Why Tables and Graphs for Knowledge Discovery Systems

When:
Wednesday, January 4, 2017, 10:00 am - 11:00 am PSTiCal
Where:
Arlington
This event is open to the public.
Type:
Heterogenous Computing: Systems, Architectures, and Applications Seminar
Speaker:
John Feo
Description:

Please join us for a presentation by John Feo, the Director of the Northwest Institute for Advanced Computing.

The availability of data is changing the way science, business, and law enforcement operate. Economic competitiveness and national security depend increasingly on the insightful analysis of large data sets. The breadth of analytic processes are forcing knowledge discovery platforms to supplement traditional table-based methods with graph methods that provide better support for sparse data and dynamic relationships among typed entities. While storing data in only tables makes it difficult to discover complex patterns of activities in time and space, tables are the most efficient data structures for storing dense node and edge attributes, and executing simple select and join operations. Consequently, knowledge discovery systems must support both in a natural way without preference. In this talk, I will describe the software stack we have built to support both tables and property graphs, and how we have extended SQL to define graph views and queries. We are now using the stack to support a hierarchical library of graph and machine learning methods, and a comprehensive knowledge base system.

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