Querying Temporal Anomalies in Healthcare Information Systems and Beyond [BEST PAPER AWARD]

Querying Temporal Anomalies in Healthcare Information Systems and Beyond [BEST PAPER AWARD]. Khnaisser, C., Hamrouni, H., Blumenthal, D. B., Dignös, A., & Gamper, J. In Chiusano, S., Cerquitelli, T., & Wrembel, R., editors, Advances in Databases and Information Systems, pages 209–222, 2022. Springer International Publishing.
abstract bibtex

Finding anomalies in temporal relational databases is a difficult and challenging task, in particular if data is integrated from different sources. The problem is especially pressing in healthcare information systems, where temporal anomalies can pinpoint critical events such as erroneous drug administration or prescription. In this paper, we define three different temporal anomalies, which we call temporal redundancy, contradiction, and incompleteness. We define two different operators for each of these anomalies: the retrieval operator to retrieve all tuples of a relation that cause anomalous behaviour, and the labelling operator to annotate a temporal relation with additional information that marks normal and anomalous tuples. Finally, we present and evaluate different implementation techniques for the two operators for relational database systems.

@inproceedings{khnaisser_querying_2022,
	title = {Querying {Temporal} {Anomalies} in {Healthcare} {Information} {Systems} and {Beyond} [{BEST} {PAPER} {AWARD}]},
	isbn = {978-3-031-15740-0},
	abstract = {Finding anomalies in temporal relational databases is a difficult and challenging task, in particular if data is integrated from different sources. The problem is especially pressing in healthcare information systems, where temporal anomalies can pinpoint critical events such as erroneous drug administration or prescription. In this paper, we define three different temporal anomalies, which we call temporal redundancy, contradiction, and incompleteness. We define two different operators for each of these anomalies: the retrieval operator to retrieve all tuples of a relation that cause anomalous behaviour, and the labelling operator to annotate a temporal relation with additional information that marks normal and anomalous tuples. Finally, we present and evaluate different implementation techniques for the two operators for relational database systems.},
	booktitle = {Advances in {Databases} and {Information} {Systems}},
	publisher = {Springer International Publishing},
	author = {Khnaisser, Christina and Hamrouni, Hind and Blumenthal, David B. and Dignös, Anton and Gamper, Johann},
	editor = {Chiusano, Silvia and Cerquitelli, Tania and Wrembel, Robert},
	year = {2022},
	keywords = {Conference Paper, MEIE},
	pages = {209--222},
}

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