Efficient computation of temporal aggregates with range predicates

A temporal aggregation query is an important but costly operation for applications that maintain time-evolving data (data warehouses, temporal databases, etc.). Due to the large volume of such data, performance improvements for temporal aggregation queries are critical. In this paper we examine techniques to compute temporal aggregates that include key-range predicates (range temporal aggregates). In particular we concentrate on SUM, COUNT and AVG aggregates. This problem is novel; to handle arbitrary key ranges, previous methods would need to keep a separate index for every possible key range. We propose an approach based on a new index structure called the Multiversion SB-Tree, which incorporates features from both the SB-Tree and the Multiversion B-Tree, to handle arbitrary key-range temporal SUM, COUNT and AVG queries. We analyze the performance of our approach and present experimental results that show its efficiency.

[1]  David B. Lomet,et al.  Access methods for multiversion data , 1989, SIGMOD '89.

[2]  Bernhard Seeger,et al.  An asymptotically optimal multiversion B-tree , 1996, The VLDB Journal.

[3]  Jennifer Widom,et al.  Incremental computation and maintenance of temporal aggregates , 2001, Proceedings 17th International Conference on Data Engineering.

[4]  Ramez Elmasri,et al.  A consensus glossary of temporal database concepts , 1994, SGMD.

[5]  Bongki Moon,et al.  Scalable algorithms for large temporal aggregation , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[6]  Ramez Elmasri,et al.  The Consensus Glossary of Temporal Database Concepts - February 1998 Version , 1997, Temporal Databases, Dagstuhl.

[7]  Xinfeng Ye,et al.  Processing temporal aggregates in parallel , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[8]  Richard T. Snodgrass,et al.  Computing temporal aggregates , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[9]  Hans-Peter Kriegel,et al.  The R*-tree: an efficient and robust access method for points and rectangles , 1990, SIGMOD '90.

[10]  Richard T. Snodgrass,et al.  Parallel algorithms for computing temporal aggregates , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[11]  Christos Faloutsos,et al.  Designing Access Methods for Bitemporal Databases , 1998, IEEE Trans. Knowl. Data Eng..

[12]  Arie Shoshani,et al.  The Representation of a Temporal Data Model in the Relational Environment , 1988, SSDBM.

[13]  Vassilis J. Tsotras,et al.  Comparison of access methods for time-evolving data , 1999, CSUR.

[14]  Jennifer Widom,et al.  Incremental computation and maintenance of temporal aggregates , 2003, The VLDB Journal.

[15]  Michael Ian Shamos,et al.  Computational geometry: an introduction , 1985 .

[16]  Divyakant Agrawal,et al.  The Dynamic Data Cube , 2000, EDBT.

[17]  Sridhar Ramaswamy,et al.  A Unified Approach for Indexed and Non-Indexed Spatial Joins , 2000, EDBT.

[18]  Arie Segev,et al.  A consensus glossary of temporal database concepts , 1994, SIGMOD 1994.