Discovery of co-evolving spatial event sets

Jin Soung Yoo; Shashi Shekhar; Sangho Kim; Mete Celik

doi:10.1137/1.9781611972764.27

Discovery of co-evolving spatial event sets

Jin Soung Yoo, Shashi Shekhar, Sangho Kim, Mete Celik

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

22 Scopus citations

Abstract

A spatial co-located event set represents a subset of spatial events whose instances are located in a spatial neighborhood. The discovery of co-evolving spatial event sets involves finding co-located event sets whose spatial prevalence variations over time are similar to a specific query sequence. Mining co-evolving spatial event sets is computationally challenging due to the high computational cost of finding co-located event instances on continuous geographic space, large temporal space and a composite interest measure, i.e., the spatial prevalence time sequence of a co-located event set. We propose a novel method for mining co-evolving spatial event sets. We analyze the proposed algorithm in terms of correctness and completeness, and experimentally evaluate the algorithm.

Original language	English (US)
Title of host publication	Proceedings of the Sixth SIAM International Conference on Data Mining
Publisher	Society for Industrial and Applied Mathematics
Pages	306-315
Number of pages	10
ISBN (Print)	089871611X, 9780898716115
DOIs	https://doi.org/10.1137/1.9781611972764.27
State	Published - 2006
Event	Sixth SIAM International Conference on Data Mining - Bethesda, MD, United States Duration: Apr 20 2006 → Apr 22 2006

Publication series

Name	Proceedings of the Sixth SIAM International Conference on Data Mining
Volume	2006

Other

Other	Sixth SIAM International Conference on Data Mining
Country/Territory	United States
City	Bethesda, MD
Period	4/20/06 → 4/22/06

Keywords

Co-evolution pattern
Co-located events
Spatio-temporal data mining

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10.1137/1.9781611972764.27

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Discovery of co-evolving spatial event sets. / Yoo, Jin Soung; Shekhar, Shashi; Kim, Sangho et al.
Proceedings of the Sixth SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2006. p. 306-315 (Proceedings of the Sixth SIAM International Conference on Data Mining; Vol. 2006).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yoo, JS, Shekhar, S, Kim, S & Celik, M 2006, Discovery of co-evolving spatial event sets. in Proceedings of the Sixth SIAM International Conference on Data Mining. Proceedings of the Sixth SIAM International Conference on Data Mining, vol. 2006, Society for Industrial and Applied Mathematics, pp. 306-315, Sixth SIAM International Conference on Data Mining, Bethesda, MD, United States, 4/20/06. https://doi.org/10.1137/1.9781611972764.27

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title = "Discovery of co-evolving spatial event sets",

abstract = "A spatial co-located event set represents a subset of spatial events whose instances are located in a spatial neighborhood. The discovery of co-evolving spatial event sets involves finding co-located event sets whose spatial prevalence variations over time are similar to a specific query sequence. Mining co-evolving spatial event sets is computationally challenging due to the high computational cost of finding co-located event instances on continuous geographic space, large temporal space and a composite interest measure, i.e., the spatial prevalence time sequence of a co-located event set. We propose a novel method for mining co-evolving spatial event sets. We analyze the proposed algorithm in terms of correctness and completeness, and experimentally evaluate the algorithm.",

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AB - A spatial co-located event set represents a subset of spatial events whose instances are located in a spatial neighborhood. The discovery of co-evolving spatial event sets involves finding co-located event sets whose spatial prevalence variations over time are similar to a specific query sequence. Mining co-evolving spatial event sets is computationally challenging due to the high computational cost of finding co-located event instances on continuous geographic space, large temporal space and a composite interest measure, i.e., the spatial prevalence time sequence of a co-located event set. We propose a novel method for mining co-evolving spatial event sets. We analyze the proposed algorithm in terms of correctness and completeness, and experimentally evaluate the algorithm.

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KW - Co-located events

KW - Spatio-temporal data mining

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Discovery of co-evolving spatial event sets

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