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Querying Temporal Anomalies in Healthcare Information Systems and Beyond

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Advances in Databases and Information Systems (ADBIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13389))

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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.

Supported by Ministère de l’Économie et de l’Innovation – Québec and by the Autonomous Province of Bozen-Bolzano with research call “Research Südtirol/Alto Adige 2019” (project Enabling Industrial-Strength, Open-Source Temporal Query Processing – ISTeP).

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Notes

  1. 1.

    See: https://www.postgresql.org/docs/current/functions-aggregate.html for PostgreSQL or https://docs.microsoft.com/en-us/u-sql/functions/aggregate/array-agg for MS SQLServer.

References

  1. Böhlen, M.H., Dignös, A., Gamper, J., Jensen, C.S.: Temporal data management – an overview. In: Zimányi, E. (ed.) eBISS 2017. LNBIP, vol. 324, pp. 51–83. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-96655-7_3

    Chapter  Google Scholar 

  2. Böhlen, M.H., Snodgrass, R.T., Soo, M.D.: Coalescing in temporal databases. In: VLDB, pp. 180–191. Morgan Kaufmann (1996)

    Google Scholar 

  3. Bouros, P., Mamoulis, N., Tsitsigkos, D., Terrovitis, M.: In-memory interval joins. VLDB J. 30(4), 667–691 (2021). https://doi.org/10.1007/s00778-020-00639-0

    Article  Google Scholar 

  4. Combi, C., Degani, S., Jensen, C.S.: Capturing temporal constraints in temporal ER models. In: Li, Q., Spaccapietra, S., Yu, E., Olivé, A. (eds.) ER 2008. LNCS, vol. 5231, pp. 397–411. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87877-3_29

    Chapter  Google Scholar 

  5. Combi, C., Keravnou-Papailiou, E., Shahar, Y.: Temporal Information Systems in Medicine, 1st edn. Springer, New York (2010). https://doi.org/10.1007/978-1-4419-6543-1

    Book  Google Scholar 

  6. Date, C.J., Darwen, H., Lorentzos, N.A.: Time and Relational Theory: Temporal Databases in the Relational Model and SQL. Morgan Kaufmann (2014)

    Google Scholar 

  7. Dignös, A., Böhlen, M.H., Gamper, J., Jensen, C.S.: Extending the kernel of a relational DBMS with comprehensive support for sequenced temporal queries. ACM Trans. Database Syst. 41(4), 26:1–26:46 (2016)

    Google Scholar 

  8. Dignös, A., Böhlen, M.H., Gamper, J., Jensen, C.S., Moser, P.: Leveraging range joins for the computation of overlap joins. VLDB J. 31(1), 75–99 (2021). https://doi.org/10.1007/s00778-021-00692-3

    Article  Google Scholar 

  9. Dignös, A., Glavic, B., Niu, X., Gamper, J., Böhlen, M.H.: Snapshot semantics for temporal multiset relations. Proc. VLDB Endow. 12(6), 639–652 (2019)

    Article  Google Scholar 

  10. Dong, X.L., Kementsietsidis, A., Tan, W.: A time machine for information: looking back to look forward. SIGMOD Rec. 45(2), 23–32 (2016)

    Article  Google Scholar 

  11. Ethier, J.F., Goyer, F., Fabry, P., Barton, A.: The prescription of drug ontology 2.0 (PDRO): more than the sum of its parts. Int. J. Environ. Res. Public Health 18(22), 12025 (2021)

    Article  Google Scholar 

  12. Jensen, C.S., Snodgrass, R.T.: Timeslice operator. In: Liu, L., Özsu, M.T. (eds.) Encyclopedia of Database Systems, pp. 3120–3121. Springer, Boston (2009). https://doi.org/10.1007/978-0-387-39940-9_1426

    Chapter  Google Scholar 

  13. Johnson, A., Bulgarelli, L., Pollard, T., Horng, S., Celi, L.A., Mark, R.: Mimic-iv (2020). https://doi.org/10.13026/A3WN-HQ05. https://physionet.org/content/mimiciv/0.4/

