Abstract
Temporal aggregation is an important but costly operation for applications that maintain time-evolving data (data warehouses, temporal databases, etc.). In this paper we examine the problem of computing temporal aggregates over data streams. Such aggregates are maintained using multiple levels of temporal granularities: older data is aggregated using coarser granularities while more recent data is aggregated with finer detail. We present specialized indexing schemes for dynamically and progressively maintaining temporal aggregates. Moreover, these schemes can be parameterized. The levels of granularity as well as their corresponding index sizes (or validity lengths) can be dynamically adjusted. This provides a useful trade-o. between aggregation detail and storage space. Analytical and experimental results show the efficiency of the proposed structures. Moreover, we discuss how the indexing schemes can be extended to solve the more general range temporal and spatiotemporal aggregation problems.
This work was partially supported by NSF CAREER Award 9984729, NSF IIS- 9907477, the DoD and AT&T.
This work was partially supported by NSF grants IIS-9907477, EIA-9983445, and the Department of Defense.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
P. Agarwal and J. Erickson, “Geometric Range Searching and Its Relatives”, Advances in Discrete and Computational Geometry, B. Chazelle, E. Goodman and R. Pollack (ed.), American Mathematical Society, Providence, 1998.
J. L. Bentley, “Multidimensional Divide-and-Conquer”, Communications of the ACM23(4), 1980.
C. Bettini, “Semantic Compression of Temporal Data”, Proc. of WAIM, 2001.
C. Bettini, C. E. Dyreson, W. S. Evans, R. T. Snodgrass, and X. S. Wang, “A Glossary of Time Granularity Concepts”, O. Etzion, S. Jajodia and S. M. Sripada (eds.), Temporal Databases: Research and Practice, LNCS 1399, 1998.
B. Becker, S. Gschwind, T. Ohler, B. Seeger and P. Widmayer, “An Asymptotically Optimal Multiversion B-Tree”, VLDB Journal 5(4), 1996.
C. Bettini, S. Jajodia and X. S. Wang, Time Granularities in Databases, Data Mining, and Temporal Reasoning, Springer, 2000.
M. de Berg, M. van Kreveld, M. Overmars and O. Schwarzkopf, Computational Geometry: Algorithms and Applications, Springer-Verlag Berlin Heidelberg, Germany, ISBN 3-540-61270-X, 1997.
C. Bettini and R. De Sibi, “Symbolic Representation of User-Defined Time Granularities”, Proc. of TIME, 1999.
C. Bettini, X. S. Wang and S. Jajodia, “Temporal Semantic Assumptions and Their Use in Databases”, IEEE TKDE 10(2), 1998.
P. Domingos and G. Hulten, “Mining high-speed data streams”, Proc. of SIGKDD, 2000.
J. Gendrano, B. Huang, J. Rodrigue, B. Moon and R. Snodgrass, ldParallel Algorithms for Computing Temporal Aggregates”, Proc. of ICDE, 1999.
J. Gehrke, F. Korn and D. Srivastava, “On Computing Correlated Aggregates over Continual Data Streams”, Proc. of SIGMOD, 2001.
S. Guha, N. Koudas and K. Shim, “Data-Streams and Histograms”, Proc. of STOC, 2001.
H. Garcia-Molina, W. Labio and J. Yang, “Expiring Data in a Warehouse”, Proc. of VLDB, 1998.
S. Guha, N. Mishra, R. Motwani and L. O’Callaghan, “Clustering Data Streams”, Proc. of FOCS, 2000.
J. Hellerstein, P. Haas and H. Wang, “Online Aggregation”, Proc. of SIGMOD, 1997.
M. R. Henziger, P. Raghavan and S. Rajagopalan, “Computing on Data Streams”, TechReport 1998-011, DEC, May 1998.
H. V. Jagadish, I. S. Mumick and A. Silberschatz, “View maintenance issues for the chronicle data model”, Proc. of PODS, 1995.
N. Kline and R. Snodgrass, “Computing Temporal Aggregates”, Proc. of ICDE, 1995.
I. Lazaridis and S. Mehrotra, “Progressive Approximate Aggregate Queries with a Multi-Resolution Tree Structure”, Proc. of SIGMOD, 2001.
J. Matoušek, “Geometric Range Searching”, Computing Surveys 26(4), 1994.
B. Moon, I. Lopez and V. Immanuel, “Scalable Algorithms for Large Temporal Aggregation”, Proc. of ICDE, 2000.
F. Preparata and M. Shamos, Computational Geometry: An Introduction, Springer-Verlag, Berlin/Heidelberg, Germany, 1985.
D. Papadias, Y. Tao, P. Kalnis and J. Zhang, “Indexing Spatio-Temporal Data Warehouses”, Proc. of ICDE, 2002.
J. Skyt, C. S. Jensen and T. B. Pedersen, “Specification-Based Data Reduction in Dimensional Data Warehouses”, TimeCenter TechReport TR-61, 2001.
P. Tuma, “Implementing Historical Aggregates in TempIS”, Master’s thesis,Wayne State University, Michigan, 1992.
X. Ye and J. Keane, “Processing temporal aggregates in parallel”, Proc. of Int. Conf. on Systems, Man, and Cybernetics, 1997.
J. Yang and J. Widom, “Incremental Computation and Maintenance of Temporal Aggregates”, Proc. of ICDE, 2001.
D. Zhang, A. Markowetz, V. J. Tsotras, D. Gunopulos and B. Seeger, “Efficient Computation of Temporal Aggregates with Range Predicates”, Proc. of PODS, 2001.
D. Zhang, V. J. Tsotras, D. Gunopulos and A. Markowetz, “Efficient Aggregation over Objects with Extent”, TechReport UCR CS 01 01, CS Dept., UC Riverside, 2001. http://www.cs.ucr.edu/?donghui/publications/boxaggr.ps
D. Zhang, D. Gunopulos, V. J. Tsotras, and B. Seeger, “Temporal Aggregation over Data Streams using Multiple Granularities” (full version), http://www.cs.ucr.edu/?donghui/publications/hta_full.ps
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, D., Gunopulos, D., Tsotras, V.J., Seeger, B. (2002). Temporal Aggregation over Data Streams Using Multiple Granularities. In: Jensen, C.S., et al. Advances in Database Technology — EDBT 2002. EDBT 2002. Lecture Notes in Computer Science, vol 2287. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45876-X_40
Download citation
DOI: https://doi.org/10.1007/3-540-45876-X_40
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43324-8
Online ISBN: 978-3-540-45876-0
eBook Packages: Springer Book Archive