Discovering Non-compliant Window Co-Occurrence Patterns: A Summary of Results
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.
KeywordsSelective Catalytic Reduction Wheel Speed Naive Approach Minimum Support Threshold Candidate Pattern
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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