An intensional approach for periodic data in relational databases

Abstract

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 explicitly storing and accessing such data very challenging. In this paper, we propose a new methodology, based on an implicit representation of periodic data. The representation model we propose captures the notion of periodic granularity provided by the temporal database glossary, and is a consistent extension of the TSQL2 temporal relational data model. On top of our new data model, we propose a suitable indexing technique. We define the algebraic operators, and introduce access algorithms to cope with them, proving that they are correct and complete with respect to the traditional explicit approach. We also propose an experimental evaluation of our approach. Keywords temporal databases • periodic data • implicit representation 1 Introduction Periodic events seem to be an intrinsic part of our life, and of our way of perceiving reality. Day and nights repeat at regular periodic patterns, as well as seasons,

Periodic Data, Burden or Convenience

“…we have taken into account range queries, providing sound and complete query answering algorithms for them; we have extended Codd's algebraic operators of Cartesian product, Union, Projection, nontemporal selection, and Difference, in order to provide a complete query language coping with implicit periodic data; Such operators are sound and complete, and are a consistent extension of BCDM (and TSQL2) algebra. Details can be found [18]. in an extensive experimentation of our model and methodology, we showed that our 'implicit' approach overcomes the performance of traditional 'explicit' approaches both in terms of space and disk I/O's, and in terms of query response time.…”

“…We rely on the definition of granularity taken from the temporal database glossary [12] and adapted in [18], [19]. Such definitions are the basis for our treatment of periodic data.…”

“…The Pattern is a relation over the schema (PatID, Start, End), where PatID is an attribute containing identifiers denoting periodic patterns, while Start and End are temporal attributes denoting the starting and the ending points of the periods in the periodic pattern. Algorithm 1: Implicit temporal range queries Algorithms for 'atemporal range queries' can be found in [18] and 'explicit temporal range queries' on implicit model is straight forward by making explicit answer from 'implicit temporal range queries'. In following subsection we provide algorithm for implicit temporal range queries.…”

“…It is important to mention that the ratio in example where we simulated small hospital was 157. More details of other experiments can be found in [18].…”