Discovering non-compliant window co-occurrence patterns: A summary of results

Reem Y. Ali, Venkata M.V. Gunturi, Andrew J. Kotz, Shashi Shekhar, William F. Northrop

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations

Abstract

Given a set of trajectories annotated with measurements of physical variables, the problem of Non-compliant Window Co-occurrence (NWC) pattern discovery aims to determine temporal signatures in the explanatory variables which are highly associated with windows of undesirable behavior in a target variable. NWC discovery is important for societal applications such as eco-friendly transportation (e.g. identifying engine signatures leading to high greenhouse gas emissions). Challenges of designing a scalable algorithm for NWC discovery include the non monotonicity of popular spatio-temporal statistical interest measures of association such as the cross-K function. This challenge renders the anti-monotone pruning based algorithms (e.g. Apriori) inapplicable. To address this limitation, we propose two novel upper bounds for the cross- K function which help in filtering uninteresting candidate patterns. Using these bounds, we also propose a Multi-Parent Tracking approach (MTNMiner) for mining NWC patterns. A case study with real world engine data demonstrates the ability of the proposed approach to discover patterns which are interesting to engine scientists. Experimental evaluation on real-world data show that MTNMiner results in substantial computational savings over the naive approach.

Original languageEnglish (US)
Pages (from-to)391-410
Number of pages20
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9239
DOIs
StatePublished - 2015
Event14th International on Symposium on Spatial and Temporal Databases, SSTD 2015 - Hong Kong, China
Duration: Aug 26 2015Aug 28 2015

Bibliographical note

Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No. 1029711, IIS-1320580, 0940818 and IIS-1218168, the USDOD under Grant No. HM1582-08-1-0017 and HM0210-13-1-0005, and the University of Minnesota under the OVPR U-Spatial. We are particularly grateful to Kim Koffolt and the members of the University of Minnesota Spatial Computing Research Group for their valuable comments.

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