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
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9239)

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.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Reem Y. Ali
    • 1
  • Venkata M.V. Gunturi
    • 1
  • Andrew J. Kotz
    • 2
  • Shashi Shekhar
    • 1
  • William F. Northrop
    • 2
  1. 1.Department of Computer Science and EngineeringUniversity of MinnesotaMinneapolisUSA
  2. 2.Department of Mechanical EngineeringUniversity of MinnesotaMinneapolisUSA

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