NDLI: Cascading Spatio-Temporal Pattern Discovery

Content Provider	IEEE Xplore Digital Library
Author	Mohan, P. Shekhar, S. Shine, J.A. Rogers, J.P.
Copyright Year	1989
Abstract	Given a collection of Boolean spatiotemporal (ST) event-types, the cascading spatiotemporal pattern (CSTP) discovery process finds partially ordered subsets of these event-types whose instances are located together and occur serially. For example, analysis of crime data sets may reveal frequent occurrence of misdemeanors and drunk driving after and near bar closings on weekends, as well as after and near large gatherings such as football games. Discovering CSTPs from ST data sets is important for application domains such as public safety (e.g., identifying crime attractors and generators) and natural disaster planning, (e.g., preparing for hurricanes). However, CSTP discovery presents multiple challenges; three important ones are 1) the exponential cardinality of candidate patterns with respect to the number of event types, 2) computationally complex ST neighborhood enumeration required to evaluate the interest measure and 3) the difficulty of balancing computational complexity and statistical interpretation. Current approaches for ST data mining focus on mining totally ordered sequences or unordered subsets. In contrast, our recent work explores partially ordered patterns. Recently, we represented CSTPs as directed acyclic graphs (DAGs); proposed a new interest measure, the cascade participation index (CPI); outlined the general structure of a cascading spatiotemporal pattern miner (CSTPM); evaluated filtering strategies to enhance computational savings using a real-world crime data set and proposed a nested loop-based CSTPM to address the challenge posed by exponential cardinality of candidate patterns. This paper adds to our recent work by offering a new computational insight, namely, that the computational bottleneck for CSTP discovery lies in the interest measure evaluation. With this insight, we propose a new CSTPM based on spatiotemporal partitioning that significantly lowers the cost of interest measure evaluation. Analytical evaluation shows that our new CSTPM is correct and complete. Results from significant amount of new experimental evaluation with both synthetic and real data show that our new ST partitioning-based CSTPM outperforms the CSTPM from our previous work. We also present a case study that verifies the applicability of CSTP discovery process.
Sponsorship	IEEE IEEE Comput. Soc. Tech. Committee on Data Eng IEEE Computer Society
Starting Page	1977
Ending Page	1992
Page Count	16
File Size	1263055
File Format	PDF
ISSN	10414347
Volume Number	24
Issue Number	11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Data mining Time measurement Hurricanes Indexes Data models Meteorology spatiotemporal partial order Cascading spatiotemporal patterns space-time K-function cascade participation index spatiotemporal join spatio-temporal continuity positive ST autocorrelation
Content Type	Text
Resource Type	Article
Subject	Information Systems Computational Theory and Mathematics Computer Science Applications

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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