/or information) are being created to provide statistical information that researchers are using to discover new innovations and applications in almost every aspect of contemporary life and lifestyles. However, utilizing data also involves addressing a growing problem, which includes identifying data, sources thereof, and managing the ever-increasing amount of data becoming available. Moreover, as the amount and complexity of data, datasets, databases, datastores and data storage facilities increase, the ability to identify, locate, retrieve, analyze, and present data in useful ways is also becoming increasingly difficult. Today, managing large amounts of data for useful purposes poses a significant problem for individual users, organizations, and entities alike. Conventional techniques are problematic in that these are neither capable nor configured to manage large scale problems such as providing integrated access to data that is both available on public resources as well as those that are hosted or stored on private (i.e., secure (i.e., requiring authentication or authorization before access is permitted)) data storage resources. More importantly, users are typically burdened by conventional techniques in that access to data often requires not only proficient, if not expert, knowledge of both computer programming languages commonly known and used by data researchers and scientists (e.g., Python, or others), but knowledge of complex computer databases, datastores, data repositories, data warehouses, data and object schema, data modeling, graph modeling, graph data, linked data, and numerous other data science topics is also required. Queries executed to retrieve data using conventional techniques typically require knowledge of specific programming or formatting languages, which can limit the usability of data. Specifically, conventional techniques are problematic because these lack intrinsic knowledge or technical functionality to permit a user such as a data scientist to locate, manage, access, and execute queries to retrieve data from various disparate and often dissimilar data resources. 
     Thus, what is needed is a solution for managing consolidated, integrated access to public and/or privately-accessible (i.e., secure) data without the limitations of conventional techniques. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various embodiments or examples (“examples”) of the invention are disclosed in the following detailed description and the accompanying drawings: 
         FIG. 1  illustrates an exemplary topology for platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization; 
         FIG. 2  illustrates an exemplary platform architecture for a platform for managing integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization; 
         FIG. 3  illustrates an exemplary layered architecture for platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization; 
         FIG. 4  illustrates an exemplary data flow for platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization; 
         FIG. 5  illustrates an exemplary data operations model illustrating various processes for platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization; 
         FIG. 6A  illustrates an exemplary process flow for platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization; 
         FIG. 6B  illustrates a further exemplary process flow for platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization; 
         FIG. 6C  illustrates another exemplary process flow for platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization; 
         FIG. 6D  illustrates an additional exemplary process flow for platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization; 
         FIG. 6E  illustrates yet a further exemplary process flow for platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization; 
         FIG. 7A  illustrates an alternative exemplary process flow for platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization; 
         FIG. 7B  illustrates a further alternative exemplary process flow for optimization of rewritten queries using platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization; and 
         FIG. 8  illustrates an exemplary computer system suitable for platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization. 
     
    
    
     DETAILED DESCRIPTION 
     Various embodiments or examples may be implemented in numerous ways, including as a system, a process, an apparatus, a user interface, or a series of program instructions on a computer readable medium such as a computer readable storage medium or a computer network where the program instructions are sent over optical, electronic, or wireless communication links. In general, operations of disclosed processes may be performed in an arbitrary order, unless otherwise provided in the claims. 
     A detailed description of one or more examples is provided below along with accompanying figures. The detailed description is provided in connection with such examples, but is not limited to any particular example. The scope is limited only by the claims and numerous alternatives, modifications, and equivalents are encompassed. Numerous specific details are set forth in the following description in order to provide a thorough understanding. These details are provided for the purpose of example and the described techniques may be practiced according to the claims without some or all of these specific details. For clarity, technical material that is known in the technical fields related to the examples has not been described in detail to avoid unnecessarily obscuring the description. 
       FIG. 1  illustrates an exemplary topology for platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization. Here, topology  100  includes dataset access platform (“platform”)  102 , databases  104 - 106 , data networks  108 - 112  (as used herein, “data network” and “network” may be used interchangeably without limitation or restriction and are intended to be interpreted similarly with respect to this Detailed Description and/or the accompanying claims), databases  114 - 118 , access control module  120 , database  122 , and datastore  123  (including databases  124 - 128 ). In some examples, “topology” may refer to a computer network topology that represents a map or aggregation of computing resources that are used to implement a feature, function, or set or group of functionality, including identified resources, technical specifications, protocols, languages, formats, and other elements. As used herein, “database” (e.g., databases  104 - 106 ,  114 - 118 ,  122 ,  124 - 128 ) may refer to any type of data storage facility, including, but not limited to, a standalone, web, networked, or computing cloud-based database, datastore, data repository, data warehouse, or any other type of facility or resource that may be used to store and/or retrieve data and information stored in accordance with a structured, unstructured, relational, or non-relational data schema or data object schema. As used herein, the terms “computing cloud” or “cloud” may be used interchangeably without limitation and may refer to any logical collection, grouping, assembly, or identified set of data computing based resources that provide various types of processing, storage, or other data operation and are not limited to any specific topology or geographic restriction and may be deployed over a distributed area or set of resources such as a collection of computers or servers located in disparate facilities distributed geographically, without limitation. In some examples, “datastore” (e.g., datastore  123 ) may refer to one or more databases (e.g., databases  104 - 106 ,  114 - 118 ,  122 ,  124 - 128 ) that are grouped or otherwise rendered interoperable using logical layers to provide management or overriding layers of management functionality for purposes of accessing, storing, and/or retrieving data and information stored within one or more databases within a given datastore. A datastore (e.g., datastore  123 ) does not need to topologically or logically reside on a single or individual network resource, as an example, and may be distributed in a widespread or disparate architecture using networked resources such as those found within a public or private (i.e., secured using authentication, authorization, token, password, or any other form of data security technique) data network, a computing cloud, or logical collection of networked data storage resources. For example, datastore  123  is shown including databases  124 - 128 , but may, in other examples, also include one, some, or none of databases  104 - 106  and  114 - 118 . Datastore  123  may also be implemented as a computing cloud and are not limited to any specific types of network architectures or topologies and the examples shown here are provided for purposes of exemplary illustration and description, without limitation. In other examples, other designs and implementations beyond those set forth and described herein may be used, without limitation or restriction to any specific design, architecture, implementation, embodiment, or example (i.e., collectively, “example”). 
     As illustrated in exemplary topology  100 , in some examples, dataset access platform  102  may be configured to access public and/or privately-accessible datasets that are hosted on one or more databases, some, all, or none of which may be hosted on data networks such as networks  108 - 112 . As used herein, “dataset access platform,” “access platform,” and “platform” may be used interchangeably without limitation and, in some examples, refers to a computer program, software, firmware, circuitry, algorithms, logic, hardware, or a combination thereof in order to implement techniques (e.g., systems, processes, or the like) for providing integrated query, access, retrieval, and other data operations using public and private datasets. As shown in topology  100 , platform  102  may be configured to access databases  104 - 106 ,  114 - 118 ,  122  and/or datastore  123  including databases  124 - 128  in order to execute a query to retrieve one or more datasets stored in these elements. Datasets may be retrieved by, for example, data scientists, researchers, or any other user who may be interested in querying and retrieving a dataset for a given purpose. Datasets may include any type, form, format, or amount of publicly-accessible sources of data such as those available from Data.Gov, the U.S. Department of Defense, oceanographic data from the National Oceanic and Atmospheric Administration (NOAA), as well as privately collected, curated, managed, and created datasets such as those found on corporate, non-profit, research, scientific, or academic data networks. Datasets may be retrieved from a large number of sources and, as used herein, are not intended to be limited to any specific type, source, or format of data. In some examples, network  108  may be a publicly-accessible data network that includes one or more databases such as databases  114 - 118 . 
     In some examples, databases  104 - 106 ,  114 - 118 ,  122  and datastore  123  including databases  124 - 128  may be accessed or used by dataset access platform  102  using a “farm” or collection of graph database engines (see element  228  ( FIG. 2 ) below) that are configured to execute queries received by (e.g., queries sent in SQL or other structured or unstructured programming or formatting languages to) platform  102  to retrieve datasets from one or more of  104 - 106 ,  114 - 118 ,  122  and datastore  123 , which includes databases  124 - 128 , each database of which may be configured for public (i.e., open) or private (i.e., secure, authentication required, access controlled, or the like) access, without limitation. In some examples, a dataset may reside on a private database (e.g., within a data network that requires authentication or access control conditions (e.g., tokens, certificates, passwords, hashes, or the like) in order to access the data network (e.g., network  112 ) and/or the dataset (i.e., which may be stored on database  122  or datastore  123  including databases  124 - 128 ). Private datasets (e.g., database  122 ) may reside on a secure network in order to prevent access to data that may be sensitive, confidential, private, personal, or otherwise not desired or intended for public viewing. 
