Intelligent selection and retrieval of multiple time-oriented records Export

@article{citeulike:5817831, abstract = {Abstract\ \ Time-oriented domains with large volumes of time-stamped information, such as medicine, security information and finance, require useful, intuitive intelligent tools to process large amounts of time-oriented multiple-subject data from multiple sources. We designed and developed a new architecture, the VISualizatIon of Time-Oriented RecordS (VISITORS) system, which combines intelligent temporal analysis and information visualization techniques. The VISITORS system includes tools for intelligent selection, visualization, exploration, and analysis of raw time-oriented data and of derived (abstracted) concepts for multiple subject records. To derive meaningful interpretations from raw time-oriented data (known as temporal abstractions), we use the knowledge-based temporal-abstraction method. A major task in the VISITORS system is the selection of the appropriate subset of the subject population on which to focus during the analysis. Underlying the VISITORS population-selection module is our ontology-based temporal-aggregation (OBTAIN) expression-specification language which we introduce in this study. The OBTAIN language was implemented by a graphical expression-specification module integrated within the VISITORS system. The module enables construction of three types of expressions supported by the language: Select Subjects, Select Time Intervals, and Get Subjects Data. These expressions retrieve a list of subjects, a list of relevant time intervals, and a list of time-oriented subjects' data sets, respectively. In particular, the OBTAIN language enables population-specification, through the Select Subjects expression, by using an expressive set of time and value constraints. We describe the syntax and semantics of the OBTAIN language and of the expression-specification module. The OBTAIN expressions constructed by the expression-specification module, are computed by a temporal abstraction mediation framework that we have previously developed. To evaluate the expression-specification module, five clinicians and five medical informaticians defined ten expressions, using the expression-specification module, on a database of more than 1,000 oncology patients. After a brief training session, both user groups were able in a short time (mean = 3.3 ± 0.53\ min) to construct ten complex expressions using the expression-specification module, with high accuracy (mean = 95.3 ± 4.5 on a predefined scale of 0 to 100). When grouped by time and value constraint subtypes, five groups of expressions emerged. Only one of the five groups (expressions using time-range constraints), led to a significantly lower accuracy of constructed expressions. The five groups of expressions could be clustered into four homogenous groups, ordered by increasing construction time of the expressions. A system usability scale questionnaire filled by the users demonstrated the expression-specification module to be usable (mean score for the overall group = 68), but the clinicians' usability assessment (60.0) was significantly lower than that of the medical informaticians (76.1).}, author = {Klimov, Denis and Shahar, Yuval and Taieb-Maimon, Meirav}, citeulike-article-id = {5817831}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/s10844-009-0100-0}, citeulike-linkout-1 = {http://www.springerlink.com/content/u5568658x7h15034}, doi = {10.1007/s10844-009-0100-0}, journal = {Journal of Intelligent Information Systems}, keywords = {stream\_based\_systems, temporal\_databases}, posted-at = {2009-09-22 21:16:36}, priority = {2}, title = {Intelligent selection and retrieval of multiple time-oriented records}, url = {http://dx.doi.org/10.1007/s10844-009-0100-0} }

TY - JOUR ID - citeulike:5817831 L3 - citeulike-article-id:5817831 N2 - Abstract  Time-oriented domains with large volumes of time-stamped information, such as medicine, security information and finance, require useful, intuitive intelligent tools to process large amounts of time-oriented multiple-subject data from multiple sources. We designed and developed a new architecture, the VISualizatIon of Time-Oriented RecordS (VISITORS) system, which combines intelligent temporal analysis and information visualization techniques. The VISITORS system includes tools for intelligent selection, visualization, exploration, and analysis of raw time-oriented data and of derived (abstracted) concepts for multiple subject records. To derive meaningful interpretations from raw time-oriented data (known as temporal abstractions), we use the knowledge-based temporal-abstraction method. A major task in the VISITORS system is the selection of the appropriate subset of the subject population on which to focus during the analysis. Underlying the VISITORS population-selection module is our ontology-based temporal-aggregation (OBTAIN) expression-specification language which we introduce in this study. The OBTAIN language was implemented by a graphical expression-specification module integrated within the VISITORS system. The module enables construction of three types of expressions supported by the language: Select Subjects, Select Time Intervals, and Get Subjects Data. These expressions retrieve a list of subjects, a list of relevant time intervals, and a list of time-oriented subjects’ data sets, respectively. In particular, the OBTAIN language enables population-specification, through the Select Subjects expression, by using an expressive set of time and value constraints. We describe the syntax and semantics of the OBTAIN language and of the expression-specification module. The OBTAIN expressions constructed by the expression-specification module, are computed by a temporal abstraction mediation framework that we have previously developed. To evaluate the expression-specification module, five clinicians and five medical informaticians defined ten expressions, using the expression-specification module, on a database of more than 1,000 oncology patients. After a brief training session, both user groups were able in a short time (mean = 3.3 ± 0.53 min) to construct ten complex expressions using the expression-specification module, with high accuracy (mean = 95.3 ± 4.5 on a predefined scale of 0 to 100). When grouped by time and value constraint subtypes, five groups of expressions emerged. Only one of the five groups (expressions using time-range constraints), led to a significantly lower accuracy of constructed expressions. The five groups of expressions could be clustered into four homogenous groups, ordered by increasing construction time of the expressions. A system usability scale questionnaire filled by the users demonstrated the expression-specification module to be usable (mean score for the overall group = 68), but the clinicians’ usability assessment (60.0) was significantly lower than that of the medical informaticians (76.1). JF - Journal of Intelligent Information Systems TI - Intelligent selection and retrieval of multiple time-oriented records KW - stream_based_systems KW - temporal_databases AU - Klimov, Denis AU - Shahar, Yuval AU - Taieb-Maimon, Meirav UR - http://dx.doi.org/10.1007/s10844-009-0100-0 ER -