title: A knowledge-based time-oriented active database approach for intelligent abstraction, querying and continuous monitoring of clinical data.
abstract: Query and interpretation of time-oriented medical data involves two subtasks: Temporal-reasoning--intelligent analysis of time-oriented data, and temporal-maintenance--effective storage, query, and retrieval of these data. Integration of these tasks into one system, known as temporal-mediator, has been proven to be beneficial to biomedical applications such as monitoring, therapy, quality assessment, visualization and exploration of time-oriented data. One potential problem in existing temporal-mediation approaches is lack of sufficient responsiveness when querying or continuously monitoring the database for complex abstract concepts that are derived from the raw data, especially regarding a large patient group. We propose a new approach: the knowledge-based time-oriented active database, a temporal extension of the active-database concept, and a merger of temporal reasoning and temporal maintenance within a persistent database framework. The approach preserves the efficiency of databases in handling data storage and retrieval, while enabling specification and performance of complex temporal reasoning using an incremental-computation approach. We implemented our approach within the Momentum system. Initial experiments are encouraging; an evaluation is underway
authors.affiliation: Medical Informatics Research Center, Dept. of Information Systems Engineering, Ben Gurion University, Beer Sheva 84105, Israel. spokoiny@bgumail.bgu.ac.il
authors.forename: Alex
authors.initials: A
authors.lastname: Spokoiny
authors.forename: Yuval
authors.initials: Y
authors.lastname: Shahar
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003628:Database Management Systems; D019992:Databases as Topic; D011381:Programming Languages; D013995:Time