Connect Data Silos Across the Enterprise
Today’s data landscape is increasingly complex, varied, and distributed. The emergence of IoT, rise in unstructured data volume, and increasing frequency and complexity of analytics requests all contribute to the need for a more flexible data management solution. In order to keep up with enterprise demands for data and analytics, it’s necessary to use tools that are designed to flexibly integrate data sources for varying purposes. Stardog, the leading Enterprise Knowledge Graph platform, lets you connect data silos, no matter their structure, velocity, or location.
High-performance graph database
Stardog’s Enterprise Knowledge Graph platform is underpinned by a graph database that offers state-of-the-art performance for both storing RDF data and executing SPARQL queries. Stardog collects detailed statistics from the graph structure to compute accurate cardinality estimations for Stardog’s sophisticated query optimizer. Our performance is tested daily on many publicly available benchmarks such as BSBM (Berlin SPARQL Benchmark), SP2B (SPARQL Performance Benchmark) and LUBM (Lehigh University Benchmark), along with many custom benchmarks developed by the Stardog team based on real-world workloads from our users.
Powerful Virtual Graphs For The Enterprise
Virtualization is known for being a flexible alternative to typical data integration, which relies upon ETL and permanent transformation of source data. However, data virtualization software is typically based on relational data structure, which doesn’t lend itself to data that is from diverse structures, is externally sourced, suffers from frequently changing schemas, has conflicting definitions, or has uneven properties. Stardog’s Virtual Graphs are the most mature and powerful graph-based virtualization solution on the market.
Browse and Search with Stardog Explorer
Sometimes the answer you need is expressible as a simple fact: What is the social security number of person X? What is the risk factor for investment Y? But in other cases, understanding and using the connectedness of the knowledge graph — using the path from one node to another — is key to finding the answer to your questions. For example, what does the supply chain look like for this batch of troublesome parts?
Full W3C standards support
Stardog’s platform is based on semantic graph standards that infuse meaning into your data, creating a connected network of knowledge to power your business. This is critical because an enterprise may support hundreds of applications, each with its own data model. Connecting data models across applications typically requires custom code to map data across these different sources. Thanks to the open standards that Stardog supports, now these applications can work together with a common data model, while underlying data sources retain local control.
Improve data quality with SHACL
For a Knowledge Graph to be useful, it’s critical that the data be valid and consistent. Stardog offers tools to validate and enforce data integrity; namely SHACL, a declarative language for specifying constraints over RDF graphs. Constraints can find inconsistencies across your data silos, flag conflicting data, or prevent the Knowledge Graph from accessing bad data. Constraints also support measuring the quality of the data, performing verification after an integration, and assisting in planning future improvement measures.
Machine Learning with Knowledge Graphs
Stardog Voicebox, a knowledge engineer powered by Large Language Models (LLM), dramatically shortens the time to build your data model as an Enterprise Knowledge Graph and gives human users the ability to focus on their enterprise data and unlock the insight it contains. Voicebox can streamline: Knowledge graph question answering by surfacing results without the need to write graph queries or connect a BI tool like Tableau Knowledge graph data modeling by taking prompts everyday language and building out classes and relationships Knowledge graph adoption by answering support questions about using, programming, or administering Stardog In addition, Stardog’s built-in machine learning offers both similarity search and predictive analytics (classification, regression).
Best In Class Inference Engine for Explainable AI
Stardog’s Inference Engine associates related information stored in disparate sources, and then uses this rich web of relationships to discover new relationships within your data. By inferring new connections between concepts in the Knowledge Graph, the resulting network of information becomes increasingly more valuable. Furthermore, this represents the breadth of organizational knowledge in a machine-understandable format. With knowledge digitized, Knowledge Graphs support better decision-making and faster time to insight. Inference creates new relationships by interpreting your source data against your data model.