An intensional approach for periodic data in relational databases
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Periodic data play a major role in many application domains, spanning from manufacturing to office automation, from scheduling to data broadcasting. In many of such domains, the huge number of repetitions make the goal of extesionally storing and accessing such data very challenging. In this paper, we propose a new methodology, based on an intensional representation of periodic data. The representation model we propose captures the notion of periodic granularity provided by the temporal database glossary, and is an extension of the TSQL2 temporal relational data model. We define the algebraic operators, and introduce access algorithms to cope with them, proving that they are correct with respect to the traditional extesional approach.We also provide an experimental evaluation of our approach.
Journal of Intelligent Information Systems
© 2013 Springer Netherlands. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com.
Pattern Recognition and Data Mining