Publication

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.

@inproceedings{abc,
	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.},
	author = {Chang Ge and Martin Kaufmann and Lukasz Golab and Peter M. Fischer and Anil K. Goel},
	booktitle = {Proceedings of the 27th International Conference on Scientific and Statistical Database Management, SSDBM {\textquoteright}15},
	title = {Indexing bi-temporal windows.},
	url = {http://doi.acm.org/10.1145/2791347.2791373},
	venue = {La Jolla, CA, USA},
	year = {2015}
}