Case Based Representation and Retrieval with Time Dependent Features

Abstract. The temporal dimension of the knowledge embedded in cases has often been neglected or oversimplified in Case Based Reasoning sys-tems. However, in several real world problems a case should capture the evolution of the observed phenomenon over time. To this end, we propose to represent temporal information at two levels: (1) at the case level, if some features describe parameters varying within a period of time (which corresponds to the case duration), and are therefore collected in the form of time series; (2) at the history level, if the evolution of the system can be reconstructed by retrieving temporally related cases. In this paper, we describe a framework for case representation and retrieval able to take into account the temporal dimension, and meant to be used in any time dependent domain. In particular, to support case retrieval, we provide an analysis of similarity-based time series retrieval techniques; to support history retrieval, we introduce possible ways to summarize the case content, together with the corresponding strategies for identifying similar instances in the knowledge base. A concrete ap-plication of our framework is represented by the system RHENE, which is briefly sketched here, and extensively described in [20].

Accounting for the temporal dimension in Case-based retrieval: a framework for medical applications

Time-varying information embedded in cases has often been neglected and its role oversimplified in case-based reasoning systems. In several real-world problems, and in particular in medical applications, a case should capture the evolution of the observed phenomenon over time. To this end, we propose to represent temporal information at two levels: (1) at the case level, when some features are collected in the form of time series, because they describe parameters varying within a period of time (which corresponds to the case duration), and we aim at analyzing the system behavior within the case duration interval itself; (2) at the history level, when we are interested in reconstructing the evolution of the system by retrieving temporally related cases. In this paper, we describe a framework for case representation and retrieval that is able to take into account the temporal dimension, and is meant to be used in any time dependent domain, which is particularly well suited for medical applications. To support case retrieval, we provide an analysis of similarity-based time series retrieval techniques; to support history retrieval, we introduce possible ways to summarize the case content, together with the corresponding strategies for identifying similar instances in the knowledge base. A concrete application of our framework is represented by RHENE, a system for intelligent retrieval in the hemodialysis domain