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Proceedings Paper

A scalable architecture for extracting, aligning, linking, and visualizing multi-Int data
Author(s): Craig A. Knoblock; Pedro Szekely
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Paper Abstract

An analyst today has a tremendous amount of data available, but each of the various data sources typically exists in their own silos, so an analyst has limited ability to see an integrated view of the data and has little or no access to contextual information that could help in understanding the data. We have developed the Domain-Insight Graph (DIG) system, an innovative architecture for extracting, aligning, linking, and visualizing massive amounts of domain-specific content from unstructured sources. Under the DARPA Memex program we have already successfully applied this architecture to multiple application domains, including the enormous international problem of human trafficking, where we extracted, aligned and linked data from 50 million online Web pages. DIG builds on our Karma data integration toolkit, which makes it easy to rapidly integrate structured data from a variety of sources, including databases, spreadsheets, XML, JSON, and Web services. The ability to integrate Web services allows Karma to pull in live data from the various social media sites, such as Twitter, Instagram, and OpenStreetMaps. DIG then indexes the integrated data and provides an easy to use interface for query, visualization, and analysis.

Paper Details

Date Published: 15 May 2015
PDF: 10 pages
Proc. SPIE 9499, Next-Generation Analyst III, 949907 (15 May 2015); doi: 10.1117/12.2177119
Show Author Affiliations
Craig A. Knoblock, The Univ. of Southern California (United States)
Pedro Szekely, The Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 9499:
Next-Generation Analyst III
Barbara D. Broome; Timothy P. Hanratty; David L. Hall; James Llinas, Editor(s)

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