     As shown, platform  102  may be configured to access datasets stored on publicly-accessible (i.e., public or open) databases  104 - 106  and  114 - 118  or, in some examples, private database  122  and/or datastore  123  and databases  124 - 128 . Platform  102 , in some examples, may be a platform or application such as that developed by Data.World of Austin, Tex., including various features and functionality, as described in some of those properties incorporated by reference as set forth above. As shown, datastore  123  includes databases  124 - 128 , although the number, type, format, data schema, and other characteristics may be varied and are not limited to the examples shown and described. For example, datastore  123  may use a database management system (not shown) to manage databases  124 - 128 . As shown here, platform  102  may be configured to communicate over one or more other data networks such as the Internet, a private data network, or a computing cloud, without limitation to the type of data network provided a layered topology is used to communicate queries to/from platform  102  and a destination or target database (e.g., databases  104 - 106 ,  114 - 118 ,  122  and datastore  123  including databases  124 - 128 ). Platform  102  may also be configured to access datastore  123 , which could be housed and operated on a separate data network (e.g., data network  112 ) than another data network through which a query or request is transmitted, passed, or sent (e.g., data network  110 ). In other words, platform  102  may be a standalone, distributed, local, remote, or cloud-based application, process, algorithm(s), computer program, software, firmware, hardware, server, or the like (hereafter “application”) that may be a standalone or distributed application, the latter of which may have one or more resources housed, stored in memory, executed from, or reside on disparate physical resources (e.g., servers, computers, or the like) in different geographic locations. However, when a query or request to query (the terms “query,” “request,” or “request to query” may be used interchangeably herein) is received by platform  102  for one or more of databases  104 - 106 ,  114 - 118 ,  122  and datastore  123  including databases  124 - 128 , platform  102  may be configured to receive, parse, interpret, convert, rewrite, optimize, and execute the query in order to retrieve a dataset from one of the aforementioned data sources (i.e., databases  104 - 106 ,  114 - 118 ,  122  and datastore  123  including databases  124 - 128 ). 
     In some examples, a query (e.g., sent in SQL, SPARQL, R, Python, Java, Javascript, JSON, XML, or any other programming or formatting language that is used to generate and send queries for retrieving datasets) may be received by platform  102  and sent to access control module  120  (as with platform  102 , access control module  120  may be a standalone, distributed, local, remote, or cloud-based application, process, algorithm(s), computer program, software, firmware, hardware, server, or the like (hereafter “application”)), which provides access control functionality and prevents unauthorized access to datasets stored on one or more of databases  122  and  124 - 128  and datastore  123 . In other words, access control module  120  receives queries on behalf of, for example, a private data network (e.g., network  112 ), which could be a scientific, academic, research, governmental, military, financial, corporate, non-profit, or any other type of data network in which non-public access is desired or security measures including, but not limited to access control module  120 , are intended to limit, deter, or prevent access. If the query received by platform  102  and sent to network  112 , which is an exemplary private data network, is rejected due to a lack of authorization or permission to access the dataset and/or data network (i.e., an access control condition is not met), platform  102  can notify a user (not shown) on a display or user interface that indicates a status of the query (also not shown). For example, a query written in SQL may be received by platform  102 , which may be a standalone (e.g., hosted, remote, or local) or distributed (e.g., server, network, or cloud-based) software platform composed of multiple programs or scripts (e.g., Java®, JavaScript®, and/or other programming or formatting languages, structured or unstructured, or the like) that is configured to parse and analyze the query to determine through inference (as described in greater detail below) attributes, one of which may include an access control condition that permits the query to be run (i.e., executed) against an access-controlled (e.g., password, encryption, authentication, token-based, or any other form of electronic or digital security measure intended to limit or prevent access to a given dataset) database, datastore, dataset, network, or the like. Once authenticated (i.e., an access control condition matches or is approved by access control module  120 ), a query (not shown) from platform  102  may be permitted access in order to retrieve a dataset from database  122  or datastore  123  (and, subsequently, databases  124 - 128 ). Due to conventional solutions being problematic in handling and executing queries in one format against databases that may be in another format, platform  102  is configured to receive, parse, and run inference operations (as described in greater detail below) in order to determine and identify any attributes that may be related to the query, the dataset(s), or the database or datastore in which the dataset(s) are stored. More specifically, platform  102  includes, among other modules and functionality, an inference engine (not shown) that is configured to infer one or more attributes of a query, the target dataset (i.e., the dataset requested once the query has been executed), and the source database or datastore on which the dataset(s) are stored. Further, platform  102  may also be configured to convert a query from one format (e.g., SQL or another structured or unstructured query language) into a different “atomic” format (e.g., RDF™ (as developed by W3C®, or another triple-oriented language (i.e., languages and protocols such as SPARQL (as also developed by W3C®) that may be used to convert data associated with queries into subject-predicate-object-oriented data structures otherwise known as “triples”) that can be used to generate, by platform  102 , rewritten queries that incorporate other triple data directed to attributes such as type, format, access control conditions, or in an integrated manner against various types and formats of databases, datastores, data repositories, data warehouses, and the like. 
     As an example, platform  102  may be configured to rewrite a query (e.g., programmed or formatted in SQL, Python, R, or other statistical or data analytical software) from one format, structure, or schema to another in order to execute a query against multiple disparate types of data storage facilities (e.g., databases, datastores, data repositories, data warehouses, and the like), which may each be of a different schema, structure, and/or type, without restriction. Further, in some examples, platform  102  may be configured to rewrite a query from one format, structure, or schema into another, but also “optimize” a rewritten query (as described in further detail below), by converting data associated with one or more inferred attributes that were determined during the parsing of the query upon its receipt by platform  102 . “Optimizing” a query before, during, or after it has been rewritten by platform  102 , may, in some examples, refer to optimizing a copy of a query or a master of a query. Optimizing a query may occur during or after a rewriting operation has been performed by platform  102 , which could include, but is not limited to, rewriting a query (i.e., master or a copy) from one query language to another format that can then be used to generate further downstream queries for different target or disparate databases that may include datasets that are either sought, in accordance with the original query, or logic incorporated into platform  102  may execute to infer there may be other datasets that are indexed or linked (i.e., as linked data) by platform  102  that, although not known or targeted by the original query, could be returned with the intended target dataset. In some examples, queries may be optimized after being written from SQL to triples using RDF™, SPARQL™, or the like because the rewritten triple data, which may be stored in a datastore accessed by platform  102 , but intended to store converted triple data from incoming queries (i.e., a “triple store”) may be retrieved with other triple data that has been generated resultantly from inferred attributes. In other words, inferred attributes such as type, data types (i.e., specific types of data that are typically identified by columnar or row headings in a tabular format, but could also be found in a multi-dimensional grid storage structure such as name, date, value, postal code, country, state, or any other type that can be used to identify a logical grouping of data, without limitation or restriction), data structure, data schema, object schema, addresses (e.g., Uniform Resource Locator (URL), Uniform Resource Identifier (URI), web address, and the like), layout, design, style, format, language, structure, and others without limitation to any particular attribute or type or category thereof. The triple data rewritten from the query and the triple data associated with attributes related to the query (hereafter, “query” may refer to a copy of a query or a master (i.e., original or originally received by platform  102 ) query, without limitation or restriction) may be specifically rewritten for a database housing or storing the intended target dataset database. In some examples, an original query or a copy of an original query may be subject to various data operations by platform  102 , without restriction or limitation. If a copy of an original query is used by platform  102 , the original query may itself be identified as a “master” and saved to one or more of databases  104 - 106  or another database, datastore, data warehouse, data repository, or other data facility or structure used by platform  102  to store internal data. Thus, a master query or master (hereafter “master”) may be preserved in the event query data used by platform  102  becomes corrupted or unusable. 
