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  • A General Approach to Represent and Query Now-Relative Medical Data in Relational Databases

    Author(s)
    Anselma, Luca
    Piovesan, Luca
    Sattar, Abdul
    Stantic, Bela
    Terenziani, Paolo
    Griffith University Author(s)
    Stantic, Bela
    Sattar, Abdul
    Year published
    2015
    Metadata
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    Abstract
    Now-related temporal data play an important role in the medical context. Current relational temporal database (TDB) approaches are limited since (i) they (implicitly) assume that the span of time occurring between the time when facts change in the world and the time when the changes are recorded in the database is exactly known, and (ii) do not explicitly provide an extended relational algebra to query now-related data. We propose an approach that, widely adopting AI symbolic manipulation techniques, overcomes the above limitations.Now-related temporal data play an important role in the medical context. Current relational temporal database (TDB) approaches are limited since (i) they (implicitly) assume that the span of time occurring between the time when facts change in the world and the time when the changes are recorded in the database is exactly known, and (ii) do not explicitly provide an extended relational algebra to query now-related data. We propose an approach that, widely adopting AI symbolic manipulation techniques, overcomes the above limitations.
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    Journal Title
    Lecture Notes in Computer Science
    Volume
    9105
    DOI
    https://doi.org/10.1007/978-3-319-19551-3_41
    Subject
    Other information and computing sciences not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/125351
    Collection
    • Journal articles

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