Synonyms
Approximate aggregate query; On-line aggregation
Definition
Aggregate queries generally take a set of objects as input and produce a single scalar value as output, summarizing one aspect of the set. Commonly used aggregate types include MIN, MAX, AVG, SUM, and COUNT.
If the input set is very large, it might not be feasible to compute the aggregate precisely and in reasonable time. Alternatively, the precise value of the aggregate may not even be needed by the application submitting the query, e. g., if the aggregate value is to be mapped to an 8-bit color code for visualization. Hence, this motivates the use of approximate aggregate queries, which return a value close to the exact one, but at a fraction of the time.
Progressiveapproximate aggregate queries go one step further. They do not produce a single approximate answer, but continuously refine the answer as time goes on, progressively improving its quality. Thus, if the user has a fixed deadline, he can obtain the best...
Recommended Reading
Acharya, S., Gibbons, P., Poosala, V., Ramaswamy, S.: Joint synopses for approximate query answering. SIGMOD '99: Proceedings of the 1999 ACM SIGMOD international conference on Management of data, pp. 275–286. ACM Press, New York, NY, USA (1999)
Chakrabarti, K., Garofalakis, M.N., Rastogi, R., Shim, K.: Approximate query processing using wavelets. In: VLDB '00: Proceedings of the 26th International Conference on Very Large Data Bases, pp. 111–122. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2000)
Garofalakis, M., Kumar, A.: Wavelet synopses for general error metrics. ACM Trans. Database Syst. 30(4), 888–928 (2005)
Hellerstein, J.M., Haas, P.J., Wang, H.J.: Online aggregation. In: SIGMOD '97: Proceedings of the 1997 ACM SIGMOD International conference on Management of data, pp. 171–182. ACM Press, New York, NY, USA (1997)
Ioannidis, Y.E., Poosala, V.: Histogram-based approximation of set-valued query-answers. In: VLDB '99: Proceedings of the 25th International Conference on Very Large Data Bases, pp. 174–185. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999)
Karras, P., Mamoulis, N.: One-pass wavelet synopses for maximum-error metrics. In: VLDB '05: Proceedings of the 31st international conference on Very large data bases, pp. 421–432, Trondheim, Norway. VLDB Endowment (2005)
Lazaridis, I., Mehrotra, S.: Progressive approximate aggregate queries with a multi-resolution tree structure. In: SIGMOD '01: Proceedings of the 2001 ACM SIGMOD international conference on Management of data, pp. 401–412. ACM Press, New York, NY, USA (2001)
Lazaridis, I., Mehrotra, S.: Approximate selection queries over imprecise data. In: ICDE '04: Proceedings of the 20th International Conference on Data Engineering, p.140. Washington, DC, USA, IEEE Computer Society (2004)
Lenz, H.J., Jurgens, M.: The Ra*-tree: An improved r-tree with materialized data for supporting range queries on olap-data. In: DEXA Workshop, 1998
Papadias, D., Kalnis, P., Zhang, J., Tao, Y.: Efficient OLAP operations in spatial data warehouses. In: SSTD '01: Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases, pp. 443–459. London, UK, Springer-Verlag (2001)
Porkaew, K., Lazaridis, I., Mehrotra, S., Winkler, R.:Database Support for Situational Awareness. In: Vassiliop, M.S., Huang, T.S. Computer-Science Handbook for Displays – Summary of Findings from the Army Research Lab's Advanced Displays & Interactive Displays Federated Laboratory. Rockwell Scientific Company (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag
About this entry
Cite this entry
Lazaridis, I., Mehrotra, S. (2008). Aggregate Queries, Progressive Approximate. In: Shekhar, S., Xiong, H. (eds) Encyclopedia of GIS. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-35973-1_41
Download citation
DOI: https://doi.org/10.1007/978-0-387-35973-1_41
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-30858-6
Online ISBN: 978-0-387-35973-1
eBook Packages: Computer ScienceReference Module Computer Science and Engineering