     In some examples, other databases that are “known” through previous queries or discovery by platform  102  that may store or house datasets similar, related, or associated with the intended dataset may be identified as a linked dataset or linked data and included in part of a data model or graph that can be used to retrieve data or datasets in response to various queries. In other words, platform  102  may use a graph (i.e., data model) that, once a query is received, logic (e.g., a logic module that may employ rules, machine learning, artificial intelligence, deep learning, natural language processing, or other algorithms, software, computer programs, applications, or the like to implement decision-based processing of data) then determines other linked data may be related to the dataset sought by the query and delivered to the user in response. Further, the linked datasets may also be included in a modified or new graph that may be created to include the intended target dataset as a new node within the graph. Various types of graph generation techniques may be used, without limitation or restrictions, such as mapping different data types (e.g., using specification such as comma separated values (“csv”) to RDF, CSVW, among others) and storing these maps as graphs within a database or datastore (e.g., databases  104 - 106  and  114 - 118 ). Other graph generation techniques may be used and are not limited to any particular algorithm, process, or methodology. 
     In some examples, although a SQL-based query may have a SELECT statement (i.e., a programmatic query command or query statement intended to fetch an intended dataset or data stored within a given database), platform  102  may be configured to convert a statement (e.g., a query statement such as SELECT in SQL, and other comparable commands in any other type of query language, structured or unstructured) into SPARQL, for example, by parsing the query statement into a data structure such as an abstract syntax tree in an intended (i.e., target) language such as SPARQL. Once generated, an abstract syntax tree mapping a received query statement may be used to determine how to map the statement from its native language into a comparable statement in, for example, SPARQL (or another language that may be configured to perform the processes described herein). Using an abstract syntax tree (not shown) may be used to further generate a resultant SPARQL query statement, command, data structure, or object that may be configured to execute over (e.g., using) a triple store or triple data within a datastore, such as those described herein. Using attributes inferred from or stated in a originally-received (i.e., native) statement (e.g., SQL query statement, as described above as an example), triple data can be amassed in a triple store (i.e., a datastore, database, repository, or other type of data structure configured to store triple data reduced, atomically, as described herein) and used during the generation of a substantially equivalent statement (e.g., a query) into SPARQL. As an example, attributes may identify an access control condition (e.g., password, token, or other security feature that must be navigated successfully before access to a dataset or a database, data repository, datastore, or other type of data structure is permitted) that manages (e.g., controls) access to a target or intended dataset. For example, a password, token, hash value, or any other type of security-oriented attribute may be converted into one or more triples and, in some examples, an endpoint server (not shown) associated, in data communication, or configured to perform data operations with platform  102  may be used to rewrite the triple data of the query and the attribute into another form, format, language, structure, or schema for a target database that the endpoint server is configured to communicate with over one or more data networks. In some examples, platform  102  may be configured to receive a query, rewrite the data associated with the query and any attributes (e.g., attributes of the query, the target dataset(s), the target database(s), paths, linked data, or any other attribute including, but not limited to those examples provided above) into a language, structure, schema, or format associated with another database by converting query data (i.e., data associated with a query) and data associated with attributes of the queries into triples, execute the rewritten queries, and, in some examples, return not only the requested dataset(s), but also dataset(s) that may be related to the dataset(s). In other examples, platform  102  may be configured to return only the target dataset(s) requested by the query and no others. In still other examples, platform  102  may be configured to return some dataset(s) that may be associated with or related to the target dataset(s) requested by the query, which may be determined based on rules or logic of platform  102 . Further, platform  102  may also be configured to create or modify a graph (e.g., data model) that is used when a query for a given dataset is received, which may be further used to return additional data that could be valuable due to an attribute-determined relationship or association between the target dataset, the query, and other dataset(s) known or graphed or identified as linked data by platform  102 . The above-described topology, elements, and processes may be varied in size, shape, configuration, function, and implementation and are not limited to the examples shown and described. 
       FIG. 2  illustrates an exemplary system architecture for a platform for managing integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization. Here, system  200  is shown, including application  201  (in some examples, application  201  may be comparable in function and structure to platform  102  as described above in connection with  FIG. 1 ), data communication bus  202 , application programming interface or API (hereafter “API”)  204 , proxy/endpoint server  206  (which may also be referred to interchangeably as a “proxy,” “endpoint,” “proxy server,” “endpoint server”), logic module  210 , conversion module  212 , inference engine  214 , query engine  216 , display module  218 , databases  20 - 224 , and graph database engine  228 . Data elements transferred (i.e., received and sent) from application  201  may take various forms including, but not limited to query  203 , dataset  242  (which may be interchangeably referred to herein as a “target dataset(s)”), and rewritten query  244 . In some examples, system  102  may be an exemplary implementation of platform  102  ( FIG. 1 ). The elements shown and the configuration, structure, relative size of the elements, and functions described are not intended to be limiting and the sizes and shapes of the elements have no limitation or meaning apart from those provided within the detailed description of this specification or as claimed. 
     As shown, application  201  may be a implemented as a process, computer program, software, firmware, hardware, circuitry, logic, or a combination thereof (hereafter “application”) and, in some examples, may be written in Java® and/or JavaScript®, among others. Each of elements  201 - 228  may be programmed, developed, or encoded using software programming techniques familiar to these programming and formatting languages or others, without restriction, regardless of whether object-oriented, structured, or unstructured. In some examples, application  201  is configured with elements  202 - 228  in order to receive query  203  that is directed to retrieve (e.g., fetch, download, access and copy, or otherwise obtain using one or more data operations) a target dataset (e.g., dataset  242 ) in response to rewritten query  244 . As described herein, application  201  may be written in any programming or formatting language (e.g., SQL, Python, R, or others) used to query a database. Application  201  may be configured to receive query  203  using API  204  and analyzing, using logic module  210 , query  203  to determine one or more attributes associated with query  203 , dataset  242 , or a database (e.g., databases  104 - 106 , databases  114 - 118 , database  122 , and datastore  123  (including databases  124 - 128 ) as shown and described above in connection with  FIG. 1 ). Query  203  may be stored in a database configured to store query data (i.e., query data  224 ). Once stored, query  203  may be identified, in some examples, as a “master” of query  203 . A copy of query  203  may be made and also stored in one or more of databases  220 - 224  and used as a replica. In other words, a replica or copy (hereafter, “replica” and “copy” may be used interchangeably without restriction or limitation) may be used to perform various data operations such as those described herein rather than a master of query  203 , the latter of which may be preserved (i.e., stored) for later use to restore from an event that results in partial or full loss of the data in query  203 , whether due to corruption, catastrophe, or some other event that can cause a similar detrimental or destructive effect. In other examples, an original version of a query (i.e., the originally-received version of query  203 ) may be used by application  201 . 
     Here, in some examples, a replica of query  203  (not shown) or query  203  is parsed by logic module  210 , which is configured to analyze data received by application  201  (e.g., query  203 ) or dataset  242  and to generate instructions to other elements within application  201  to perform various data operations such as those described herein. Structurally, logic module  210  may be a set of logical rules or algorithms for machine learning, deep learning, artificial intelligence, or the like. Logic module  210  may be programmatically coded in one or more languages such as Java®, JavaScript®, R, or others, without limitation or restriction. Functionally, logic module  210  may be configured to perform various data operations such as generating data or signals to provide instructions to inference engine  214 , query engine  216 , or any other element of application  201 . Logic module  210  may also be configured to generate and send instructions (i.e., as data or signals) to graph database engine  228  in order to generate one or more data models associated with query  203 . Further, during parsing, inference engine  214  may be configured to determine attributes associated with query  203  through inference (e.g., Bayesian, statistical, probabilistic, predictive, or other techniques may be employed for inference and are not limited to any specific types of techniques for inferring attribute data associated with query  203 ). In some examples, attributes may include, but are not limited to, any type of information or characteristic associated with or about a query, dataset  242 , which is intended to be fetched by query  203  (i.e., using, for example, a SQL SELECT command to retrieve dataset  242  for a given database (not shown)), and the destination or target database from which dataset  242  is to be retrieved. While examples are provided for the disclosed techniques to operate on a singular dataset, these may also be extended to operate on multiple datasets and databases, without limitation or restriction. Attributes may include, but are not limited to, property attributes (e.g., string literal, numerical, or the like), values, qualities, characteristics, or any other data, metadata, and information about or related to an item contained within a dataset or a database and which can be inferred by inference engine  214 . Attributes, once inferred by inference engine  214  as a result of parsing being directed by logic module  210 , along with query  203  can be converted into “atomic” data or triples in accordance with languages, protocols, and formats such as the Resource Description Framework (hereafter “RDF”) as promulgated by the World Wide Web Consortium (hereafter “W3C”), SPARQL, and others used for organizing, formatting, programming, converting, structuring, or otherwise manipulating data for use on “semantic web” applications and the like, including semantic uses for retrieving dataset  242  from databases or the like or from other data networks that do not employ common data languages, formats, and protocols. By converting, for example, SQL-based data (or data for query  203  formatted using a structured or unstructured language) can be converted into RDF triple data that can be used as a common base language, format, or protocol that can later be used by query engine  216  and proxy/endpoint server  206  to “rewrite” or construct rewritten query  244 , which is ultimately transmitted from application  201  to a database for retrieving dataset  242 . In some examples, dataset  242  may be retrieved or fetched from a database using rewritten query  244  and may include not only dataset  242 , but also other datasets that might be related to or are similar to the dataset sought. 
