research-article

On computing temporal aggregates with range predicates

Authors:

Alexander Markowetz,

Vassilis J. Tsotras,

Dimitrios Gunopulos,

Bernhard SeegerAuthors Info & Claims

ACM Transactions on Database Systems (TODS), Volume 33, Issue 2

Article No.: 12, Pages 1 - 39

https://doi.org/10.1145/1366102.1366109

Published: 24 June 2008 Publication History

Abstract

Computing temporal aggregates 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 aggregate queries are critical. Previous approaches have aggregate predicates that involve only the time dimension. In this article we examine techniques to compute temporal aggregates that include key-range predicates as well (range-temporal aggregates). In particular we concentrate on the SUM aggregate, while COUNT is a special case. 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 aggregate queries. We analyze the performance of our approach and present experimental results that show its efficiency. Furthermore, we address a novel and practical variation called functional range-temporal aggregates. Here, the value of any record is a function over time. The meaning of aggregates is altered such that the contribution of a record to the aggregate result is proportional to the size of the intersection between the record's time interval and the query time interval. Both analytical and experimental results show the efficiency of our result.

References

[1]

Becker, B., Gschwind, S., Ohler, T., Seeger, B., and Widmayer, P. 1996. An asymptotically optimal multiversion B-tree. VLDB J. 5, 4, 264--275.]]

Digital Library

[2]

Chazelle, B. 1988. A functional approach to data structures and its use in multidimensional searching. SIAM J. Comput. 17, 427--462.]]

Digital Library

[3]

Gao, D., Gendrano, J. A. G., Moon, B., Snodgrass, R. T., Park, M., Huang, B. C., and Rodrigue, J. M. 2004. Main memory-based algorithms for efficient parallel aggregation for temporal databases. Distrib. Paral. Datab. 16, 2, 123--163.]]

Digital Library

[4]

Gendrano, J., Huang, B., Rodrigue, J., Moon, B., and Snodgrass, R. 1999. Parallel algorithms for computing temporal aggregates. In Proceedings of International Conference on Data Engineering (ICDE). 418--427.]]

Digital Library

[5]

Govindarajan, S., Agarwal, P. K., and Arge, L. 2003. CRB-Tree: An efficient indexing scheme for range-aggregate queries. In Proceedings of International Conference on Database Theory (ICDT). 143--157.]]

Digital Library

[6]

Jensen, C. S. and Snodgrass, R. 1999. Temporal data management. IEEE Trans. Knowl. Data Engine. (TKDE) 11, 1, 36--44.]]

Digital Library

[7]

Kang, S. T., Chung, Y. D., and Kim, M.-H. 2004. An efficient method for temporal aggregation with range-condition attributes. J. Inform. Sci. 168, 1-4, 243--265.]]

Digital Library

[8]

Kim, J. S., Kang, S. T., and Kim, M. H. 1999. Effective temporal aggregation using point-based trees. In Proceedings of International Conference on Database & Expert Systems Applications (DEXA). 1018--1030.]]

Digital Library

[9]

Kline, N. and Snodgrass, R. 1995. Computing temporal aggregates. In Proceedings of International Conference on Data Engineering (ICDE). 222--231.]]

Digital Library

[10]

Kline, N. and Soo, M. 1998. Time-IT, the time-integrated testbed. ftp://ftp.cs.arizona.edu/timecenter/time-it-0.1.tar.gz.]]

[11]

Moon, B., Lopez, I., and Immanuel, V. 2000. Scalable algorithms for large temporal aggregation. In Proceedings of International Conference on Data Engineering (ICDE). 145--154.]]

Digital Library

[12]

Preparata, F. and Shamos, M. 1985. Computational Geometry: An Introduction. Springer Verlag.]]

Digital Library

[13]

Salzberg, B. and Tsotras, V. J. 1999. Comparison of access methods for time-evolving data. ACM Comput. Surv. 31, 2, 158--221.]]

Digital Library

[14]

Tao, Y. and Papadias, D. 2004. Range aggregate processing in spatial databases. In IEEE Trans. Knowl. Data Engin. 1555--1570.]]

Digital Library

[15]

Tao, Y., Papadias, D., and Faloutsos, C. 2004. Approximate temporal aggregation. In Proceedings of International Conference on Data Engineering (ICDE). 190--201.]]

Digital Library

[16]

Tao, Y. and Xiao, X. 2008. Efficient temporal counting with bounded error. VLDB J.]]

Digital Library

[17]

Yang, J. and Widom, J. 2001. Incremental computation and maintenance of temporal aggregates. In Proceedings of International Conference on Data Engineering (ICDE). 51--60.]]

Digital Library

[18]

Yang, J. and Widom, J. 2003. Incremental computation and maintenance of temporal aggregates. In Proceedings of the International Conference on Very Large Data Bases (VLDB) 12, 3, 262--283.]]

Digital Library

[19]

Ye, X. and Keane, J. 1997. Processing temporal aggregates in parallel. In Proceedings of the International Conference on Systems, Man, and Cybernetics. 1373--1378.]]

[20]

Zhang, D., Markowetz, A., Tsotras, V. J., Gunopulos, D., and Seeger, B. 2001. Efficient computation of temporal aggregates with range predicates. In Proceedings of the ACM International Symposium on Principles of Database Systems (PODS). 237--245.]]

