Name
Title | ||
|---|---|---|
Supporting Flexible, Efficient, and User-Interpretable Retrieval of Similar Time Series |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TKDE.2011.264 | IEEE Trans. Knowl. Data Eng. |
Keywords | Field | DocType |
abstracting,indexing,knowledge representation,query processing,time series,transforms,user interfaces,abstraction similarity,decision making,dimensionality reduction,flexible retrieval,indexing techniques,information search,interactive query answering,knowledge representation formalisms,knowledge representation methods,knowledge retrieval,mathematical technique,mathematical transforms,multilevel abstraction mechanisms,observed phenomenon dynamics,representation techniques,temporal abstractions,time-series features,user-guided retrieval process,user-interpretable retrieval,Decision support,information search and retrieval,knowledge representation formalisms and methods,knowledge retrieval | Data mining,Interactivity,End user,Computer science,Search engine indexing,Artificial intelligence,Interpretability,Knowledge representation and reasoning,Information retrieval,Knowledge retrieval,User interface,Machine learning,Scalability | Journal |
Volume | Issue | ISSN |
25 | 3 | 1041-4347 |
Citations | PageRank | References |
7 | 0.46 | 18 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefania Montani | 1 | 901 | 81.42 |
| Giorgio Leonardi | 2 | 179 | 20.36 |
| A. Bottrighi | 3 | 247 | 24.69 |
| Luigi Portinale | 4 | 806 | 155.06 |
| Paolo Terenziani | 5 | 924 | 112.83 |