Article

A generalized framework for mining spatio-temporal patterns in scientific data

Authors:

Srinivasan Parthasarathy,

Sameep MehtaAuthors Info & Claims

KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

Pages 716 - 721

https://doi.org/10.1145/1081870.1081962

Published: 21 August 2005 Publication History

Abstract

In this paper, we present a general framework to discover spatial associations and spatio-temporal episodes for scientific datasets. In contrast to previous work in this area, features are modeled as geometric objects rather than points. We define multiple distance metrics that take into account objects' extent and thus are more robust in capturing the influence of an object on other objects in spatial neighborhood. We have developed algorithms to discover four different types of spatial object interaction (association) patterns. We also extend our approach to accommodate temporal information and propose a simple algorithm to derive spatio-temporal episodes. We show that such episodes can be used to reason about critical events. We evaluate our framework on real datasets to demonstrate its efficacy. The datasets originate from two different areas: Computational Molecular Dynamics and Computational Fluid Flow. We present results highlighting the importance of the identified patterns and episodes by using knowledge from the underlying domains. We also show that the proposed algorithms scale linearly with respect to the dataset size.

References

[1]

M. J. Atallah. A linear time algorithm for the hausdorff distance between convex polygons. Information Processing Letters, 17:207--209, 1983.

[2]

C. Bailey-Kellogg and F. Zhao. Qualitative spatial reasoning: extracting and reasoning with spatial aggregates. AI Mag., 24(4):47--60, 2004.

Digital Library

[3]

J. Christian. Numerical simulation of hydrodynamics by the method of point vortices. Journal of Computational Physics, 135, pages 189--197, 1971.

Digital Library

[4]

A. Cohn and S.M. Hazarika. Qualitative spatial representation and reasoning: an overview. Fundam. Inf., 46(1-2):1--29, 2001.

Digital Library

[5]

N. Cressie. Spatial Statistics. John Wiley and Sons, 1991.

[6]

J. Fernyhough et al. Event recognition using qualitative reasoning on automatically generated spatio-temporal models from visual input. In IJCAI97 Workshop on Spatial and Temporal Reasoning.

[7]

C. Henze. Feature detection in linked derived spaces. In In IEEE VIS, 1998.

Digital Library

[8]

M. Jiang et al. Feature mining paradigms for scientific data. In SDM, 2003.

[9]

H. Mannila and H. Toivonen. Discovering generalised episodes using minimal occurences. In SIGKDD, 1996.

[10]

S. Mehta et al. Dynamic classification of defect structures in molecular dynamics simulation data. In SDM, 2005.

[11]

S. Mehta et al. Detection and visualization of anamolous structurs in molecular dynamics simulation data. In In IEEE VIS, 2004.

Digital Library

[12]

Y. Morimoto. Mining frequent neighboring class sets in spatial databases. In SIGKDD, 2001.

Digital Library

[13]

R. Munro et al. Complex spatial relationships. In ICDM, 2003.

Digital Library

[14]

C. R. Rao and S. Suryawanshi. Statistical analysis of shape of objects based on landmark data. Proc Natl Acad Sci USA, 93(22):12132--12136, 1996.

[15]

D.A. Richie et al. Real-time multiresolution analysis for accelerated molecular dynamics simulations. In American Phy. Soc. Mar. Mtng, 2001.

[16]

A. Sadarjoen et al. Selective visualization of vortices in hydrodynamic flows. In IEEE VIS, 1998.

Digital Library

[17]

S. Shekhar and Y. Huang. Discovering spatial co-location patterns: A summary of results. Lecture Notes in Computer Science, 2001.

Digital Library

[18]

D. Silver and X. Wang. Volume tracking. IEEE VIS, 1996.

Digital Library

[19]

W. R. Tobler. A computer movie simulating urban growth in the detroit region. Economic Geography 46:234--230, 1970.

[20]

H. Xiong et al. A framework for discovering co-location patterns in data sets with extended spatial objects. In SDM, 2004.

[21]

X. Yan and J. Han. gSpan: Graph-based substructure pattern mining. In ICDM, 2002.

Digital Library

[22]

H. Yang et al. Discovering spatial relationships between approximately equivalent patterns. In BIOKDD, 2004.

[23]

H. Yang et al. A generalized framework for mining spatio-temporal patterns in scientific data. In OSU-CISRC-5/05-TR14, Ohio State University, 2005.

[24]

Kenneth Yip. Structural inferences from massive datasets. In IJCAI, 1997.

