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Knowledge Graphs have been welcomed by more and more businesses and researchers, they are natural to model after the real world problems and present them in easy to consume ways, and usually stored in document databases such as MongoDB, some are stored using traditional RDBMS. Lacking a powerful and instant graph computing engine has bogged down KGs, rendering many graph analytics slow. With Ultipa’s real-time deep traversal capabilities, KGs fly instantly.
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Pain PointsSolutions
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Slowness and Shallowness
Most knowledge graph storage engine are not designed to compute in real-time or in-depth. This makes graph analytics embarrassingly slow and superficial.
Deep Graph Computing & Programmability
Ultipa KG is capable of super-deep graph computing to traverse over 1 Billion nodes and edges per second per instance. One can use the powerful yet super-easy uQL to program and dynamically extract subgraphs out of a much larger KG.
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Poor visualization
Graphical visualization is a must-have with any knowledge graph, but in the era of Enterprise 2.0, most KG visualization are not performance tuned, therefore incapable of handling large amount of data.
High Visualization
Graph is high-dimensional, it’s utterly important and crucial to leverage visualization to not only make your whole operational process painless and visually appealing but also to make the results self-explaining and intuitive.