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    RDFox provided by graph.build

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    Sold by: graph.build 
    Deployed on AWS
    High performance knowledge graph and semantic reasoning engine.

    Overview

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    RDFox is the first market-ready knowledge graph designed from the ground up with reasoning in mind. RDFox is a main-memory data store that allows users to efficiently manage graph-structured data represented according to the RDF data model and query that data using the SPARQL 1.1 query language.

    RDFox also enables intelligent information processing by providing means for representing and reasoning with domain knowledge in the form of rules and ontologies. Rules in RDFox can be represented using an extension of the Datalog language, whereas ontologies can be represented in the standard OWL 2 language and in the Semantic Web Rule Language, SWRL.

    Oxford Semantic Technologies' RDFox product is supported on the AWS Marketplace by Data Lens Labs Ltd.

    This offering can only be used for non-production environments. For questions related to full production licenses via a Private Offer, please contact us at https://www.data-lens.com/#contact .

    Highlights

    • Best-in-class query performance via the W3C Semantic Web Standards SPARQL query language.
    • Reasoning over OWL ontologies and rules.
    • Incremental materialisation over ontologies and rules.

    Details

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    Delivery option
    RDFox

    Latest version

    Operating system
    Linux

    Deployed on AWS
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    Pricing

    RDFox provided by graph.build

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    Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time. Alternatively, you can pay upfront for a contract, which typically covers your anticipated usage for the contract duration. Any usage beyond contract will incur additional usage-based costs.
    Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator  to estimate your infrastructure costs.

    Usage costs (1)

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    Dimension
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    Cost/unit/hour
    Hours
    Container Hours
    $4.11

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    We do not currently support refunds, but you can cancel at any time.

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    Usage information

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    Delivery details

    RDFox

    Supported services: Learn more 
    • Amazon ECS
    • Amazon EKS
    • Amazon ECS Anywhere
    • Amazon EKS Anywhere
    Container image

    Containers are lightweight, portable execution environments that wrap server application software in a filesystem that includes everything it needs to run. Container applications run on supported container runtimes and orchestration services, such as Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS). Both eliminate the need for you to install and operate your own container orchestration software by managing and scheduling containers on a scalable cluster of virtual machines.

    Version release notes

    Added support for geospatial search within Lucene data sources (RD-2529). See Section 7.2.4.1 for details. Due to the introduction of a new tuple table parameter, sort, to support this change, persistent servers initialized with RDFox v7.5 or v7.4 that have one or more Lucene data sources registered cannot be restarted with v7.6. To upgrade to v7.6, first deregister all Lucene data sources and compact the affected data stores in the lower release before shutting down the server and restarting with v7.6.

    Added a built-in function GEODIST for geospatial distance calculations (RD-2538). See Section 9.2.8 for details.

    The * and ? wildcards are now expanded in accordance with standard file globbing rules when encountered within file path arguments to the import, evaluate, update, and answer shell commands (RD-2521). See Section 15.5 for details.

    Added new endpoint parameters request-time-limit and allow-request-time-limit-override to control the time limits for processing non-query requests (RD-2479). See Section 19.2 for details.

    API logging can now be enabled, disabled, and reconfigured without restarting the server (RD-2525). See Section 20.1 for details.

    Component information measurements for data store state (persisted, offline, or locked), license expiry, last auto-compaction statistics, and dead fact removal statistics - all previously exported as UNCATEGORIZED - now have individual metrics ensuring accessibility via the OpenMetrics format (RD-2409). See Section 16.5.7 for the full list of supported metrics.

    ASK queries are now mapped to SELECT WHERE { ... } LIMIT 1 queries to improve performance (RD-2542).

    Optimized the performance of SELECT COUNT(DISTINCT ?x) queries over a single tuple pattern (RD-2524).

    Added millisecond-precision to some timestamps, such as the tstamp shell command, the startup time in the serverinfo shell command, and the timestamps in the HTTP request loggers (RD-2407).

    Ensured that the file names created by the API and HTTP loggers include the server instance ID when file-sequence persistence is used (RD-2530). This makes it possible to distinguish between log files created by different replicas in a HA setup.

    Improved the error message returned when a tuple table exceeds the maximum tuple capacity (RD-2545).

    Removed the dir.dlog, dir.facts, dir.queries, dir.scripts, and dir.stores Shell variables (RD-2521). Any paths that would resolve against one of these variables will now resolve against the dir.root variable instead.

