WIP - SKOD: A Framework for Situational Knowledge on Demand

Date

2019-01-01

Author

Palacios, Servio
Solaiman, K.M.A.
Angın, Pelin
Nesen, Alina
Bhargava, Bharat
Collins, Zachary
Sipser, Aaron
Stonebraker, Michael
Macdonald, James

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Extracting relevant patterns from heterogeneous data streams poses significant computational and analytical challenges. Further, identifying such patterns and pushing analogous content to interested parties according to mission needs in real-time is a difficult problem. This paper presents the design of SKOD, a novel Situational Knowledge Query Engine that continuously builds a multi-modal relational knowledge base using SQL queries; SKOD pushes dynamic content to relevant users through triggers based on modeling of users’ interests. SKOD is a scalable, real-time, on-demand situational knowledge extraction and dissemination framework that processes streams of multi-modal data utilizing publish/subscribe stream engines. The initial prototype of SKOD uses deep neural networks and natural language processing techniques to extract and model relevant objects from video streams and topics, entities and events from unstructured text resources such as Twitter and news articles. Through its extensible architecture, SKOD aims to provide a high-performance, generic framework for situational knowledge on demand, supporting effective information retrieval for evolving missions.

Subject Keywords

Query engine, Multi-modal information retrieval, Knowledge base, Stream processing, Targeted information dissemination

URI

https://hdl.handle.net/11511/48932

Collections

Department of Computer Engineering, Conference / Seminar