This paper presents a new approach to measuring similarity over massive time-series data. Our approach is built on two principles: one is to parallelize the large amount computation using a scalable cloud serving system, called TimeCloud. The another is to benefit from the filter-and-refinement approach for query processing, such that similarity computation is efficiently performed over approximated data at the filter step, and then the following refinement step measures precise similarities for only a small number of candidates resulted from the filtering. To this end, we establish a set of firm theoretical backgrounds, as well as techniques for processing kNN queries. Our experimental results suggest that the approach proposed is efficient and scalable. © 2012 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Ngo, T. N., Jeung, H., & Aberer, K. (2012). Model-based similarity measure in TimeCloud. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7235 LNCS, pp. 376–387). https://doi.org/10.1007/978-3-642-29253-8_32
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