Model-Based Similarity Measure in TimeCloud

Autor:	Thanh-Nguyen Ngo, Karl Aberer, Hoyoung Jeung
Rok vydání:	2012
Předmět:	NCCR-MICS/ESDM Computer science business.industry NCCR-MICS Small number Computation Cloud computing Filter (signal processing) Similarity measure computer.software_genre Set (abstract data type) Similarity (network science) Scalability Data mining business computer
Zdroj:	Web Technologies and Applications ISBN: 9783642292521 APWeb
DOI:	10.1007/978-3-642-29253-8_32
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ed4f5ac8c59faf33c3a7873cb926075b https://doi.org/10.1007/978-3-642-29253-8_32 Zobrazit plný text záznamu