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| Content Provider | ACM Digital Library |
|---|---|
| Author | Gao, Hong Li, Jianzhong Peng, Jinglin Wang, Hongzhi |
| Abstract | A fundamental problem of time series is k nearest neighbor (k-NN) query processing. However, existing methods are not fast enough for large dataset. In this paper, we propose a novel approach, STS3, to process k-NN queries by transforming time series to sets and measure the similarity under Jaccard metric. Our approach is more accurate than Dynamic Time Warping(DTW) in our suitable scenarios and it is faster than most of the existing methods, due to the efficient similarity search for sets. Besides, we also developed an index, a pruning and an approximation technique to improve the k-NN query procedure. As shown in the experimental results, all of them could accelerate the query processing effectively. |
| Starting Page | 2039 |
| Ending Page | 2052 |
| Page Count | 14 |
| File Format | |
| ISBN | 9781450335317 |
| DOI | 10.1145/2882903.2882963 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-06-26 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | K-nn Time series Representation Similarity search |
| Content Type | Text |
| Resource Type | Article |
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