Synonyms
Imprecise spatial queries
Definition
An uncertain item is defined as a range-limited probability density function (pdf) in a multi-dimensional space, which can model the uncertainty of location, sensor and biological data. Given a set of uncertain items, a probabilistic spatial query returns results augmented with probabilistic guarantees for the validity of answers. The impreciseness of query answers is an inherent property of these applications due to data uncertainty, unlike the techniques for approximate processing that trade accuracy for performance. New query definitions, processing and indexing techniques are required to handle these queries.
Historical Background
Data uncertainty is an inherent property in a number of important and emerging applications. Consider, for example, a habitat monitoring system used in scientific applications, where data such as temperature, humidity, and wind speed are acquired from a sensor network. Due to physical imperfection of the...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Recommended Reading
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.
Chen J, Cheng R. Efficient evaluation of imprecise location-dependent queries. In: Proceedings of the 23rd International Conference on Data Engineering; 2007.
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.
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.
Cheng R, Kalashnikov DV, Prabhakar S. Querying imprecise data in moving object environments. IEEE Trans Knowl Data Eng. 2004;16(9):1112–27.
Cheng R, Singh S, Prabhakar S, Shah R, Vitter J, Xia Y. Efficient join processing over uncertain data. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management; 2006.
Cheng R, Xia Y, Prabhakar S, Shah R, Vitter JS. Efficient indexing methods for probabilistic threshold queries over uncertain data. In: Proceedings of the 30th International Conference on Very Large Data Bases; 2004. p. 876–87.
Dai X, Yiu ML, Mamoulis N, Tao Y, Vaitis M. Probabilistic spatial queries on existentially uncertain data. In: Proceedings of the 9th International Symposium on Advances in Spatial and Temporal Databases; 2005. p. 400–17.
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.
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.
Ljosa V, Singh A. APLA: indexing arbitrary probability distributions. In: Proceedings of the 23rd International Conference on Data Engineering; 2007. p. 946–55.
Parker A, Subrahmanian V, Grant J. A logical formulation of probabilistic spatial databases. IEEE Trans Knowl Data Eng. 2007;19(11):1541–56.
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.
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.
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.
Sistla PA, Wolfson O, Chamberlain S, Dao S. Querying the uncertain position of moving objects. In: Etzion O, Jajodia S, Sripada S, editors. Temporal databases: research and practice. Berlin/New York: Springer; 1998.
Tao Y, Cheng R, Xiao X, Ngai WK, Kao B, Prabhakar S. Indexing multi-dimensional uncertain data with arbitrary probability density functions. In: Proceedings of the 31st International Conference on Very Large Data Bases; 2005. p. 922–33.
Tao Y, Xiao X, Cheng R. Range search on multidimensional uncertain data. ACM Trans Database Syst. 2007;32(3):15.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media, LLC, part of Springer Nature
About this entry
Cite this entry
Cheng, R., Chen, J. (2018). Probabilistic Spatial Queries. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_276
Download citation
DOI: https://doi.org/10.1007/978-1-4614-8265-9_276
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-8266-6
Online ISBN: 978-1-4614-8265-9
eBook Packages: Computer ScienceReference Module Computer Science and Engineering