Intelligent data interpretation and case base exploration through temporal abstractions

Interpreting time series of measurements and exploring a repository of cases with time series data looking for similarities, are non-trivial, but very important tasks. Classical methodological solutions proposed to deal with (some of) these goals, typically based on mathematical techniques, are characterized by strong limitations, such as unclear or incorrect retrieval results and reduced interactivity and flexibility. In this paper, we describe a novel case base exploration and retrieval architecture, which supports time series summarization and interpretation by means of Temporal Abstractions, and in which multi-level abstraction mechanisms and proper indexing techniques are provided, in order to grant expressiveness in issuing queries, as well as efficiency and flexibility in answering queries themselves. Relying on a set of concrete examples, taken from the haemodialysis domain, we illustrate the system facilities, and we demonstrate the advantages of relying on this methodology, with respect to more classical mathematical ones.