Article: K-CEP: a knowledge-based complex event processing framework to manage qualitative spatiotemporal patterns Journal: International Journal of Internet Protocol Technology (IJIPT) 2016 Vol.9 No.4 pp.174 - 186 Abstract: In this paper, we present a framework for managing qualitative spatiotemporal patterns. Our framework is designed for large scale monitoring systems. Such systems generate a huge amount of real-time data in various formats. End-users are interested in finding significant data configurations based on their expertise and attempt to leverage the large amounts of data generated by acquisition systems. Several software tools have been proposed to help users achieve such goals. However, available solutions are mostly based on relational databases and use SQL queries to support such functionalities. These systems do not allow for real-time detection of situations of interest (also called 'patterns' in this domain) due to the weak expressiveness of SQL queries. We present a novel approach based on complex event processing for the real-time detection of situations of interest based on events, states and spatial objects. We leverage the rich semantics of a qualitative pattern representation model to present a complete solution to qualitatively represent patterns and to detect their instances from the event cloud. Thanks to our approach, a user will be able to react to detected patterns instead of trying to identify ('to mine') patterns in databases as it is proposed in current approaches. Inderscience Publishers - linking academia, business and industry through research

Title: K-CEP: a knowledge-based complex event processing framework to manage qualitative spatiotemporal patterns

Authors: Foued Barouni; Bernard Moulin

Addresses: Computer Science Department, Laval University, Pavillon Pouliot, 1065, rue de la Médecine, Quebec City QC G1V 0A6, Canada ' Computer Science Department, Laval University, Pavillon Pouliot, 1065, rue de la Médecine, Quebec City QC G1V 0A6, Canada

Abstract: In this paper, we present a framework for managing qualitative spatiotemporal patterns. Our framework is designed for large scale monitoring systems. Such systems generate a huge amount of real-time data in various formats. End-users are interested in finding significant data configurations based on their expertise and attempt to leverage the large amounts of data generated by acquisition systems. Several software tools have been proposed to help users achieve such goals. However, available solutions are mostly based on relational databases and use SQL queries to support such functionalities. These systems do not allow for real-time detection of situations of interest (also called 'patterns' in this domain) due to the weak expressiveness of SQL queries. We present a novel approach based on complex event processing for the real-time detection of situations of interest based on events, states and spatial objects. We leverage the rich semantics of a qualitative pattern representation model to present a complete solution to qualitatively represent patterns and to detect their instances from the event cloud. Thanks to our approach, a user will be able to react to detected patterns instead of trying to identify ('to mine') patterns in databases as it is proposed in current approaches.

Keywords: complex event processing; semantics; knowledge base; dynamic spatiotemporal patterns; pattern management; pattern representation.

DOI: 10.1504/IJIPT.2016.081318

International Journal of Internet Protocol Technology, 2016 Vol.9 No.4, pp.174 - 186

Received: 18 Sep 2015
Accepted: 18 May 2016
Published online: 04 Jan 2017 *

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Title: K-CEP: a knowledge-based complex event processing framework to manage qualitative spatiotemporal patterns

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