     In some examples, the determination of whether dataset  242  may be related to other dataset(s) that were previously retrieved or otherwise indexed by application  201  and its elements (namely, graph database engine  228 , which may be configured to create a graph or data model representative of dataset  242  that were previously fetched (i.e., retrieved) and/or stored in one or more of databases  220 - 224 ) may be made by logic module  210 , query engine  216 , and graph database engine  228 . When query  203  is received, for example, logic module  210  analyzes inferred attribute data from inference engine  214  and can generate/send instructions to query engine  216  to reference graph database engine  228  in order to determine whether any of the triple data converted from query  203  and stored in one or more of databases  220 - 224  matches previously converted triple data stored similarly. Alternatively, a graph created of query  203  (or a copy thereof) or dataset  242  may also be stored in one or more of databases  220 - 224  and used as a reference for a comparison to another graph previously stored in databases  220 - 224  to determine if there is a match (i.e., where there are other datasets that may be related (and presumably of interest to a data scientist (i.e., user)) or similarity with dataset  242 . In other examples, a rule or set of rules that establish a percentage or numerical threshold may be input using logic module  210  (e.g., display module  218  may be configured to generate, by executing one or more scripts, forms, or formats such as HTML, XML, PHP, or the like) to provide a user interface that a data scientist or researcher (i.e., a user of platform  200 ) may use to input a rule, criteria, or restriction for use in determining whether there are any dataset(s) that may be similar to dataset  242 . In still other examples, users may enter other rules, criteria, or restrictions that permit or do not permit application  201  to return similar or matching datasets for presentation on a user interface (not shown) provided by display module  218 , which, working in concert with API, may receive and send (for display or visual rendering) data in various types of formats including, but not limited to HTML, XML, XHTML, or any other type of programming or formatting language that may be used to generate the user interface. 
     Referring back to inference engine  214 , any attributes inferred may be analyzed by logic module  210  and then converted into, for example, triple data (e.g., triple formats such as those described herein and in accordance with protocols such as SPARQL, RDF, among others, without limitation and/or restriction) that can be stored along with the triple data associated with query  203  itself; stored, that is, in one or more of databases  220 - 224 . Inference engine  214  may also be configured to infer attributes about a given dataset(s) such as layout (e.g., columns, rows, axes, matrices, cells, text, among others), data type (e.g., string literals, numbers, integers, fractions, decimals, whole numbers, and the like), but also exceptions (i.e., data that is inconsistent with inferred attributes or other data within a given dataset(s)). In some examples, when exceptions are found, display module  218  may be configured to visually present, render, or otherwise display, in various types of graphical user interface layouts (not shown), without limitation or restriction. In some examples, user interfaces may be presented that provide, in addition to data from a retrieved dataset(s), but also exceptions, annotations, outlier data, inferred attributes, attribute data, or others, using techniques that data scientists and researchers would be familiar with using (e.g., Python, R, and the like) without requiring in-depth or expert knowledge of programming languages underlying platform  102  (e.g., SPARQL, RDF, Java®, JavaScript®, among others). In some examples, one or more of databases  220 - 224  may be configured to store only triple data, while another database may be configured to store query  203  as a master (as previously described) or copies thereof in order to restore from a catastrophic loss or data corruption event. As an example, query  203  may be rejected by a target database (e.g., databases  104 - 106 , databases  114 - 118 , database  122 , and datastore  123  (including databases  124 - 128 ) as shown and described above in connection with  FIG. 1 ) or access control module  120  ( FIG. 1 )) because of a partial or complete corruption of data. A master or copy of query  203  may be retrieved by application  201  from one or more of databases  220 - 224  and used to generate another rewritten query (e.g., rewritten query  244 ) using triple data associated with query  203  and triple data associated with any attributes inferred by inference engine  214 , both of which may be stored in one or more of databases  220 - 224 . Likewise, dataset  242  or a copy thereof may also be stored in one or more of databases  220 - 224 . In some examples, attribute(s) determined from inference operations run against query  203  may also include an access control condition or data related thereto, such as a password, token, authentication key, private or public key, hash value, or any other type of data security mechanism. 
     In some examples, an access control condition, in some examples, as a type of attribute can also be converted by conversion module  212  into triple data that may be stored in one or more of databases  220 - 224 , one or all of which may be either local, remote (not shown), or distributed (local or remote) data storage facilities. In some examples, databases  220 - 224  may be standalone, server, network, or cloud-based data storage facilities and are not limited to the examples or configurations shown and described in connection with  FIG. 2 . 
     Referring back to conversion module  212 , data associated with query  203  (or a copy thereof) may be converted into triple data and stored in one or more of databases  220 - 224 , which may be later used to generate rewritten query  244  by, in some examples, proxy/endpoint server  206 . In some examples, proxy/endpoint server  206  may be implemented using multiple instantiations for different types, structures, formats, and data schema of databases, datastores, data warehouses, data repositories, or any other types of data storage facility(s). As shown, after query  203  has been converted into triple data that may be stored in one or more of databases  220 - 224  (and as further described above) and any inferred attributes determined by inference engine  214  have also been converted into triple data (which may likewise be stored in one or more of databases  220 - 224 ), proxy/endpoint server  206  and query engine  216  are configured to generate rewritten query  244  for each target database (not shown) on which dataset  242  is stored (e.g., as originally programmed using, for example, a SELECT statement in SQL) as well as any other dataset(s) that have been identified by logic module  210  as a result of analyzing graphs and/or data models generated by graph database engine  228  and/or those previously generated by graph database engine  228  and stored on one or more of databases  220 - 224  (i.e., identifying other datasets that may be similar to or match dataset  242 , or identifying isomorphic (i.e., data that is related to other data) amongst queried, retrieved, or linked dataset(s)). Further, logic module  210  may also limit, expand, or otherwise modify the number and type of dataset(s) retrieved in response to a fetch command or statement, depending upon rules or instructions provided by a user as received by API  204  and display module  218 . In still further examples, proxy/endpoint server  206  may include multiple instantiations, each of which is configured to generate multiple rewritten queries for different types, formats, structures, and/or data schemas for various databases (i.e., multiple versions of rewritten query  244 , where each version may be generated for different types of databases (e.g., Relational, Document-oriented, Key-value, Graph, or others), without limitation or restriction to any particular type, format, or data schema of database. The described techniques enable data scientists (e.g., users) to generate a request using a query language that can be parsed, analyzed, converted, and rewritten in order to support different types, formats, structures, and data schemas without having to manually rewrite each query for a specific type of database. Further, rewritten query  244  may be “optimized” such that data or metadata representing attributes inferred by inference engine  214  can also be included as triple data during the rewriting process (as described in further detail below) in order to include data or information that can not only fetch or retrieve dataset  242 , but also dataset(s) that may be useful, valuable, or otherwise related to the one sought by query  203 . Optimization may also include rewriting query  203  from one query language into triples, as discussed herein, and from the triples data into rewritten query  244  by proxy/endpoint server  206 , which may also include, during the rewriting process (as described in greater detail below) an access control condition (e.g., password, token, authentication data, encryption data, hash value, or other security data or information) from the converted triple data stored in databases  220 - 224  in order for rewritten query  24  to gain access to and retrieve from, for example, dataset  242  from a private (i.e., secure) network (e.g., network  112 , which may include access control module  120 , datastore  123 , and databases  122 - 128 ). In other examples, the above-described elements may be varied in size, shape, configuration, function, and implementation and are not limited to the descriptions provided. 
