title: Coping efficiently with now-relative medical data.
abstract: In Medical Informatics, there is an increasing awareness that temporal information plays a crucial role, so that suitable database approaches are needed to store and support it. Specifically, most clinical data are intrinsically temporal, and a relevant part of them are now-relative (i.e., they are valid at the current time). Even if previous studies indicate that the treatment of now-relative data has a crucial impact on efficiency, current approaches have several limitations. In this paper we propose a novel approach, which is based on a new representation of now, and on query transformations. We also experimentally demonstrate that our approach outperforms its best competitors in the literature to the extent of a factor of more than ten, both in number of disk accesses and of CPU usage.
authors.affiliation: Institute for Integrated and Intelligent Systems, Griffith University, Queensland, Australia.
authors.forename: Bela
authors.initials: B
authors.lastname: Stantic
authors.forename: Paolo
authors.initials: P
authors.lastname: Terenziani
authors.forename: Abdul
authors.initials: A
authors.lastname: Sattar
mesh_terms: D000223:Adaptation, Psychological; D000465:Algorithms; D001185:Artificial Intelligence; D016247:Information Storage and Retrieval / Q000379:methods*; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D013358:Subject Headings; D014481:United States; D014584:User-Computer Interface