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  • Towards an implicit treatment of periodically-repeated medical data

    Author(s)
    Stantic, Bela
    Terenziani, Paolo
    Sattar, Abdul
    Bottrighi, Alessio
    Governatori, Guido
    Griffith University Author(s)
    Stantic, Bela
    Sattar, Abdul
    Governatori, Guido
    Terenziani, Paolo
    Year published
    2010
    Metadata
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    Abstract
    Temporal information plays a crucial role in medicine, so that in Medical Informatics there is an increasing awareness that suitable database approaches are needed to store and support it. Specifically, a great amount of clinical data (e.g., therapeutic data) are periodically repeated. Although an explicit treatment is possible in most cases, it causes severe storage and disk I/O problems. In this paper, we propose an innovative approach to cope with periodic medical data in an implicit way. We propose a new data model, representing periodic data in a compact (implicit) way, which is a consistent extension of TSQL2 consensus ...
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    Temporal information plays a crucial role in medicine, so that in Medical Informatics there is an increasing awareness that suitable database approaches are needed to store and support it. Specifically, a great amount of clinical data (e.g., therapeutic data) are periodically repeated. Although an explicit treatment is possible in most cases, it causes severe storage and disk I/O problems. In this paper, we propose an innovative approach to cope with periodic medical data in an implicit way. We propose a new data model, representing periodic data in a compact (implicit) way, which is a consistent extension of TSQL2 consensus approach. Then, we identify some important types of temporal queries, and present query answering algorithms to answer them. We also sketch a temporal relational algebra for our approach. Finally, we show experimentally that our approach outperforms current explicit approaches.
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    Conference Title
    MEDINFO 2010, PTS I AND II
    Volume
    160
    Issue
    PART 1
    DOI
    https://doi.org/10.3233/978-1-60750-588-4-1131
    Subject
    Data engineering and data science
    Library and information studies
    Publication URI
    http://hdl.handle.net/10072/37730
    Collection
    • Conference outputs

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