       FIG. 3  illustrates an exemplary layered architecture for platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization. Here, application stack  300  (hereafter “stack  300 ”) illustrates an exemplary layered architecture that may be used to implement application  201  ( FIG. 2 ), including application layer  302 , query layer  304 , linked data layer  306 , and data layer  308 . Stack  300  is neither a comprehensive nor fully inclusive layered architecture for developing an application or platform for managing integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization. 
     As shown, stack  300  includes application layer  302 , which may be the architectural layer at which application  201  ( FIG. 2 ) or platform  102  ( FIG. 1 ) is coded using, for example, Java®, JavaScript®, Ruby, C+, C++, C #, C, or any other structured or unstructured programming language. In some examples, data for coded functionality that is used to enable one or more of the elements shown and described in connection with  FIG. 2  may be transferred (i.e., sent, received), modified, executed, or otherwise operated on at this layer in the architecture of stack  300 . In other examples, application layer  302  may be implemented differently in the architecture of application  201 . 
     Query layer  304  is an exemplary layer of the architecture of application stack  300 , which may be an architectural layer at which query data is retrieved, analyzed, parsed, or otherwise used to transfer data for various computing operations associated with receiving query  203  ( FIG. 2 ) and generating rewritten query  244  ( FIG. 2 ) in order to retrieve dataset  242  ( FIG. 2 ). Query layer  304  may also be the layer in stack  300  at which API  204 , proxy/endpoint server  206 , conversion module  212 , inference engine  214 , query engine  216 , display module  218 , databases  220 - 224 , and graph database engine  228  receive data and signals generated from logic module  210  for performing various data operations (e.g., parsing, analyzing, converting, rewriting, and optimizing query  203  and rewritten query  244 , among others) on query  203 , dataset  242 , or rewritten query  244  prior to converting data associated with these data elements to triples (as described herein). In other examples, query layer  304  may be designed, configured, and implemented differently and is not intended to be limited nor restricted to the examples shown and described. 
     Here, linked data layer  306  may be an architectural data layer at which query  203  ( FIG. 2 ) can be analyzed and parsed by logic module  210  ( FIG. 2 ), from which graphs may be generated. Once graphs are generated, in some examples, linked data layer  306  is the architectural layer at which graph data (not shown) may be transferred (i.e., sent, received) or otherwise communicated between the various elements of application  201  ( FIG. 2 ). Further, graph data (i.e., data and metadata associated with graphs of linked data that are generated, stored, modified, or otherwise used by application  201  when rewriting and optimizing query  203  into rewritten query  244  (as described in greater detail below). 
     Here, triple data layer  308  is illustrative of an exemplary layer in the architecture of application  201  ( FIG. 2 ) at which “atomic” triple data has been converted from the native programmatic and/or formatting language of query  203  or another query received by application  201 . As discussed above, conversion module  212 , in some examples, converts data associated with query  203  into RDF or other forms of “atomic” triples data, which can be stored by platform  201  (e.g., in databases  220 - 224 ). As used herein, “atomic” may refer to a common conversion data format that, once converted, can be used to create various types of queries for datasets stored on different, inconsistent, or incongruous databases. Some examples of types of triple formats and protocols that may be used to convert query  203  include, but are not limited to RDF, SPARQL, R, Spark, among others. Once converted, triple data layer  308  is the layer at which triple data can be exchanged among the various elements of application  201  ( FIG. 2 ) from which rewritten query  244  can be created by proxy/endpoint server  206  ( FIG. 2 ) and query engine  216  ( FIG. 2 ) to create federated queries (i.e., rewriting query  203  for multiple inconsistent and non-congruous databases (as described herein) using disparate data communication and transfer protocols, query languages, data schema, data models, and the like. As used herein, “federated” may refer to the described techniques being used to generate, transmit, execute, and manage rewritten queries (i.e., multiple instances of rewritten query  244 ) for different databases in order to retrieve not only the originally-requested dataset of query  203 , but other dataset(s) that may be related to, associated with, or included for retrieval, regardless of the data type, format, structure, data schema, data model, graph, or other characteristics of the database on which the datasets (e.g., dataset  242 ) are stored. Further, any attributes determined by inference engine  214  are also converted by conversion module  212  ( FIG. 2 ) and stored in one or more of databases  220 - 224 , but may also be exchanged, transferred, modified, or otherwise operated upon at triple data layer  308  of stack  300  as shown in  FIG. 3 . In other examples, stack  300  and the various layers shown may be varied in structure, function, format, data type, data model, or other aspects and are not limited to the examples shown and described. 
       FIG. 4  illustrates an exemplary data flow for platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization. Here, data flow  400  includes query triple data  402 , attribute triple data  404 , query rewrite process  406 , rewritten query  408 , public datasets  410 - 412 , and private datasets  414 - 416 . In some examples, query triple data  402  and attribute triple data  404  are received as data inputs to query rewrite process  406 . Using converted triples (as described above) in, for example, RDF, query rewrite process  406  then generates rewritten query  408 , which is then directed by proxy/endpoint servers (e.g., proxy/endpoint server  206  ( FIG. 2 )) to one or more public and/or private databases that may be housed, stored, operated, distributed by, or otherwise logically accessible on one or more public and/or private data networks (not shown). In some examples, rewritten query  408  may be similar to rewritten query  244  ( FIG. 2 ) and, is converted by conversion module  212  ( FIG. 2 ) from triple-formatted data (e.g., query triple(s)  402  and attribute triple(s)  404 ) into the query language or format of a target dataset (e.g., dataset  242  ( FIG. 2 ), public datasets  410 - 412 , private datasets  414 - 416 , among others). Once rewritten query  408  is generated, it may be directed, transmitted, transferred, or otherwise executed as a query against one or more databases (not shown) storing public datasets  410 - 412  and private datasets  414 - 416 . The number, type, shape, and flow of data flow diagram  400  may be varied in process, steps, order, function, description, or other aspects, without limitation or restriction, and are not limited to the examples shown and described. 
       FIG. 5  illustrates an exemplary data operations model illustrating various processes for platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization. Here, data operations model includes query data  502 , attribute data  504 , query rewrite process  506 , rewritten query  508 , and processes for query copy/replication  510 , storage  512 , triple conversion  514 , endpoint query generation  516 , and endpoint query execution  518 . As shown, each of elements  502 - 518  may be a implemented as a process, computer program, software, firmware, hardware, circuitry, logic, or a combination thereof (hereafter “application”) and, in some examples, may be written in Java® and/or JavaScript®, or any other programming or formatting language, without limitation or restriction. Elements  502 - 518  may be programmed, developed, or encoded using software programming techniques familiar to these programming and formatting languages or others, without restriction, regardless of whether object-oriented, structured, or unstructured. In some examples, query data  502  and query attribute data  504  are input to query rewrite process  506 . Although not shown, query data  502  may be data that is inferred (by inference engine  214  ( FIG. 2 )) and converted into triples data (e.g., RDF triples) by conversion module  212  ( FIG. 2 ). Likewise, attribute data  504  may be triple data that is converted from inferred data generated from inference engine  214  regarding one or more characteristics associated with query  502  (e.g., query  203  ( FIG. 2 )). Using triple data associated with a query (e.g., query  502 , query  203  ( FIG. 2 )) and one or more attributes inferred from the query (i.e., by inference engine  214  ( FIG. 2 )), query rewrite process  506  may be an application, computer program, software, firmware, script, thread, multiple threaded program or application, distributed or cloud-based application, circuitry, logic, or a combination thereof, that is configured to generate a rewritten query (e.g., rewritten query  508 ) that may be executed against one or more databases. As proxy/endpoint server  206  ( FIG. 2 ) is configured to execute rewritten query  508  against a given database and other proxy/endpoint servers (not shown) can be implemented to also execute other instances or versions of rewritten query  508  for different databases, formats, protocols, languages, schema, data models, object models, or the like. In so doing, platform  102  ( FIG. 1 ) and application  201  ( FIG. 2 ) can generate, execute, and manage multiple queries similar to a federated system by directing each rewritten query (i.e., rewritten query  508 ) to a proxy/endpoint server  206  that is configured or scripted to generate and execute a query (e.g., query  508 ) for a given query language or protocol (e.g., SQL, SPARQL, XPath, MDX, LDAP, Datalog, CQL, and various other structured or unstructured languages or protocols, without limitation or restriction). Some of the processes and data operations that support this functionality are shown and described herein connection with  FIG. 5 . 
