Italiano (Italian) English (Inglese)
martedì, 12 dicembre 2017


Dettagli Pubblicazione
Autori:Alessio Bottrighi
Giorgio Leonardi
Stefania Montani
Luigi Portinale
Paolo Terenziani
Area Scientifica:Artificial Intelligence
Intelligent Data Analysis
Knowledge Representation
Temporal Reasoning
Titolo:Supporting Flexible, Efficient, and User-Interpretable Retrieval of Similar Time Series
Apparso su: IEEE Transactions on Knowledge and Data Engineering
Editore:IEEE Computer Society
Tipo Pubblicazione:Paper on International Journal
Sommario:Supporting decision making in domains in which the observed phenomenon dynamics have to be dealt with, can greatly benefit of retrieval of past cases, provided that proper representation and retrieval techniques are implemented. In particular, when the parameters of interest take the form of time series, dimensionality reduction and flexible retrieval have to be addresses to this end. Classical methodological solutions proposed to cope with these issues, typically based on mathematical transforms, are characterized by strong limitations, such as a difficult interpretation of retrieval results for end users, reduced flexibility and interactivity, or inefficiency. In this paper, we describe a novel framework, in which time-series features are summarized by means of Temporal Abstractions, and then retrieved resorting to abstraction similarity. Our approach grants for interpretability of the output results, and understandability of the (user-guided) retrieval process. In particular, multilevel abstraction mechanisms and proper indexing techniques are provided, for flexible query issuing, and efficient and interactive query answering. Experimental results have shown the efficiency of our approach in a scalability test, and its superiority with respect to the use of a classical mathematical technique in flexibility, user friendliness, and also quality of results.