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Stream Mining, Spatial

  • Reference work entry
  • First Online:
Encyclopedia of GIS

  • 107 Accesses

Synonyms

Spatiotemporal Data Mining; Stream Data Mining

Definition

Spatial stream mining is the process of discovering novel patterns, rules, and trends within a set of spatial streams. A spatial data stream is characterized as a data stream which possesses both spatial and non-spatial attributes. Examples of spatial data streams are the location of a continuously moving vehicle in a time period and the non-spatial measurements of a geospatially aware sensor network. In general, the data stream is “continuous, mutable, ordered, fast, high-dimensional, and unbounded” (Babcock et al. 2002). Spatial stream mining hence focuses on developing and optimizing mining techniques for spatial data streams.

Historical Background

Active work in stream query processing began in early 2000 with systems such as STREAM (Arasu et al. 2003) and COUGAR (Yao and Gehrke 2002). Most of these efforts placed heavy emphasis on query processing and gave little attention to data mining tasks. Then later much...

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References

  • Arasu A, Babcock B, Babu S, Datar M, Ito K, Nishizawa I, Rosenstein J, Widom J (2003) STREAM: the Stanford stream data manager. In: Proceedings of the 2003 ACM SIGMOD international conference on management of data, San Diego, p 665

    Google Scholar 

  • Babcock G, Babu S, Datar M, Motwani R, Widom J (2002) Models and issues in data stream systems. In: ACM symposium on principles of database systems (PODS), Madison, pp 1–16

    Google Scholar 

  • Chakrabarti C, Vishwanath M, Owens R (2005) Architectures for wavelet transforms: a survey. J VLSI Signal Process 171–192

    Google Scholar 

  • Domingos P, Hulten G (2000) Mining high-speed data streams. In: Proceedings of ACM special interest group on knowledge discovery and data mining, Boston, pp 71–80

    Google Scholar 

  • Hand DJ, Mannila H, Smyth P (2001) Principles of data mining. MIT, Cambridge

    Google Scholar 

  • Hershberger J, Shrivastava N, Suri S (2006) Cluster hulls: a technique for summarizing spatial data streams. In: Proceedings of IEEE international conference on data engineering, Atlanta, p 138

    Google Scholar 

  • Hulten G, Spencer L, Domingos P (2001) Mining time-changing data streams. In: Proceedings of ACM special interest group on knowledge discovery and data mining, San Francisco, pp 97–106

    Google Scholar 

  • Natwichai J, Li X (2004) Knowledge maintenance on data streams with concept drifting: international symposium on computation and information sciences (CIS), Shanghai, pp 705–710

    Google Scholar 

  • O’Callaghan L, Mishra N, Meyerson A, Guha S, Motwani R (2002) Streaming-data algorithms for high-quality clustering. In: Proceedings of IEEE international conference on data engineering, San Jose, pp 685–694

    Google Scholar 

  • Pan F, Wang B, Ren D, Hu X, Perrizo W (2003) Proximal support vector machine for spatial data using peano trees. In: ISCA computer applications in industry and engineering, Las Vegas, pp 292–297

    Google Scholar 

  • Perrizo W, Jockheck W, Perera A, Ren D, Wu W, Zhang Y (2002) Multimedia data mining using P-trees. In: International workshop on multimedia data mining (MDM/KDD), Edmonton, pp 19–29

    Google Scholar 

  • Rao KR, Yip P (1990) Discrete cosine transform: algorithms, advantages, applications. Academic, San Diego

    Book  MATH  Google Scholar 

  • Ruoming J, Agrawal G (2003) Efficient decision tree construction on streaming data. In: ACM special interest group on knowledge discovery and data mining (SIGKDD), Washington, DC, pp 571–576

    Google Scholar 

  • Versavel J (1999) Road safety through video detection. In: Proceedings of IEEE international conference on intelligent transportation system, Boulder, pp 753–757

    Google Scholar 

  • Wang B, Pan F, Ren D, Cui Y, Ding Q, Perrizo W (2003) Efficient OLAP operations for spatial data using peano trees: In ACM special interest group on management of data workshop (SIGMOD), San Diego, pp 28–34

    Google Scholar 

  • Yao Y, Gehrke JE (2002) The cougar approach to in-network query processing in sensor networks: ACM special interest group on data management of data (SIGMOD) record, pp 9–18

    Google Scholar 

  • Zhao J, Lu CT, Kou Y (2003) Detecting region outliers in meteorological data. In: Proceedings of ACM international symposium on advances in geographic information system, New Orleans, pp 49–55

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Virginia Polytechnic Institute and State University, Falls Church, VA, USA

    Arnold P. Boedihardjo

Authors
  1. Arnold P. Boedihardjo
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Editor information

Editors and Affiliations

  1. University of Minnesota, Minneapolis, MN, USA

    Shashi Shekhar

  2. Management Science and Information Systems Department, Rutgers Business School, Rutgers, The State University of New Jersey, Newark, NJ, USA

    Hui Xiong

  3. Department of Management Sciences, Tippie College of Business, University of Iowa, Iowa City, IA, USA

    Xun Zhou

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© 2017 Springer International Publishing AG

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Boedihardjo, A.P. (2017). Stream Mining, Spatial. In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-17885-1_1358

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