     In some examples, query copy/replication  510  may be a process that is implemented by application  201  ( FIG. 2 ) and configured to replicate or copy (hereafter, “replicate” and “copy” may be used interchangeably to the generation of a copy or replica of a query (e.g., query  203  ( FIG. 2 )), dataset (e.g., dataset  242  ( FIG. 2 )), rewritten query (e.g., rewritten query  244  ( FIG. 2 )), linked data graph (i.e., “graph”), object model, data model, or any other type of data instance that may be used, manipulated, modified, deleted, generated, created, or otherwise operated upon by application  201 . Further, query copy/replication  510  may be implemented as a process that occurs before, during, after, or as a part of query rewrite  506 . In some examples, query copy/replication  510  may be also be performed in parallel or serial with other processes or threads (e.g., storage  512 , triple conversion  514 , endpoint query generation  516 , endpoint query execution  518 , among others). In other examples, query/copy replication  510  may be designed, implemented, configured, or otherwise executed differently and is not limited to the examples shown and described. 
     When a replica is generated by query copy/replication  510 , in some examples, storage  512  may be configured to run or execute as a process to store a generated copy of a query and the original query (i.e., master) in one or more databases associated with application  201  ( FIG. 2 ) and as described above. Other data, including inferred data such as attribute or characteristic data, graphs, linked data, graph data, and the like may also be stored and retrieved using storage  512 . As described previously, databases may include any type of data storage facility that is configured to physically, virtually, logically, or otherwise work with application  201  in a standalone, hosted, distributed, or cloud-based configuration. 
     Here, triple conversion  514  may be implemented as, for example, a process configured to convert query data into triples (e.g., RDF triples, items that are subject-predicate-object oriented, or another atomic format apart from those described herein). Data associated with a query may include query data received and parsed directly from, for example, query  203  ( FIG. 2 ) or other data associated with characteristics or attributes of a query that may be inferred by inference engine  214  ( FIG. 2 ). Triple data, once converted from query or attribute data, in some examples, may be stored in a similar manner using a process similar to that described above in connection with copy/replica storage  512 . Triple data (e.g., query  502 , attribute  504 ) may be used by query rewrite  506  to construct and generate rewritten query  508 , which can be converted back from a triples-based format (e.g., RDF, or others) into another structured or unstructured data query language (e.g., SQL, SPARQL, and others) by an endpoint server (e.g., proxy/endpoint server  206  ( FIG. 2 )) that is configured to communicate with a given database, datastore, data network, or the like using, for example, endpoint query generation  516  as a process for doing so. For example, endpoint query generation  516  may be a process or set of processes used by application  201  ( FIG. 2 ) as an instance running on proxy/endpoint server  206  and which is configured to execute a query using endpoint query execution  518  as a process or set of processes to do so. Rewritten queries (e.g., rewritten query  508 ) may be executed using endpoint query execution  518  as a process or set of processes that are configured to execute (i.e., run) against any public or private data network or secure data network such as those provided by Data.Gov, the U.S. Department of Defense, the National Institutes of Health, or other private, corporate, academic, non-profit, or other types of organizations or entities that have datasets. In some examples, application  201  and graph database engine  228  may be configured to generate, store, and modify graphs of linked data as datasets are identified by platform  102  ( FIG. 1 ). 
     Here, some data networks may utilize SQL as a primary data storage and query language while others may use DMX for data mining purposes, and still others may use LDAP for querying services run over Transport Control Protocol/Internet Protocol (i.e., “TCP/IP”). In still other examples, proxy/endpoint server  206  may use different query languages and the processes described herein such as triple conversion  514 , endpoint query generation  516 , and endpoint query execution  518  are not limited to any particular language or version thereof. In other examples, the above-described processes may be designed, implemented, configured, or otherwise executed differently and are not limited to the examples shown and described. 
       FIG. 6A  illustrates an exemplary process flow for platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization. Here, process  600  begins by receiving a query ( 602 ). Once received a copy (i.e., replica) is generated and a graph is created by, for example, graph database engine  228  ( FIG. 2 ) ( 604 ). Once created, the original query (e.g., query  203  ( FIG. 2 )) may be stored as a master and the copy may also be stored, but in the same or a different location (i.e., in a different database). Further, any newly-generated or modified graphs and graph data may also be stored, either in the same, similar, or a different location than that of the master and the copy of query  203 . Subsequent to generating the copy and the graph, process  600  may include parsing a copy of the query ( 606 ). Further, inference engine  214  ( FIG. 2 ) may be directed by control data or signals from logic module  210  ( FIG. 2 ) to determine and identify any attributes (i.e., characteristics) associated with a query, the queried (i.e., requested) dataset(s), any linked data that might suggest other datasets previously determined and identified to be related or similar to the data in the requested dataset ( 608 ). A determination is made as to whether any inferred attributes indicate whether there is an access control condition present, such as those described above ( 610 ). If no access control condition is found amongst the inferred attributes, then a rewritten query is generated by converting any query data and inferred attribute data into triples using a format such as RDF and then used to construct rewritten queries that can be formatted for specific types and query languages by proxy/endpoint servers (e.g., proxy/endpoint server  206  ( FIG. 2 ) that are configured to be in data communication with various data networks ( 612 ). 
     Alternatively, in some examples, if an access control condition (e.g., such as those described above) is determined by inference engine  214  ( FIG. 2 ), then the access control condition and the query data are converted into triples (as described herein) ( 614 ). The triple data is then used to generate a rewritten query (e.g., rewritten query  244  ( FIG. 2 ), rewritten query  508  ( FIG. 5 )) that includes both the query and the access control condition ( 616 ). Once a query has been rewritten from triple data, regardless of whether an access control condition is inferred to be present among the attribute data of the original query, the rewritten query is directed to a given proxy/endpoint server (e.g., proxy/endpoint server  206  ( FIG. 2 ) which converts the triples data into a language(s) and format(s) for the target or destination data network and database ( 618 ) after which process  600  ends. In some examples, rewritten queries having access control conditions are sent to private data networks to obtain datasets housed (i.e., stored) within (i.e., private datasets) and rewritten queries without access control conditions may be sent to public data networks to obtain datasets housed within (i.e., public datasets). 
     Alternative processes may be implemented other than the examples shown and/or described. For example, an alternative process may be included to parse a query to identify its various components and then determine what datasets are desired (i.e., targeted) for access. Once determined, the targeted dataset(s) can be evaluated further by inferring any attributes such as access control conditions. Access control conditions inferred may include, but are not limited to, checking token-based access controls for each targeted dataset and, if an access control condition or attribute indicates access is not authorized by data within the query, it is rejected and data is transmitted back to the user for display via, for example, display module  218  ( FIG. 2 ). However, if a query does have an inferred attribute that is an access control condition that authorizes access, then a rewritten query may be generated at each proxy/endpoint server (e.g., proxy/endpoint server  206 ), which each represent an internal endpoint that is configured to transfer data with a given database engine (i.e., database or data network on which a target dataset is stored). Subsequently, rewritten queries or those parts of a rewritten query that differ due to the query language or format of a given destination database engine, database, datastore, or data network, may be sent to graph database engine  228  ( FIG. 2 ) for updating one or more stored graphs associated with the original query (e.g., query  203  ( FIG. 2 )) or other graphs. In other words, process  600  and alternative processes such as those described above may be performed in order to enable, for example, proxy/endpoint server  206  ( FIG. 2 ) to “issue” federated pieces of a query to internal graph database engines such as graph database engine  228  ( FIG. 2 ). As used herein, “federation” may refer to an overall process or set of processes or techniques that are used to generate, manage, receive responses to, graph, track, and perform other processes related to executing a query against multiple incongruous and non-contiguous databases, database engines, or data, generally, of different formats, languages, structures (or lack thereof), and the like, while managing integrated and consolidated retrieval (e.g., fetch) of requested datasets in response to the query. 
     In other examples, the above-described process may be varied in function, order, procedure, and process, without limitation to any of the examples or accompanying descriptions. 
       FIG. 6B  illustrates a further exemplary process flow for platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization. Here, process  620  illustrates exemplary processes for managing query copies and masters and initiates by generating a copy of a query and creating a graph and graph data associated with the query and its copy ( 604 ). In some examples, process  620 , for copies of queries, identifies the copy as a replica ( 622 ), identifies a database or datastore for storing the replica ( 624 ), updates the graph associated with the query to identify (i.e., through the use of metadata, tags, markers, or other elements that can be used to discretely distinguish a copy from a master) the copy or replica to be used for further data operations to be performed, for example, by platform  102  ( FIG. 1 ) and/or application  201  ( FIG. 2 ). Further, after updating the graph and graph data, the copy is made available for parsing by, for example, logic module  210  ( FIG. 2 ) or the other elements of application  201 . 
     Running as parallel processes to those used for handling query copies as described above, in some examples, a query may be identified as a master ( 630 ). Once identified, a database or datastore in data communication with application  201  ( FIG. 2 ) is identified to store the master ( 632 ). Examples of databases or datastores that may be used to store a master are databases  220 - 224  ( FIG. 2 ) or those described above in connection with platform  102  and  FIG. 1 . After identifying a database or datastore in which to store the master, the graph generated for the query is updated with the stored location of the master and the stored location of the dataset(s) to be retrieved (i.e., fetched) ( 634 ). After inferring this information (e.g., by running inference engine  214  against a master), the master is stored in the previously-identified database or datastore ( 636 ). In other examples, the above-described process may be varied in function, order, procedure, and process, without limitation to any of the examples or accompanying descriptions. 
       FIG. 6C  illustrates another exemplary process flow for platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization. Here, process  640  initiates (i.e., starts) by receiving a copy of a query from process  628  ( FIG. 6B ) ( 642 ). Once received, the copy is parsed by, for example, logic module  210  and one or more of elements  204 - 228  ( FIG. 2 ) ( 644 ). Before, during, or after parsing (despite the exemplary process  640  illustrating parsing occurring beforehand), inference engine  214  ( FIG. 2 ), for example, is invoked in order to determine whether any attributes and/or attribute data associated with the query can be determined from the copy of the query ( 646 ). A determination is then made to determine whether an access control condition may be present amongst the inferred attribute(s) and/or attribute data (i.e., as inferred by, for example, inference engine  214  ( FIG. 2 )) ( 648 ). If an access control condition is determined to be amongst the inferred attributes and/or attribute data, then the access control condition is identified for conversion to a triple data format (such as those described herein (e.g., RDF, SPARQL, subject-predicate-object)) ( 650 ). Once identified, the attributes and/or attribute data are stored in, for example, a database or datastore used by application  201  ( FIG. 2 ), along with links in an updated graph (i.e., a data model of the query), which link the copy of the query and the master to the attribute(s) and/or attribute data ( 652 ). Alternatively, if no access control condition (as described in detail above) is found, then any attribute(s) and/or attribute data is stored with links in an updated graph, which link the copy of the query and the master to the attribute(s) and/or attribute data ( 652 ). In other examples, the above-described process may be varied in function, order, procedure, and process, without limitation to any of the examples or accompanying descriptions. 
       FIG. 6D  illustrates an additional exemplary process flow for platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization. Here, process  660  starts by initiating a rewriting process, script, thread, application, software, firmware, or the like, which has been configured to generate rewritten queries (e.g., rewritten query  244  ( FIG. 2 ), rewritten query  508  ( FIG. 5 )) using a proxy/endpoint server (e.g., proxy/endpoint server  206  ( FIG. 2 )) ( 662 ). In some examples, application  201  ( FIG. 2 ) may have one or more proxy/endpoint servers, each of which has been configured to rewrite a query by converting triple data into another data format for a query language used by a given data network and dataset. In some examples, the given data network and dataset may be those originally targeted by a query (e.g., query  203  ( FIG. 2 )). In other examples, a given data network and dataset may be different than those originally targeted by a query, but which may be determined to be related or similar to, associated with, or linked through analysis of a graph or graph data; the analysis being performed by, for example, graph database engine  228  ( FIG. 2 ). 
     Referring back to  FIG. 6D , a copy of a query and any inferred attributes or attribute data are identified for rewriting ( 664 ). More specifically, a copy of a query and inferred attributes and/or attribute data has been converted into triple data, as described above. Once identified, triple data and query data can be evaluated by logic module  210  ( FIG. 2 ) to identify or determine whether an access control condition is an attribute of the query, the dataset, or the data network on which the dataset is stored and, if so, identifying the access control condition for inclusion in a rewritten query (e.g., rewritten query  244  ( FIG. 2 ), rewritten query  508  ( FIG. 5 )) ( 666 ). Next, the copy of the query is converted (as part of the rewriting process) with any attributes or attribute data or access control conditions into triple data in accordance with a second data format (e.g., RDF, SPARQL, or the like) apart from that of the first data format of the original query (e.g., query  203 ). Once converted, the triple data is stored in a triple store (e.g. a datastore configured to store triple-formatted data (e.g., RDF), one or more of databases  220 - 224  ( FIG. 2 ), or the like)) and control data and/or signals may be sent from conversion module  212 , query engine  216 , or logic module  210  to one or more proxy/endpoint servers (e.g., proxy/endpoint server  206  ( FIG. 2 )) to indicate that query  203  has been rewritten and is available for further query rewriting by an endpoint server for a given data network and/or database on which the requested dataset is stored (or on which linked datasets are stored, which may be retrieved and presented for display to a user (e.g., data scientist, researcher, scientist, academic researcher, or any other user or consumer of data using platform  102  ( FIG. 1 )) ( 670 ). In other examples, the above-described process may be varied in function, order, procedure, and process, without limitation to any of the examples or accompanying descriptions. 
       FIG. 6E  illustrates yet a further exemplary process flow for platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization. Here, process  680  starts by initiating execution of a rewritten query (e.g., rewritten query  244  ( FIG. 2 ), rewritten query  508  ( FIG. 5 )) ( 682 ). Next, a target dataset (e.g., dataset  242  ( FIG. 2 )) is identified for retrieval (i.e., fetch) ( 684 ). In some examples, a first determination is made as to whether a target dataset is being stored on a public (i.e., publicly-accessible, open, or access is not subject or dependent upon an access control condition such as those described above) or private (secure or subject to authorization or authentication, as described herein) data network ( 686 ). If the target dataset is stored on a private data network, then another determination is made as to whether an access control condition is required to access the target dataset ( 688 ). For example, although a given dataset may be hosted (i.e., stored, reposited, or otherwise housed) on a private data network, there may be an access control condition required to access both a private data network and a private dataset. In other examples, a private dataset may be hosted on a public network and, although an access control condition is not required to access the public data network, an access control condition may be required to access a private dataset stored thereon. While this example is not illustrated, it is neither limited nor restricted from the scope of the techniques discussed herein. 
     Referring back to  FIG. 6E , if an access control condition has been detected or otherwise determined to be required for a private data network by, for example, inference engine  214  ( FIG. 2 ) (i.e., based on inferring attributes or attribute data associated with a query (e.g., query  203 )), then access to a private data network and a dataset may each require an access control condition, as described above. An access control condition (i.e., authenticating access to a private dataset) may be performed by including triple data associated with an access control condition to be converted and also included in a rewritten query (e.g., rewritten query  244  ( FIG. 2 ), rewritten query  508  ( FIG. 5 )). Finally, upon completion of rewriting a query, as described above, a rewritten query may be executed by transmission from a proxy/endpoint server (e.g., proxy/endpoint server  206 ) to either a destination data network on which a target dataset is stored or to another data network(s) on which dataset(s) that may be linked to the requested dataset may also be stored, and retrieving the requested and/or linked dataset(s) (i.e., linked datasets may be those that are identified as being linked to a requested dataset due to linkages that are identifying in a linked data model such as a graph or graph data, which are generated, stored, indexed, and otherwise managed by graph database engine  228  ( FIG. 2 ) ( 692 ). 
