27 April 2018 Customizable fusion of violent event mentions in heterogeneous data
Author Affiliations +
Abstract
Ingest and maintenance of disparate data is a difficult problem to solve. As an example, we describe the fusion of violent events across multiple, disparate data sources. The Information Consumption, Exploitation, and Dissemination (ICED) framework provides an end to end system that solves this problem. It offers a set of ontologies and a mapping tool, OSCAR, to align and ingest the disparate data sources. These alignments are registered with a tool that performs management of the ingestion process and monitors how many records are translated and ingested successfully. A critical aspect of ingestion is the fusion step and we offer a novel, attribute-based, cloud scale fusion engine to match, in our example, mentions of the same event across the sources. The fusion engine offers the capability for multiple resolution runs and the exploration of results to assess the impact and results of custom scoring models. Next, ICED indexes a subset of objects of interest, for example the violent events themselves and the actors involved, for fast query and access. ICED offers a flexible object-definition interface to allow users to develop object views for index and retrieval. This allows end-users access to succinct information ‘baseball card’ views through interaction with the search or summary user interfaces. Such ‘baseball card’ views are developed for entities and events. For example, an individual violent event instance will have a baseball card view associated with it and, importantly, connections to the baseball card views of supporting information like the actors involved or the location where the event occurred. The results can also be viewed geospatially on a map for intuitive exploration.
Conference Presentation
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Justin Del Vecchio, Justin Del Vecchio, Timothy K. Perkins, Timothy K. Perkins, Gregory Tauer, Gregory Tauer, Adam Czerniejewski, Adam Czerniejewski, Jonathan Logan, Jonathan Logan, "Customizable fusion of violent event mentions in heterogeneous data", Proc. SPIE 10653, Next-Generation Analyst VI, 106530X (27 April 2018); doi: 10.1117/12.2307047; https://doi.org/10.1117/12.2307047
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