Indexing Bi-Temporal windows

Chang Ge; Martin Kaufmann; Lukasz Golab; Peter M. Fischer; Anil K. Goel

doi:10.1145/2791347.2791373

Indexing Bi-Temporal windows

Chang Ge, Martin Kaufmann, Lukasz Golab, Peter M. Fischer, Anil K. Goel

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

Bi-Temporal databases support system (transaction) and application time, enabling users to query the history as recorded today and as it was known in the past. In this paper, we study windows over both system and application time, i.e., bi-Temporal windows. We propose a two-dimensional index that supports one-Time and continuous queries over fixed and sliding bi-Temporal windows, covering static and streaming data. We demonstrate the advantages of the proposed index compared to the state-of-The-Art in terms of query performance, index update overhead and space footprint.

Original language	English (US)
Title of host publication	SSDBM 2015 - Proceedings of the 27th International Conference on Scientific and Statistical Database Management
Editors	Amarnath Gupta, Susan Rathbun
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450337090
DOIs	https://doi.org/10.1145/2791347.2791373
State	Published - Jun 29 2015
Externally published	Yes
Event	27th International Conference on Scientific and Statistical Database Management, SSDBM 2015 - San Diego, United States Duration: Jun 29 2015 → Jul 1 2015

Publication series

Name	ACM International Conference Proceeding Series
Volume	29-June-2015

Conference

Conference	27th International Conference on Scientific and Statistical Database Management, SSDBM 2015
Country/Territory	United States
City	San Diego
Period	6/29/15 → 7/1/15

Bibliographical note

Publisher Copyright:
© 2015 Copyright held by the owner/author(s).

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10.1145/2791347.2791373

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Ge, C., Kaufmann, M., Golab, L., Fischer, P. M., & Goel, A. K. (2015). Indexing Bi-Temporal windows. In A. Gupta, & S. Rathbun (Eds.), SSDBM 2015 - Proceedings of the 27th International Conference on Scientific and Statistical Database Management Article a19 (ACM International Conference Proceeding Series; Vol. 29-June-2015). Association for Computing Machinery. https://doi.org/10.1145/2791347.2791373

Indexing Bi-Temporal windows. / Ge, Chang; Kaufmann, Martin; Golab, Lukasz et al.
SSDBM 2015 - Proceedings of the 27th International Conference on Scientific and Statistical Database Management. ed. / Amarnath Gupta; Susan Rathbun. Association for Computing Machinery, 2015. a19 (ACM International Conference Proceeding Series; Vol. 29-June-2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ge, C, Kaufmann, M, Golab, L, Fischer, PM & Goel, AK 2015, Indexing Bi-Temporal windows. in A Gupta & S Rathbun (eds), SSDBM 2015 - Proceedings of the 27th International Conference on Scientific and Statistical Database Management., a19, ACM International Conference Proceeding Series, vol. 29-June-2015, Association for Computing Machinery, 27th International Conference on Scientific and Statistical Database Management, SSDBM 2015, San Diego, United States, 6/29/15. https://doi.org/10.1145/2791347.2791373

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N2 - Bi-Temporal databases support system (transaction) and application time, enabling users to query the history as recorded today and as it was known in the past. In this paper, we study windows over both system and application time, i.e., bi-Temporal windows. We propose a two-dimensional index that supports one-Time and continuous queries over fixed and sliding bi-Temporal windows, covering static and streaming data. We demonstrate the advantages of the proposed index compared to the state-of-The-Art in terms of query performance, index update overhead and space footprint.

AB - Bi-Temporal databases support system (transaction) and application time, enabling users to query the history as recorded today and as it was known in the past. In this paper, we study windows over both system and application time, i.e., bi-Temporal windows. We propose a two-dimensional index that supports one-Time and continuous queries over fixed and sliding bi-Temporal windows, covering static and streaming data. We demonstrate the advantages of the proposed index compared to the state-of-The-Art in terms of query performance, index update overhead and space footprint.

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Indexing Bi-Temporal windows

Abstract

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