     In other examples, a public dataset may be stored on a public network and, if no access control condition is required, then platform  102  ( FIG. 1 ) and/or application  201  ( FIG. 2 ) and the elements described therewith may be configured to retrieve a requested and/or linked dataset(s) or a copy thereof. In other examples, the above-described process may be varied in function, order, procedure, and process, without limitation to any of the examples or accompanying descriptions. 
       FIG. 7A  illustrates an alternative exemplary process flow for platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization. Here, process  700  is initiated (i.e., starts) by receiving a query formatted or programmed in a first data schema (e.g., SQL) ( 702 ). A copy of the received query is generated ( 704 ) and then parsed ( 706 ). Resultant from the parsing, attributes are inferred (i.e., identified) by using various types of inference methods, techniques, and algorithms, some of which have been described herein ( 708 ). After identifying attributes associated with the query, a copy of the query data is rewritten into a second data format (e.g., RDF). Once converted into the second data format, the converted data (e.g., triple data) may be stored in a triple store for further rewriting and optimization ( 712 ). As used herein, “optimization” may refer to one or more actions that are taken during the generation of a rewritten query when, in addition to triple data associated with the original query, other data associated with inferred attributes such as access control conditions are also included (or the converted triple data associated with the inferred attributes and access control condition(s)) in a rewritten query, which may be generated by converting the triple data into a third data format, which may be the same, a similar, or a different data format than that of the original query ( 712 ). In other examples, the above-described process may be varied in function, order, procedure, and process, without limitation to any of the examples or accompanying descriptions. 
       FIG. 7B  illustrates a further alternative exemplary process flow for optimizing rewritten queries using platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization. Here, process  720  is a further detailed process or sub-process for optimizing a rewritten query as described above in connection with process step  712  ( FIG. 7A ). In some examples, triple data configured (e.g., tagged, marked, encoded, or otherwise identified) for a given rewritten query (e.g., rewritten query  244  ( FIG. 2 ), rewritten query  508  ( FIG. 5 )) is received from step  710  ( FIG. 7A ) ( 722 ) and an optimization process is initiated when data or signals are sent from query engine  216  or conversion module  212  to logic module  210  ( FIG. 2 ) to indicate that triple data has been received ( 724 ). As used herein, triple data received in step  722  may be associated with a query (e.g., query  203  ( FIG. 2 ) or a copy of a query (not shown)) and/or any inferred attributes or attribute data determined by inference engine  214  ( FIG. 2 )). 
     Referring back to  FIG. 7B , in some examples, a database engine intended to execute a rewritten query (i.e., the target of an originally-received query (e.g., query  203  ( FIG. 2 )) from platform  102  ( FIG. 1 ) or application  201  ( FIG. 2 ) may be identified ( 726 ). A database engine, in some examples, is identified as being assigned to execute queries for the target dataset(s) and to execute any access control conditions or mechanisms, if any. As used herein, a database engine may also refer to a data server or group of data servers, a data network, a datastore, or any type of database management system that is configured to manage the storage resource facility on which the queried or requested dataset is stored. Here, data or metadata is used to identify an “optimal” path from a proxy/endpoint server (e.g., proxy/endpoint server  206  ( FIG. 2 ) to a target dataset(s) ( 728 ). As used herein, “optimal” may be used interchangeably with “best” or “least worst” to identify a path between platform  102  ( FIG. 1 ) and a database engine configured to execute a query requesting data (e.g., executing a FETCH statement) to retrieve a given (i.e., target, targeted, requested, or queried) dataset. More specifically, an optimal path between platform  102  and a target dataset(s) may be a path graphed as a series of nodes from proxy/endpoint server  206  ( FIG. 2 ) to a database engine configured to execute a query request to retrieve (e.g., FETCH in SQL, or the like) a target dataset(s). In some examples, an optimal path may be one that includes the least number of network nodes (e.g., servers, central offices, logical modules or nodes, endpoints, or the like) between proxy/endpoint server  206  and the target dataset. In other examples, an optimal path may be one that is defined by the least number of “hops” between nodes, topologically. In still other examples, an optimal path may be one that is determined based on the lowest level of latency in terms of data transmission to and from platform  102 . In yet other examples, an optimal path may be determined based on real-time assessments of network and network equipment outages. In still further examples, an optimal path may also include nodes or network endpoints that are within the data network served by the database engine identified as being configured to execute a query to retrieve a target dataset(s). In yet other examples, an optimal path may be determined differently and is not limited to the examples provided herein. Data describing, defining, determining, or otherwise identifying an optimal path (i.e., path) may include data and/or metadata in any form or format, including, but not limited to XML, R, RDF, text, HTML, or any other type of programming or formatting language that may be used to generate data and metadata (i.e., information that is used to describe, characterize, attribute, or otherwise annotate data), without limitation or restriction. 
     Referring back to  FIG. 7B , data and/or metadata that identifies a path between, for example, proxy/endpoint server  206  ( FIG. 2 ) and a target dataset(s), may be converted into triple data in accordance with a second data schema ( 730 ). The converted triple data for the path, along with converted triple data for the query and any attributes or attribute data, may be retrieved by application  201  and used, by one or more elements (e.g., proxy/endpoint server  206 , logic module  210 , conversion module  212 , query engine  216 , among others) to generate a rewritten query by converting the triple data into another data schema that is used by a database engine in a destination data network on which a target dataset(s) or a linked dataset(s) is stored ( 734 ). Once generated, a rewritten query (e.g., rewritten query  244  ( FIG. 2 ), rewritten query  508  ( FIG. 5 )) may be executed by proxy/endpoint server  206  and application  201  ( FIG. 2 ). In other examples, the above-described process may be varied in function, order, procedure, and process, without limitation to any of the examples or accompanying descriptions. 
       FIG. 8  illustrates an exemplary computer system suitable for platform management of integrated access to public and privately-accessible datasets utilizing federated query generation and schema rewriting optimization. In some examples, computer system  800  may be used to implement computer programs, applications, methods, processes, or other software to perform the above-described techniques. Computer system  800  includes a bus  802  or other communication mechanism for communicating information, which interconnects subsystems and devices, such as processor  804 , system memory  806  (e.g., RAM), storage device  808  (e.g., ROM), disk drive  810  (e.g., magnetic or optical), communication interface  812  (e.g., modem or Ethernet card), display  814  (e.g., CRT or LCD), input device  816  (e.g., keyboard), and cursor control  818  (e.g., mouse or trackball). 
     According to some examples, computer system  800  performs specific operations by processor  804  executing one or more sequences of one or more instructions stored in system memory  806 . Such instructions may be read into system memory  806  from another computer readable medium, such as static storage device  808  or disk drive  810 . In some examples, hard-wired circuitry may be used in place of or in combination with software instructions for implementation. 
     The term “computer readable medium” refers to any tangible medium that participates in providing instructions to processor  804  for execution. Such a medium may take many forms, including but not limited to, non-volatile media and volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as disk drive  810 . Volatile media includes dynamic memory, such as system memory  806 . 
     Common forms of computer readable media includes, for example, floppy disk, flexible disk, hard disk, magnetic tape, any other magnetic medium, CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, or any other medium from which a computer can read. 
     Instructions may further be transmitted or received using a transmission medium. The term “transmission medium” may include any tangible or intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such instructions. Transmission media includes coaxial cables, copper wire, and fiber optics, including wires that comprise bus  802  for transmitting a computer data signal. 
     In some examples, execution of the sequences of instructions may be performed by a single computer system  800 . According to some examples, two or more computer systems  800  coupled by communication link  820  (e.g., LAN, PSTN, or wireless network) may perform the sequence of instructions in coordination with one another. Computer system  800  may transmit and receive messages, data, and instructions, including program, i.e., application code, through communication link  820  and communication interface  812 . Received program code may be executed by processor  804  as it is received, and/or stored in disk drive  810 , or other non-volatile storage for later execution. In other examples, the above-described techniques may be implemented differently in design, function, and/or structure and are not intended to be limited to the examples described and/or shown in the drawings. 
     Although the foregoing examples have been described in some detail for purposes of clarity of understanding, the above-described inventive techniques are not limited to the details provided. There are many alternative ways of implementing the above-described invention techniques. The disclosed examples are illustrative and not restrictive.