Abstract
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.
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Ngo, TN., Jeung, H., Aberer, K. (2012). Model-Based Similarity Measure in TimeCloud. In: Sheng, Q.Z., Wang, G., Jensen, C.S., Xu, G. (eds) Web Technologies and Applications. APWeb 2012. Lecture Notes in Computer Science, vol 7235. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29253-8_32
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DOI: https://doi.org/10.1007/978-3-642-29253-8_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29252-1
Online ISBN: 978-3-642-29253-8
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