[25]

M.J. Zaki et al. New algorithms for fast discovery of association rules. In SIGKDD, 1997.

Digital Library

[26]

X. Zhang et al. Fast mining of spatial collocations. In SIGKDD, 2004.

Digital Library

Cited By

Andrzejewski WBoinski P(2022)Maximal Mixed-Drove Co-occurrence PatternsInformation Systems Frontiers10.1007/s10796-022-10344-825:5(2005-2028)Online publication date: 20-Oct-2022
https://doi.org/10.1007/s10796-022-10344-8
Chebi H(2021)Scalable Data Analysis Application to Web Usage DataMultimedia and Sensory Input for Augmented, Mixed, and Virtual Reality10.4018/978-1-7998-4703-8.ch014(261-274)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-4703-8.ch014
Andrzejewski WBoinski P(2021)Maximal Mixed-Drove Co-Occurrence PatternsAdvances in Databases and Information Systems10.1007/978-3-030-82472-3_3(15-29)Online publication date: 24-Aug-2021
https://dl.acm.org/doi/10.1007/978-3-030-82472-3_3
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Index Terms

A generalized framework for mining spatio-temporal patterns in scientific data
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
      2. Scientific visualization
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems

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Published In

cover image ACM Conferences

KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

August 2005

844 pages

ISBN:159593135X

DOI:10.1145/1081870

General Chair:
Robert Grossman
University of Illinois at Chicago & Open Data Partners, USA
,
Program Chairs:
Roberto Bayardo
IBM Almaden Research, USA
,
Kristin Bennett
RPI, USA

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 21 August 2005

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KDD05: The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 21 - 24, 2005

Illinois, Chicago, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Andrzejewski WBoinski P(2022)Maximal Mixed-Drove Co-occurrence PatternsInformation Systems Frontiers10.1007/s10796-022-10344-825:5(2005-2028)Online publication date: 20-Oct-2022
https://doi.org/10.1007/s10796-022-10344-8
Chebi H(2021)Scalable Data Analysis Application to Web Usage DataMultimedia and Sensory Input for Augmented, Mixed, and Virtual Reality10.4018/978-1-7998-4703-8.ch014(261-274)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-4703-8.ch014
Andrzejewski WBoinski P(2021)Maximal Mixed-Drove Co-Occurrence PatternsAdvances in Databases and Information Systems10.1007/978-3-030-82472-3_3(15-29)Online publication date: 24-Aug-2021
https://dl.acm.org/doi/10.1007/978-3-030-82472-3_3
Anwar TLiao KGoyal ASellis TKayes AShen H(2020)Inferring Location Types With Geo-Social-Temporal Pattern MiningIEEE Access10.1109/ACCESS.2020.30189978(154789-154799)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3018997
Flouvat FSelmaoui-Folcher NSanhes JMu CPasquier CBoulicaut J(2020)Mining evolutions of complex spatial objects using a single-attributed Directed Acyclic GraphKnowledge and Information Systems10.1007/s10115-020-01478-9Online publication date: 24-Jun-2020
https://doi.org/10.1007/s10115-020-01478-9
Kang DPatel VNair ABlanas SWang YParthasarathy SEigenmann RDing CMcKee S(2019)HenosisProceedings of the ACM International Conference on Supercomputing10.1145/3330345.3330380(392-402)Online publication date: 26-Jun-2019
https://dl.acm.org/doi/10.1145/3330345.3330380
Moosavi SSamavatian MNandi AParthasarathy SRamnath RTeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)Short and Long-term Pattern Discovery Over Large-Scale Geo-Spatiotemporal DataProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330755(2905-2913)Online publication date: 25-Jul-2019
https://dl.acm.org/doi/10.1145/3292500.3330755
Tomobe KYasuoka K(2018)Detection of Anomalous Dynamics for a Single Water MoleculeJournal of Chemical Theory and Computation10.1021/acs.jctc.7b0110414:3(1177-1185)Online publication date: 22-Jan-2018
https://doi.org/10.1021/acs.jctc.7b01104
McGuire M(2017)Data Mining Techniques for the Characterization of Dynamic Regions in Spatiotemporal DataEncyclopedia of GIS10.1007/978-3-319-17885-1_1543(427-434)Online publication date: 12-May-2017
https://doi.org/10.1007/978-3-319-17885-1_1543
Yu CDing WMorabito MChen P(2016)Hierarchical Spatio-Temporal Pattern Discovery and Predictive ModelingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2015.250757028:4(979-993)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.1109/TKDE.2015.2507570
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