Making Sense of Text and Data
Provide consistent unified access to data across different systems by using the flexible and semantically precise structure of the knowledge graph model
Interlink your organization’s data and content by using knowledge graph powered natural language processing with our Content Management solutions.
Implement a Connected Inventory of enterprise data assets, based on a knowledge graph, to get business insights about the current status and trends, risk and opportunities, based on a holistic interrelated view of all enterprise assets.
Quick and easy discovery in clinical trials, medical coding of patients’ records, advanced drug safety analytics, knowledge graph powered drug discovery, regulatory intelligence and many more
Make better sense of enterprise data and assets for competitive investment market intelligence, efficient connected inventory management, enhanced regulatory compliance and more
Connect and model industry systems and processes for deeper data-driven insights in:
Improve engagement, discoverability and personalized recommendations for Financial and Business Media, Market Intelligence and Investment Information Agencies, Science, Technology and Medicine Publishers, etc.
Unlock the potential for new intelligent public services and applications for Government, Defence Intelligence, etc.
Connect and improve the insights from your customer, product, delivery, and location data. Gain a deeper understanding of the relationships between products and your consumers’ intent.
Link diverse data, index it for semantic search and enrich it via text analysis to build big knowledge graphs.
Organize your information and documents into enterprise knowledge graphs and make your data management and analytics work in synergy.
Integrate and evaluate any text analysis service on the market against your own ground truth data in a user friendly way.
Turn strings to things with Ontotext’s free application for automating the conversion of messy string data into a knowledge graph.
GraphDB Q&As
TESTED ON: GraphDB 9.10
This error is relatively common. This is a guard functionality of GraphDB. It is intended to prevent Out of Memory (OOM) exceptions. When evaluating “distinct”, the database has to keep a lot of data in memory. This is why it is being monitored. The easy way to fix the problem would be to give GraphDB more memory.
For details on what happens, read on.
This message gets triggered by queries with distinct and group by clauses. GraphDB has a few internal monitoring classes. One of these is intended for the size of the queries utilizing the “distinct” clause.
A simple query with distinct and Group by
What the monitor does is a very basic check. There is a threshold value. This value is calculated by summing a hard limit, 250 MB by default, and an estimate of the current memory allocated to processing other queries with the “distinct” keyword. If the currently available free memory is lower than this sum, the query is rejected. Here are a few examples:
So, the easy and “correct” solution would be to add extra memory to GraphDB and/or not run a lot of operations at the same time. In particular, queries with “distinct” and “group by” may lead to throttling other similar queries.
Another approach is to tweak the limit. The limit is configured via the default.min.distinct.threshold GraphDB property. By setting it to 0 – or a negative value – you are only comparing the free memory usage to the estimate of what’s already required for processing other queries with “distinct”. However, be warned that this is risking OOM errors due to lots of “distinct” queries happening at the same time.
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Ontotext answers questions from our GraphDB users. You can also check out the frequently asked questions on general topics about GraphDB. Or you can get quick answers on technical questions from the community as well as Ontotext experts using the graphdb tag on stack overflow.
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