Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Uncertain Spatial Data Management

  • Reynold Cheng
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_80742

Synonyms

Imprecise spatial databases; Probabilistic spatial databases

Definition

Spatial data are prevalent in location-based services (LBS), sensor networks, and RFID monitoring systems. Data readings collected in these applications are often imprecise. The uncertainty in the data can arise from multiple sources, including measurement errors due to the sensing instrument and discrete sampling of the measurements. It is often important to record the imprecision and also to take it into account when processing the spatial data. The challenges of handling the uncertainty in spatial data include modeling, semantics, query operators and types, efficient execution, and user interfaces. Probabilistic models have been proposed for handling the uncertainty. We call the database system that manages uncertainty of spatial data a probabilistic spatial database.

Historical Background

Data uncertainty is an inherent property in applications that deal with spatial data. In the Global Positioning...

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Böhm C, Pryakhin A, Schubert M. The gauss-tree: efficient object identification in databases of probabilistic feature vectors. In: Proceedings of the 22nd International Conference on Data Engineering; 2006.Google Scholar
  2. 2.
    Chen J, Cheng R. Efficient evaluation of imprecise location-dependent queries. In: Proceedings of the 23rd International Conference on Data Engineering; 2007.Google Scholar
  3. 3.
    Cheng R, Kalashnikov D, Prabhakar S. Evaluating probabilistic queries over imprecise data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2003. p. 551–62.Google Scholar
  4. 4.
    Cheng R, Kalashnikov DV, Prabhakar S. Querying imprecise data in moving object environments. IEEE Trans Knowl Data Eng. 2004;16(9):1112–1127.CrossRefGoogle Scholar
  5. 5.
    Cheng R, Chen J, Mokbel M, Chow C. Probabilistic verifiers: evaluating constrained nearest-neighbor queries over uncertain data. In: Proceedings of the 24th International Conference on Data Engineering; 2008.Google Scholar
  6. 6.
    Cheng R, Xie X, Yiu ML, Chen J, Sun L. UV-diagram: a Voronoi diagram for uncertain data. In: Proceedings of the IEEE International Conference on Data Engineering (IEEE ICDE 2010), Long Beach; 2010.Google Scholar
  7. 7.
    Dai X, Yiu ML, Mamoulis N, Tao Y, Vaitis M. Probabilistic spatial queries on existentially uncertain data. In: Proceedings of the 9th International Symposium Advances in Spatial and Temporal Databases; 2005. p. 400–17.CrossRefGoogle Scholar
  8. 8.
    de Berg M, van Kreveld M, Overmars M, Schwarzkopf O. Computational geometry: algorithms and applications. Berlin: Springer; 1997.zbMATHCrossRefGoogle Scholar
  9. 9.
    Deshpande A, Guestrin C, Madden S, Hellerstein J, Hong W. Model-driven data acquisition in sensor networks. In: Proceedings of the 30th International Conference on Very Large Data Bases; 2004.Google Scholar
  10. 10.
    Kriegel H, Kunath P, Renz M. Probabilistic nearest-neighbor query on uncertain objects. In: Proceedings of the 12th International Conference on Database Systems for Advanced Applications; 2007. p. 337–48.Google Scholar
  11. 11.
    Ljosa V, Singh A. APLA: indexing arbitrary probability distributions. In: Proceedings of the 23rd International Conference on Data Engineering; 2007. p. 946–55.Google Scholar
  12. 12.
    Okabe A, Boots B, Sugihara K, Chiu S. Spatial tessellations: concepts and applications of Voronoi diagrams. 2nd ed. Chichester: Wiley; 2000.zbMATHCrossRefGoogle Scholar
  13. 13.
    Parker A, Subrahmanian V, Grant J. A logical formulation of probabilistic spatial databases. IEEE Trans Knowl Data Eng. 2007;19(11):92–107.CrossRefGoogle Scholar
  14. 14.
    Pei J, Jiang B, Lin X, Yuan Y. Probabilistic skylines on uncertain data. In: Proceedings of the 33rd International Conference on Very Large Data Bases; 2007.Google Scholar
  15. 15.
    Pfoser D, Jensen C. Capturing the uncertainty of moving-objects representations. In: Proceedings of the 11th International Conference on Scientific and Statistical Database Management; 1999.Google Scholar
  16. 16.
    Singh S, Mayfield C, Shah R, Prabhakar S, Hambrusch S, Neville J, Cheng R. Database support for probabilistic attributes and tuples. In: Proceedings of the 24th International Conference on Data Engineering; 2008.Google Scholar
  17. 17.
    Sistla PA, Wolfson O, Chamberlain S, Dao S. Querying the uncertain position of moving objects. In: Temporal databases: research and practice. Berlin/New York: Springer; 1998.Google Scholar
  18. 18.
    Xie X, Cheng R, Yiu ML, Sun L, Chen J. UV-diagram: a Voronoi diagram for uncertain spatial databases. VLDB J. 2013;22(3):319–44.CrossRefGoogle Scholar
  19. 19.
    Zhang P, Cheng R, Mamoulis N, Renz M, Zuefle A, Tang Y, Emrich T. Voronoi-based nearest neighbor search for multi-dimensional uncertain databases. In: Proceedings of the International Conference on Data Engineering; 2013.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer ScienceThe University of Hong KongHong KongChina