{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T22:26:47Z","timestamp":1781216807563,"version":"3.54.1"},"reference-count":40,"publisher":"Association for Computing Machinery (ACM)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2021,11]]},"abstract":"<jats:p>Very large time series are increasingly available from an ever wider range of IoT-enabled sensors deployed in different environments. Significant insights can be gained by mining temporal patterns from these time series. Unlike traditional pattern mining, temporal pattern mining (TPM) adds event time intervals into extracted patterns, making them more expressive at the expense of increased time and space complexities. Existing TPM methods either cannot scale to large datasets, or work only on pre-processed temporal events rather than on time series. This paper presents our Frequent Temporal Pattern Mining from Time Series (FTPMfTS) approach providing: (1) The end-to-end FTPMfTS process taking time series as input and producing frequent temporal patterns as output. (2) The efficient Hierarchical Temporal Pattern Graph Mining (HTPGM) algorithm that uses efficient data structures for fast support and confidence computation, and employs effective pruning techniques for significantly faster mining. (3) An approximate version of HTPGM that uses mutual information, a measure of data correlation, to prune unpromising time series from the search space. (4) An extensive experimental evaluation showing that HTPGM outperforms the baselines in runtime and memory consumption, and can scale to big datasets. The approximate HTPGM is up to two orders of magnitude faster and less memory consuming than the baselines, while retaining high accuracy.<\/jats:p>","DOI":"10.14778\/3494124.3494147","type":"journal-article","created":{"date-parts":[[2022,2,5]],"date-time":"2022-02-05T00:31:46Z","timestamp":1644021106000},"page":"673-685","source":"Crossref","is-referenced-by-count":15,"title":["Efficient temporal pattern mining in big time series using mutual information"],"prefix":"10.14778","volume":"15","author":[{"given":"Van Long","family":"Ho","sequence":"first","affiliation":[{"name":"Aalborg University, Aalborg, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nguyen","family":"Ho","sequence":"additional","affiliation":[{"name":"Aalborg University, Aalborg, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Torben Bach","family":"Pedersen","sequence":"additional","affiliation":[{"name":"Aalborg University, Aalborg, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,2,4]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2016.03.007"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/182.358434"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339578"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2508037.2508044"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2005.149"},{"key":"e_1_2_1_7_1","volume-title":"Temporal condition pattern mining in large, sparse electronic health record data: A case study in characterizing pediatric asthma. JAMIA 27","author":"Campbell Elizabeth A","year":"2020","unstructured":"Elizabeth A Campbell , Ellen J Bass , and Aaron J Masino . 2020. Temporal condition pattern mining in large, sparse electronic health record data: A case study in characterizing pediatric asthma. JAMIA 27 ( 2020 ). Elizabeth A Campbell, Ellen J Bass, and Aaron J Masino. 2020. Temporal condition pattern mining in large, sparse electronic health record data: A case study in characterizing pediatric asthma. JAMIA 27 (2020)."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2454515"},{"key":"e_1_2_1_9_1","unstructured":"New York City. 2019. NYC OpenData. https:\/\/opendata.cityofnewyork.us\/  New York City. 2019. NYC OpenData. https:\/\/opendata.cityofnewyork.us\/"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.5555\/129837"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2014.12.001"},{"key":"e_1_2_1_12_1","unstructured":"Energi Data Portal. 2021. https:\/\/www.energidataservice.dk\/tso-electricity\/co2emis\/  Energi Data Portal. 2021. https:\/\/www.energidataservice.dk\/tso-electricity\/co2emis\/"},{"key":"e_1_2_1_13_1","unstructured":"Pecan Street Data. 2016. Pecan Street Dataport. https:\/\/www.pecanstreet.org\/dataport\/  Pecan Street Data. 2016. Pecan Street Dataport. https:\/\/www.pecanstreet.org\/dataport\/"},{"key":"e_1_2_1_14_1","doi-asserted-by":"crossref","unstructured":"William Healy Farhad Omar Lisa Ng Tania Ullah William Payne Brian Dougherty and A Hunter Fanney. 2018. Net zero energy residential test facility instrumented data. https:\/\/pages.nist.gov\/netzero\/index.html\/  William Healy Farhad Omar Lisa Ng Tania Ullah William Payne Brian Dougherty and A Hunter Fanney. 2018. Net zero energy residential test facility instrumented data. https:\/\/pages.nist.gov\/netzero\/index.html\/","DOI":"10.6028\/jres.122.014"},{"key":"e_1_2_1_15_1","first-page":"37","article-title":"Efficient Search for Multi-Scale Time Delay Correlations in Big Time Series Data. In 23rd International Conference on Extending Database Technology","volume":"2020","author":"Ho Nguyen","year":"2020","unstructured":"Nguyen Ho , Torben Bach Pedersen , Van Long Ho , and Mai Vu . 2020 . Efficient Search for Multi-Scale Time Delay Correlations in Big Time Series Data. In 23rd International Conference on Extending Database Technology , EDBT 2020. 37 -- 48 . Nguyen Ho, Torben Bach Pedersen, Van Long Ho, and Mai Vu. 2020. Efficient Search for Multi-Scale Time Delay Correlations in Big Time Series Data. In 23rd International Conference on Extending Database Technology, EDBT 2020. 