Today I’m talking to Amit Weizner, founder and tech-entrepreneur form Israel. He has founded several companies in data-intensive branches in recent years. Now he's on board of timbr.ai, a company doing SQL Knowledge Graphs.
Thomas: You are providing a solution in SQL to provide data lakes and data warehouses with ontology modeling and semantic data enrichment. You may describe the concept.
Amit: timbr SQL Knowledge Graph is our implementation of the Semantic Web in SQL. We developed timbr to enable users knowledgeable in SQL to conveniently and quickly implement and use Knowledge Graphs. timbr virtual ontologies are mapped to standard relational databases, so the vast majority of the databases in use by enterprises today can be conveniently queried as Knowledge Graphs without need of translators and ETL operations. This is very important because enterprises naturally prefer to keep their database infrastructure, and with the rowing need for volumes of data required by ML and Knowledge Bases, copying Terabytes or Petabytes of data is a common problem. timbr also delivers virtual integration of heterogenous data silos common in big enterprises and the integration of 3rd party taxonomies, ontologies and datasets for enrichment of complex analytics and AI. Semantic Web ontologies, schemas and data catalogs can conveniently be converted into timbr SQL ontologies mapped to the data to enable convenient query in SQL. We have created timbr-DBpedia SQL Knowledge Graph and timbr-GDELT SQL Knowledge Graph as showcases that demonstrate timbr’s ability to handle complex ontologies (DBpedia) and very large datasets (GDELT). Both Knowledge Graphs are available for test-drive at our website timbr.ai and we shall present the GDELT SQL Knowledge Graph at the conference.
Thomas: Please tell us about those use-cases, where this coupling of two worlds makes the most sense!
Amit: timbr features graph traversal and evolving schema capabilities in standard SQL engines that may be implemented for the typical and beyond use cases served by graph databases in many verticals. Our users come to us because of a diversity of needs, many for implementation of Knowledge Graphs on big data on data lakes and data warehouses. We are now implementing interesting use cases for Homeland Security Agencies in Israel. We also have interesting use cases for optimization of digital marketing campaigns using weather, news and other external data sources, use of data governance catalogs converted into SQL queryable ontologies, fraud prevention, pharma research and some others. A systematic and ubiquitous “use case” that we serve is that of long, complex SQL queries required in analytics and ML. timbr reduces large SQL queries by 90% or more, which is significant for organizations wishing to empower analysts and data scientists. Lastly, our customers value timbr’s graph data exploration module which is used as an alternative to complex SQL queries. Users can “walk” through their relational database and visualize data records to discover relationships and garner insights. This is an important capability that enables enterprises to become aware and USE their big data for strategic and business performance objectives.
Thomas: SEMANTiCS 2020 Austin, welcomes you as a speaker. Please give us some insights on what we can learn in this talk
Amit: You are invited to see our presentation of the timbr-GDELT SQL Knowledge Graph in which we will show a live demo of our solution. You may also visit us at our timbr.ai stand where we can respond to your questions and show a timbr demo. And don't hesitate to call on us if you see us walking around the conference halls - we shall wear our timbr.ai shirts with our logo. See you at the conference!
Co-founder and CEO at timbr.ai (WP Semantix Ltd.) Cyber Security and Technology Advisor. Big data analytics development leader. Achievement commendation - Israel Government.