Digital Library

[21]

Zhang, D., Tsotras, V. J., and Gunopulos, D. 2002. Efficient aggregation over objects with extent. In Proceedings of the ACM International Symposium on Principles of Database Systems (PODS). 121--132.]]

Digital Library

Cited By

Xu JWong R(2023)Efficiently Answering Top-k Window Aggregate Queries: Calculating Coverage Number Sequences over Hierarchical Structures2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00104(1300-1312)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00104
Meel KVinodchandran NChakraborty SLibkin LPichler RGuagliardo P(2021)Estimating the Size of Union of Sets in Streaming ModelsProceedings of the 40th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems10.1145/3452021.3458333(126-137)Online publication date: 20-Jun-2021
https://dl.acm.org/doi/10.1145/3452021.3458333
Cheng KSawada SAbe K(2021)A New Interval Algebra for Temporal Data Modeling and Analysis2021 20th International Conference on Ubiquitous Computing and Communications (IUCC/CIT/DSCI/SmartCNS)10.1109/IUCC-CIT-DSCI-SmartCNS55181.2021.00064(353-360)Online publication date: Dec-2021
https://doi.org/10.1109/IUCC-CIT-DSCI-SmartCNS55181.2021.00064
Show More Cited By

Index Terms

On computing temporal aggregates with range predicates
1. Information systems
  1. Information systems applications

Recommendations

Efficient computation of temporal aggregates with range predicates
PODS '01: Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems

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 ...
Computing Temporal Aggregates
ICDE '95: Proceedings of the Eleventh International Conference on Data Engineering

Aggregate computation, such as selecting the minimum attribute value of a relation, is expensive, especially in a temporal database. We describe the basic techniques behind computing aggregates in conventional databases and show that these techniques ...
Temporal aggregates and temporal universal quantification in standard SQL

Although it has been acknowledged for many years that querying and updating time-varying information using standard (i.e., non-temporal) SQL is a challenging task, the proposed temporal extensions of SQL have not reach acceptance in the standardization ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Database Systems

ACM Transactions on Database Systems Volume 33, Issue 2

June 2008

309 pages

ISSN:0362-5915

EISSN:1557-4644

DOI:10.1145/1366102

Issue’s Table of Contents

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 June 2008

Accepted: 01 December 2007

Revised: 01 May 2007

Received: 01 April 2007

Published in TODS Volume 33, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

26
Total Citations
View Citations
546
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)1

Reflects downloads up to 18 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Xu JWong R(2023)Efficiently Answering Top-k Window Aggregate Queries: Calculating Coverage Number Sequences over Hierarchical Structures2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00104(1300-1312)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00104
Meel KVinodchandran NChakraborty SLibkin LPichler RGuagliardo P(2021)Estimating the Size of Union of Sets in Streaming ModelsProceedings of the 40th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems10.1145/3452021.3458333(126-137)Online publication date: 20-Jun-2021
https://dl.acm.org/doi/10.1145/3452021.3458333
Cheng KSawada SAbe K(2021)A New Interval Algebra for Temporal Data Modeling and Analysis2021 20th International Conference on Ubiquitous Computing and Communications (IUCC/CIT/DSCI/SmartCNS)10.1109/IUCC-CIT-DSCI-SmartCNS55181.2021.00064(353-360)Online publication date: Dec-2021
https://doi.org/10.1109/IUCC-CIT-DSCI-SmartCNS55181.2021.00064
Gao QLee MLing T(2021)Temporal Keyword Search with Aggregates and Group-ByConceptual Modeling10.1007/978-3-030-89022-3_14(160-175)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1007/978-3-030-89022-3_14
Fang CWang XXu JWang F(2021)Efficiently Detecting Light Events in Astronomical Temporal DataSpatial Data and Intelligence10.1007/978-3-030-69873-7_13(184-197)Online publication date: 28-Feb-2021
https://doi.org/10.1007/978-3-030-69873-7_13
Kumar S(2020)Multi-dimensional aggregation: a viable solution for interval dataInternational Journal of Information Technology10.1007/s41870-020-00462-4Online publication date: 2-May-2020
https://doi.org/10.1007/s41870-020-00462-4
Chen ZYao BWang ZZhang WZheng KKalnis PTang F(2020)ITISS: an efficient framework for querying big temporal dataGeoinformatica10.1007/s10707-019-00362-124:1(27-59)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1007/s10707-019-00362-1
Yao BZhang WWang ZChen ZShang SZheng KGuo M(2018)Distributed In-Memory Analytics for Big Temporal DataDatabase Systems for Advanced Applications10.1007/978-3-319-91452-7_36(549-565)Online publication date: 13-May-2018
https://doi.org/10.1007/978-3-319-91452-7_36
Pilman MKaufmann MKöhl FKossmann DProfeta DÖzcan FKoutrika GMadden S(2016)ParTimeProceedings of the 2016 International Conference on Management of Data10.1145/2882903.2903732(999-1010)Online publication date: 26-Jun-2016
https://dl.acm.org/doi/10.1145/2882903.2903732
Zhang HWang YZhang XLim E(2015)ReputationProACM Transactions on the Web10.1145/26973909:1(1-49)Online publication date: 23-Jan-2015
https://dl.acm.org/doi/10.1145/2697390
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents