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 language | English (US) |
|---|---|
| Pages (from-to) | 391-410 |
| Number of pages | 20 |
| Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Volume | 9239 |
| DOIs | |
| State | Published - Jan 1 2015 |
| Event | 14th International on Symposium on Spatial and Temporal Databases, SSTD 2015 - Hong Kong, China Duration: Aug 26 2015 → Aug 28 2015 |
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Discovering non-compliant window co-occurrence patterns : A summary of results. / Ali, Reem Y.; Gunturi, Venkata M.V.; Kotz, Andrew J.; Shekhar, Shashi; Northrop, William.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9239, 01.01.2015, p. 391-410.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - Discovering non-compliant window co-occurrence patterns
T2 - A summary of results
AU - Ali, Reem Y.
AU - Gunturi, Venkata M.V.
AU - Kotz, Andrew J.
AU - Shekhar, Shashi
AU - Northrop, William
PY - 2015/1/1
Y1 - 2015/1/1
N2 - 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.
AB - 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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U2 - 10.1007/978-3-319-22363-6_21
DO - 10.1007/978-3-319-22363-6_21
M3 - Conference article
VL - 9239
SP - 391
EP - 410
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
SN - 0302-9743
ER -