title: An implicit approach to deal with periodically repeated medical data.
abstract: BACKGROUND
  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.
  
  
  OBJECTIVE
  In this paper, we propose an innovative approach to cope with periodic relational medical data in an implicit way.
  
  
  METHODS
  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. Finally, we also run experiments to evaluate our approach.
  
  
  RESULTS
  The experiments show that our approach outperforms current explicit approaches, especially as regard disk I/O.
  
  
  CONCLUSIONS
  We have provided an implicit approach to periodic data with is a consistent extension of TSQL2 (and which is thus grant interoperable with it), and we have experimentally proven that it outperforms current explicit approaches.
authors.affiliation: Institute for Integrated and Intelligent Systems, Griffith University, 170 Kessels Road Nathan, Queensland 4111, Australia.
authors.forename: Bela
authors.initials: B
authors.lastname: Stantic
authors.forename: Paolo
authors.initials: P
authors.lastname: Terenziani
authors.forename: Guido
authors.initials: G
authors.lastname: Governatori
authors.forename: Alessio
authors.initials: A
authors.lastname: Bottrighi
authors.forename: Abdul
authors.initials: A
authors.lastname: Sattar
mesh_terms: D000465:Algorithms; D003628:Database Management Systems; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D016347:Medical Records Systems, Computerized