  14. Kaufmann, M., et al.: Timeline index: a unified data structure for processing queries on temporal data in SAP HANA. In: SIGMOD Conference, pp. 1173–1184 (2013)

    Google Scholar 

  15. Khnaisser, C., Lavoie, L., Burgun, A., Ethier, J.-F.: Past indeterminacy in data warehouse design. In: Benslimane, D., Damiani, E., Grosky, W.I., Hameurlain, A., Sheth, A., Wagner, R.R. (eds.) DEXA 2017. LNCS, vol. 10439, pp. 90–100. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64471-4_9

    Chapter  Google Scholar 

  16. Kulkarni, K.G., Michels, J.: Temporal features in SQL: 2011. SIGMOD Rec. 41(3), 34–43 (2012)

    Article  Google Scholar 

  17. Lorentzos, N.A., Johnson, R.G.: Extending relational algebra to manipulate temporal data. Inf. Syst. 13(3), 289–296 (1988)

    Article  Google Scholar 

  18. Lorentzos, N.A., Mitsopoulos, Y.G.: SQL extension for interval data. IEEE Trans. Knowl. Data Eng. 9(3), 480–499 (1997)

    Article  Google Scholar 

  19. Lyson, H.C., et al.: A qualitative analysis of outpatient medication use in community settings: observed safety vulnerabilities and recommendations for improved patient safety. J. Patient Saf. 17(4), e335–e342 (2019)

    Article  Google Scholar 

  20. Özsoyoglu, G., Snodgrass, R.T.: Temporal and real-time databases: a survey. IEEE Trans. Knowl. Data Eng. 7(4), 513–532 (1995)

    Article  Google Scholar 

  21. Piatov, D., Helmer, S.: Sweeping-based temporal aggregation. In: Gertz, M., et al. (eds.) SSTD 2017. LNCS, vol. 10411, pp. 125–144. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64367-0_7

    Chapter  Google Scholar 

  22. Piatov, D., Helmer, S., Dignös, A.: An interval join optimized for modern hardware. In: ICDE, pp. 1098–1109. IEEE Computer Society (2016)

    Google Scholar 

  23. Won, S.-M., Kim, M.-H., Kim, J.-M.: Administration management system design for smart phone applications in use of QR code. In: Park, J.J.J.H., Ng, J.K.-Y., Jeong, H.Y., Waluyo, B. (eds.) Multimedia and Ubiquitous Engineering. LNEE, vol. 240, pp. 585–592. Springer, Dordrecht (2013). https://doi.org/10.1007/978-94-007-6738-6_71

    Chapter  Google Scholar 

  24. Zhou, X., Wang, F., Zaniolo, C.: Efficient temporal coalescing query support in relational database systems. In: Bressan, S., Küng, J., Wagner, R. (eds.) DEXA 2006. LNCS, vol. 4080, pp. 676–686. Springer, Heidelberg (2006). https://doi.org/10.1007/11827405_66

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Université de Sherbrooke, Sherbrooke, Canada

    Christina Khnaisser

  2. Free University of Bozen-Bolzano, Bozen, Italy

    Hind Hamrouni, Anton Dignös & Johann Gamper

  3. Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany

    David B. Blumenthal

Authors
  1. Christina Khnaisser
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  2. Hind Hamrouni
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  3. David B. Blumenthal
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  4. Anton Dignös
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  5. Johann Gamper
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Corresponding author

Correspondence to Christina Khnaisser .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Silvia Chiusano

  2. Politecnico di Torino, Turin, Italy

    Tania Cerquitelli

  3. Poznań University of Technology, Poznań, Poland

    Robert Wrembel

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Khnaisser, C., Hamrouni, H., Blumenthal, D.B., Dignös, A., Gamper, J. (2022). Querying Temporal Anomalies in Healthcare Information Systems and Beyond. In: Chiusano, S., Cerquitelli, T., Wrembel, R. (eds) Advances in Databases and Information Systems. ADBIS 2022. Lecture Notes in Computer Science, vol 13389. Springer, Cham. https://doi.org/10.1007/978-3-031-15740-0_16

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  • DOI: https://doi.org/10.1007/978-3-031-15740-0_16

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