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.

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

ANSELMA, LUCA;PIOVESAN, LUCA;
2015

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.
15th Conference on Artificial Intelligence in Medicine, AIME 2015
Pavia, Italy
June 17-20, 2015
Artificial Intelligence in Medicine
Springer International Publishing
9105
327
331
978-3-319-19550-6
978-3-319-19551-3
978-3-319-19550-6
978-3-319-19551-3
http://link.springer.com/chapter/10.1007%2F978-3-319-19551-3_41
temporal relational database, now-related data, temporal algebra
Anselma, Luca; Piovesan, Luca; Sattar, Abdul; Stantic, Bela; Terenziani, Paolo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1521947
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