Spatial Cone Tree: An Auxiliary Search Structure for Correlation-based Similarity Queries on Spatial Time Series Data
نویسندگان
چکیده
A spatial time series dataset [18, 19] is a collection of time series [3], each referencing a location in a common spatial framework [17]. Finding highly correlated time series from spatial time series datasets collected by satellites, sensor nets, retailers, mobile device servers, and medical instruments on a daily basis is important for many application domains such as epidemiology, ecology, climatology, and census statistics. For example, such queries were used to identify the land locations where the climate was often affected by El Nino [16]. However, correlation queries are computationally expensive because large spatio-temporal frameworks contain many locations and time points. The design of efficient access methods to facilitate correlation-based query processing[1, 7] on spatial time series data, the focus of this work, is crucial to organizations which make decisions based on large spatio-temporal datasets. The problem of designing an efficient indexing method for spatial time series data can be defined as follows.
منابع مشابه
Spatial Cone Tree: An Index Structure for Correlation-based Similarity Queries on Spatial Time Series Data
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تاریخ انتشار 2003