Learn about the steps you need to migrate your data to GraphDB to use it as a smart brain on top of your legacy systems.
Do you know what all your organization’s data can tell you? Do you want to be able to analyze it by looking into the relationships between its concepts?
If you’re pondering this questions, we at Ontotext have the answers.
Our semantic graph database GraphDB helps you interlink your enterprise data and build an integrated data view for querying and analytics, so you can turn all the information in it into knowledge and wisdom.
The semantic graph database focuses on the relationships between concepts and is able to infer new knowledge out of existing information. GraphDB performs semantic inferencing at scale and handles massive loads, queries, and inferencing in real time. It is a powerful tool to use in relationship-centered analytics and knowledge discovery.
However, our offer is not limited to just selling you the database.
We also go the extra mile in accompanying you on an easy 10-step pathway to discover the knowledge hidden in your organization’s data.
Welcome to our GraphDB Migration Service – the A-Z service that helps you prepare for migrating your data to GraphDB, walks you through the setup, and monitors performance. We make the effort to understand your specific business case and assist you in solving your data challenges. Our dedicated technical team helps you all the way through the process and we also provide professional account management, clear-cut KPIs, and continuous support.
Let’s walk you through the 10-step migration process so you can start getting insights from your data with GraphDB.
Speaking of database performance, we now have the updated GraphDB Performance Benchmark Results, presenting some well-known benchmarks and explaining how they should be interpreted in the context of common RDF use cases.
The LDBC Semantic Publishing Benchmark (SPB) is a popular benchmark that simulates the database load commonly faced by media or publishing organizations. The generated dataset is based on BBC’s Dynamic Semantic Publishing and contains both reference data and content meta-data.
The Berlin SPARQL Benchmark (BSBM) is similar to LDBC SPB in combining read queries with frequent updates. However, it covers a more generic use case, generally defined as eCommerce, and describes relations between products and producers, products and offers, offers and vendors, products and reviews.
Our engineering team works tirelessly to achieve the best possible database loading and query answering speed. Don’t hesitate to contact us at email@example.com if you don’t see your use case on our performance benchmark page. We’ll do our best to publish it.
Now that you have the performance benchmarks to look at, pick your preferred GraphDB version for your specific use case. We’ll walk you through the migration process to help you get started with analyzing your data and turning it into insights.
Ready to get to the next level of advanced analytics by migration to Ontotext GraphDB Migration Service?