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Querying and Mining of Time Series Data: Experimental Comparison of Representations and Distance Measures
"... The last decade has witnessed a tremendous growths of interests in applications that deal with querying and mining of time series data. Numerous representation methods for dimensionality reduction and similarity measures geared towards time series have been introduced. Each individual work introduci ..."
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Cited by 141 (24 self)
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The last decade has witnessed a tremendous growths of interests in applications that deal with querying and mining of time series data. Numerous representation methods for dimensionality reduction and similarity measures geared towards time series have been introduced. Each individual work introducing a particular method has made specific claims and, aside from the occasional theoretical justifications, provided quantitative experimental observations. However, for the most part, the comparative aspects of these experiments were too narrowly focused on demonstrating the benefits of the proposed methods over some of the previously introduced ones. In order to provide a comprehensive validation, we conducted an extensive set of time series experiments re-implementing 8 different representation methods and 9 similarity measures and their variants, and testing their effectiveness on 38 time series data sets from a wide variety of application domains. In this paper, we give an overview of these different techniques and present our comparative experimental findings regarding their effectiveness. Our experiments have provided both a unified validation of some of the existing achievements, and in some cases, suggested that certain claims in the literature may be unduly optimistic. 1.
Experimental comparison of representation methods and distance measures for time series data
- Data Mining and Knowledge Discovery
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Efficient Similarity Join of Large Sets of Moving Object Trajectories
"... We address the problem of performing efficient similarity join for large sets of moving objects trajectories. Unlike previous approaches which use a dedicated index in a transformed space, our premise is that in many applications of location-based services, the trajectories are already indexed in th ..."
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Cited by 5 (1 self)
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We address the problem of performing efficient similarity join for large sets of moving objects trajectories. Unlike previous approaches which use a dedicated index in a transformed space, our premise is that in many applications of location-based services, the trajectories are already indexed in their native space, in order to facilitate the processing of common spatio-temporal queries, e.g., range, nearest neighbor etc. We introduce a novel distance measure adapted from the classic Fréchet distance, which can be naturally extended to support lower/upper bounding using the underlying indices of moving object databases in the native space. This, in turn, enables efficient implementation of various trajectory similarity joins. We report on extensive experiments demonstrating that our methodology provides performance speed-up of trajectory similarity join by more than 50 % on average, while maintaining effectiveness comparable to the well-known approaches for identifying trajectory similarity based on time-series analysis. 1
Keywords: Spatio-temporal trajectory, similarity joinRobust and Fast Similarity Join of Large Sets of Moving Object Trajectories
, 2006
"... We address the problem of performing efficient similarity join for large sets of moving objects trajectories. Unlike previous approaches which use a dedicated index in a transformed space, our premise is that in many applications of location-based services, the trajectories are already indexed in th ..."
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We address the problem of performing efficient similarity join for large sets of moving objects trajectories. Unlike previous approaches which use a dedicated index in a transformed space, our premise is that in many applications of location-based services, the trajectories are already indexed in their native space, in order to facilitate the processing of common spatio-temporal queries, e.g., range, nearest neighbor etc. We introduce a novel distance measure adapted from the classic Frechet distance, which can be naturally extended to support lower/upper bounding using the underlying indices of moving object databases in the native space. This, in turn, enables efficient implementation of various trajectory similarity joins. We report on extensive experiments demonstrating that our methodology provides performance speed-up of trajectory similarity join by more than 50 % on average, while maintaining effectiveness comparable to the well-known approaches for identifying trajectory similarity based on time-series analysis.
© Springer-Verlag Berlin Heidelberg 2010 A Framework for Time-Series Analysis*
"... Abstract. The popularity of time-series databases in many applications has cre-ated an increasing demand for performing data-mining tasks (classification, clustering, outlier detection, etc.) on time-series data. Currently, however, no single system or library exists that specializes on providing ef ..."
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Abstract. The popularity of time-series databases in many applications has cre-ated an increasing demand for performing data-mining tasks (classification, clustering, outlier detection, etc.) on time-series data. Currently, however, no single system or library exists that specializes on providing efficient implemen-tations of data-mining techniques for time-series data, supports the necessary concepts of representations, similarity measures and preprocessing tasks, and is at the same time freely available. For these reasons we have designed a multi-purpose, multifunctional, extendable system FAP – Framework for Analysis and Prediction, which supports the aforementioned concepts and techniques for mining time-series data. This paper describes the architecture of FAP and the current version of its Java implementation which focuses on time-series similar-ity measures and nearest-neighbor classification. The correctness of the imple-mentation is verified through a battery of experiments which involve diverse time-series data sets from the UCR repository.
