{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:52:38Z","timestamp":1750308758883,"version":"3.41.0"},"reference-count":18,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2007,3,1]],"date-time":"2007-03-01T00:00:00Z","timestamp":1172707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGMOD Rec."],"published-print":{"date-parts":[[2007,3]]},"abstract":"<jats:p>\n            Similarity query is a frequent subroutine in time series database to find the similar time series of the given one. In this process, similarity measure plays a very important part. The previous methods do not consider the relation between point correspondences and the importance (role) of the points on the content of time series during measuring similarity, resulting in their low accuracies in many real applications. In the paper, we propose a General Hierarchical Model (GHM), which determines the point correspondences by the hierarchies of points. It partitions the points of time series into different hierarchies, and then the points are restricted to be compared with the ones in the same hierarchy. The practical methods can be implemented based on the model with any real requirements, e.g. FFT Hierarchical Measures (FHM) given in this paper. And the hierarchical filtering methods of GHM are provided for range and\n            <jats:italic>k<\/jats:italic>\n            -NN queries respectively. Finally, two common data sets were used in\n            <jats:italic>k<\/jats:italic>\n            -NN query and clustering experiments to test the effectiveness of our approach and others. The time performance comparisons of all the tested methods were performed using the synthetic data set with various sizes. The experimental results show the superiority of our approach over the competitors. And we also give the experimental powers of the filtering methods proposed in the queries.\n          <\/jats:p>","DOI":"10.1145\/1276301.1276304","type":"journal-article","created":{"date-parts":[[2007,9,14]],"date-time":"2007-09-14T13:44:55Z","timestamp":1189777495000},"page":"13-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["General Hierarchical Model (GHM) to measure similarity of time series"],"prefix":"10.1145","volume":"36","author":[{"given":"Xinqiang","family":"Zuo","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Xiaoming","family":"Jin","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2007,3]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"359","volume-title":"AAAI Workshop on Knowledge Discovery in Database","author":"Berndt D. J.","year":"1994","unstructured":"D. J. Berndt and J. Clifford . Using dynamic time warping to find patterns in time series . In AAAI Workshop on Knowledge Discovery in Database , pages 359 -- 370 , 1994 . D. J. Berndt and J. Clifford. Using dynamic time warping to find patterns in time series. In AAAI Workshop on Knowledge Discovery in Database, pages 359--370, 1994."},{"key":"e_1_2_1_2_1","first-page":"88","volume-title":"PKDD '97","author":"Das Gautam","unstructured":"Gautam Das , Dimitrios Gunopulos , and Heikki Mannila . Finding similar time series . In PKDD '97 , pages 88 -- 100 . Gautam Das, Dimitrios Gunopulos, and Heikki Mannila. Finding similar time series. In PKDD '97, pages 88--100."},{"key":"e_1_2_1_3_1","first-page":"239","volume-title":"KDD '98","author":"Keogh Eamonn","unstructured":"Eamonn Keogh and M. Pazzani . An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback . In KDD '98 , pages 239 -- 241 . Eamonn Keogh and M. Pazzani. An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. In KDD '98, pages 239--241."},{"key":"e_1_2_1_4_1","first-page":"792","volume-title":"VLDB '04","author":"Chen Lei","unstructured":"Lei Chen and Raymond Ng . On the marriage of lp-norms and edit distance . In VLDB '04 , pages 792 -- 803 . Lei Chen and Raymond Ng. On the marriage of lp-norms and edit distance. In VLDB '04, pages 792--803."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1066157.1066213"},{"key":"e_1_2_1_6_1","first-page":"69","volume-title":"FODO '93","author":"Agrawal Rakesh","unstructured":"Rakesh Agrawal , Christos Faloutsos , and Arun N. Swami . Efficient similarity search in sequence databases . In FODO '93 , pages 69 -- 84 . Rakesh Agrawal, Christos Faloutsos, and Arun N. Swami. Efficient similarity search in sequence databases. In FODO '93, pages 69--84."},{"key":"e_1_2_1_7_1","first-page":"137","volume-title":"CP '95","author":"Dina","unstructured":"Dina Q. Goldin and Paris C. Kanellakis. On similarity queries for time-series data: Constraint specification and implementation . In CP '95 , pages 137 -- 153 . Dina Q. Goldin and Paris C. Kanellakis. On similarity queries for time-series data: Constraint specification and implementation. In CP '95, pages 137--153."},{"key":"e_1_2_1_8_1","first-page":"385","volume-title":"VLDB '00","author":"Yi Byoung-Kee","unstructured":"Byoung-Kee Yi and Christos Faloutsos . Fast time sequence indexing for arbitrary lp norms . In VLDB '00 , pages 385 -- 394 . Byoung-Kee Yi and Christos Faloutsos. Fast time sequence indexing for arbitrary lp norms. In VLDB '00, pages 385--394."},{"key":"e_1_2_1_9_1","first-page":"16","volume-title":"KDD '98","author":"Das Gautam","unstructured":"Gautam Das , King-Ip Lin , Heikki Mannila , Gopal Renganathan , and Padhraic Smyth . Rule discovery from time series . In KDD '98 , pages 16 -- 22 . Gautam Das, King-Ip Lin, Heikki Mannila, Gopal Renganathan, and Padhraic Smyth. Rule discovery from time series. In KDD '98, pages 16--22."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/882082.882086"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014061"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2005.10"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/253260.253332"},{"key":"e_1_2_1_14_1","first-page":"126","volume-title":"ICDE '99","author":"Chan Kin","unstructured":"Kin pong Chan and Ada Wai-Chee Fu . Efficient time series matching by wavelets . In ICDE '99 , pages 126 -- 133 . Kin pong Chan and Ada Wai-Chee Fu. Efficient time series matching by wavelets. In ICDE '99, pages 126--133."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/347090.347153"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/375663.375680"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/347090.347189"},{"key":"e_1_2_1_18_1","volume-title":"In Third Workshop on Mining Temporal and Sequential Data","author":"Ratanamahatana Chotirat Ann","year":"2004","unstructured":"Chotirat Ann Ratanamahatana and Eamonn Keogh . Everything you know about dynamic time warping is wrong . In In Third Workshop on Mining Temporal and Sequential Data , 2004 . Chotirat Ann Ratanamahatana and Eamonn Keogh. Everything you know about dynamic time warping is wrong. In In Third Workshop on Mining Temporal and Sequential Data, 2004."}],"container-title":["ACM SIGMOD Record"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1276301.1276304","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/1276301.1276304","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T20:22:26Z","timestamp":1750278146000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1276301.1276304"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2007,3]]},"references-count":18,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2007,3]]}},"alternative-id":["10.1145\/1276301.1276304"],"URL":"https:\/\/doi.org\/10.1145\/1276301.1276304","relation":{},"ISSN":["0163-5808"],"issn-type":[{"type":"print","value":"0163-5808"}],"subject":[],"published":{"date-parts":[[2007,3]]},"assertion":[{"value":"2007-03-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}