    Added support for the query OIDC response mode (RD-2522). This is used if the authentication server does not support the fragment response mode or if the new server parameter oidc.force-query-response-mode server parameter is set to true. See Section 12.3.2 for details.

    Block data source tables being created with parameter columns that are unused (RD-2546).

    Updated the SEMSIM function to truncate inputs exceeding the token limit - determined as the the smaller of the model's context size and the value of the new RDFOX_EMBEDDING_MAX_NUMBER_OF_TOKENS environment variable, if set - instead of returning UNDEF (RD-2563).

    SQLite has been added to the official Docker images.

    Fixed a possible hang when multiple compressed files are read from or written to in parallel (RD-2553).

    Fixed a bug in the implementation of the allow-query-time-limit-override endpoint parameter, where despite the parameter being set to false or no, the Query-Time-Limit HTTP header could still be used to override the query time limit (RD-2479).

    Fixed a bug in the ODBC data source connector, where the quoted identifier parameters (quoted-identifier-quote, quoted-identifier-quote-open, and quoted-identifier-quote-close) were not fully applied to SQL queries (RD-2486).

    Fixed an issue where endpoint parameters supplied when starting the server in sandbox or shell mode could be incorrectly parsed due to an extra layer of quoting/escaping being applied (RD-2551).

    Fixed incorrect "Missing value for key" errors when overriding data store parameters with the dstore load shell command (RD-2505).

    Fixed an issue where the presence of an unbound variable in a query's SELECT list prevented ORDER BY from sorting results (RD-2500).

    Fixed an issue where DISTINCT was not correctly enforced for subselects containing BIND(?var1 AS ?var2) (RD-2537).

    Fixed an issue where array values for the aud claim in an access or ID token were rejected (RD-2531).

    Fixed a rare issue with loading datastores that contain non-RDF data (e.g. rules, axioms, or delta queries) exceeding 64KB (RD-2558).

    Fixed a cleanup failure in the rwtest shell command that could leave a data store in an invalid state and cause the next operation on that data store to crash (RD-2565).

    Resources

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    Support

    Vendor support

    UK business hours support please allow 24 hours https://www.data-lens.com/#contact . We do not currently support refunds, but you can cancel at any time.

    AWS infrastructure support

    AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.

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    Overview

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    AI generated from product descriptions
    SPARQL Query Language Support
    Supports SPARQL 1.1 query language for querying graph-structured data according to W3C Semantic Web Standards with best-in-class performance.
    Ontology and Rule Reasoning
    Enables reasoning over OWL 2 ontologies and Semantic Web Rule Language (SWRL) for intelligent information processing and domain knowledge representation.
    Datalog-based Rule Representation
    Supports rule representation using an extension of the Datalog language for expressing domain knowledge and logical constraints.
    Incremental Materialization
    Provides incremental materialization capabilities over ontologies and rules to efficiently compute and update derived facts.
    In-Memory RDF Data Store
    Main-memory data store designed to efficiently manage and query graph-structured data represented according to the RDF data model.
    Graph Database Compatibility
    Supports Amazon Neptune and any SPARQL 1.1 compliant graph database
    RDF Data Management
    Provides UI for data loading, ontology library, SPARQL query interface, query catalog, and data quality dashboard for RDF data and ontology management
    Web Component Framework
    Includes pre-built Web components for search, exploration, authoring, editing, visualization, and graph data interaction that can be configured and combined for rapid development
    Knowledge Graph Lifecycle Support
    Supports authoring, curating, editing, exploring, integrating, searching, and visualizing of Knowledge Graphs
    FAIR Data Compliance
    Implements FAIR Data principles for Knowledge Graph management and enterprise data governance
    Multi-Model Database Management
    Supports both relational tables and RDF graphs within a single integrated database management system
    Query Language Support
    Provides support for SQL, SPARQL, GraphQL, ODBC/JDBC, HTTP, and MCP protocols for data access and integration
    Data Virtualization and Replication
    Includes advanced data virtualization, replication, and integration capabilities for managing distributed data sources
    Access Control
    Implements fine-grained, attribute-based access controls for secure data management
    LLM Integration Infrastructure
    Delivers infrastructure for developing and deploying Large Language Model-based AI Agents and Assistants with loose coupling to data spaces including databases, knowledge graphs, filesystems, and APIs

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