37--48.","journal-title":"EDBT"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00185"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2019.2907987"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2016.7840659"},{"key":"e_1_2_1_19_1","volume-title":"Efficient Temporal Pattern Mining in Big Time Series Using Mutual Information. arXiv preprint arXiv:2010.03653","author":"Ho Van Long","year":"2020","unstructured":"Van Long Ho , Nguyen Ho , and Torben Bach Pedersen . 2021. Efficient Temporal Pattern Mining in Big Time Series Using Mutual Information. arXiv preprint arXiv:2010.03653 ( 2020 ). https:\/\/arxiv.org\/abs\/2010.03653 Van Long Ho, Nguyen Ho, and Torben Bach Pedersen. 2021. Efficient Temporal Pattern Mining in Big Time Series Using Mutual Information. arXiv preprint arXiv:2010.03653 (2020). https:\/\/arxiv.org\/abs\/2010.03653"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.5555\/646109.679272"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/1386118.1386120"},{"key":"e_1_2_1_22_1","first-page":"47","volume-title":"2015 IEEE conference on technologies for sustainability (SusTech)","author":"Nguyen Ho Thi Thao","year":"2015","unstructured":"Thi Thao Nguyen Ho , and Barbara Pernici . A data-value-driven adaptation framework for energy efficiency for data intensive applications in clouds . In 2015 IEEE conference on technologies for sustainability (SusTech) , pp. 47 -- 52 . IEEE, 2015 . Thi Thao Nguyen Ho, and Barbara Pernici. A data-value-driven adaptation framework for energy efficiency for data intensive applications in clouds. In 2015 IEEE conference on technologies for sustainability (SusTech), pp. 47--52. IEEE, 2015."},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077839.3084026"},{"key":"e_1_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Thi Thao Nguyen Ho Marco Gribaudo and Barbara Pernici. \"Characterizing energy per job in cloud applications.\" Electronics 5 no. 4 (2016): 90.  Thi Thao Nguyen Ho Marco Gribaudo and Barbara Pernici. \"Characterizing energy per job in cloud applications.\" Electronics 5 no. 4 (2016): 90.","DOI":"10.3390\/electronics5040090"},{"key":"e_1_2_1_25_1","first-page":"87","volume-title":"Barbara Pernici, and Giuseppe Serazzi. \"Analysis of the influence of application deployment on energy consumption.\" In International Workshop on Energy Efficient Data Centers","author":"Gribaudo Macro","year":"2014","unstructured":"Macro Gribaudo , Thi Thao Nguyen Ho , Barbara Pernici, and Giuseppe Serazzi. \"Analysis of the influence of application deployment on energy consumption.\" In International Workshop on Energy Efficient Data Centers , pp. 87 -- 101 . Springer , Cham , 2014 . Macro Gribaudo, Thi Thao Nguyen Ho, Barbara Pernici, and Giuseppe Serazzi. \"Analysis of the influence of application deployment on energy consumption.\" In International Workshop on Energy Efficient Data Centers, pp. 87--101. Springer, Cham, 2014."},{"key":"e_1_2_1_26_1","volume-title":"The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Scientific Data","author":"Kelly Jack","year":"2015","unstructured":"Jack Kelly and William Knottenbelt . 2015. The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Scientific Data ( 2015 ). Jack Kelly and William Knottenbelt. 2015. The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Scientific Data (2015)."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.5555\/951949.952125"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403095"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/882082.882086"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2005.10"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/1294301.1294302"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-013-0707-x"},{"key":"e_1_2_1_33_1","volume-title":"8th Workshopon the Representation and Processing of Sign Languages: Involving the Language Community, Miyazaki, Language Resources and Evaluation Conference","author":"Neidle Carol","year":"2018","unstructured":"Carol Neidle , Augustine Opoku , Gregory Dimitriadis , and Dimitris Metaxas . 2018 . NEW Shared & Interconnected ASL Resources: SignStream\u00ae 3 Software; DAI 2 for Web Access to Linguistically Annotated Video Corpora; and a Sign Bank . In 8th Workshopon the Representation and Processing of Sign Languages: Involving the Language Community, Miyazaki, Language Resources and Evaluation Conference 2018. Carol Neidle, Augustine Opoku, Gregory Dimitriadis, and Dimitris Metaxas. 2018. NEW Shared & Interconnected ASL Resources: SignStream\u00ae 3 Software; DAI 2 for Web Access to Linguistically Annotated Video Corpora; and a Sign Bank. In 8th Workshopon the Representation and Processing of Sign Languages: Involving the Language Community, Miyazaki, Language Resources and Evaluation Conference 2018."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2003.1161582"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.5555\/3225629.3225731"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/1376616.1376658"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8622421"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3391230"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2007.190613"},{"key":"e_1_2_1_40_1","doi-asserted-by":"crossref","unstructured":"YY Yao. 2003. Information-theoretic measures for knowledge discovery and data mining. In Entropy measures maximum entropy principle and emerging applications. 115--136.  YY Yao. 2003. Information-theoretic measures for knowledge discovery and data mining. In Entropy measures maximum entropy principle and emerging applications. 115--136.","DOI":"10.1007\/978-3-540-36212-8_6"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-61627-4_4"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3494124.3494147","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T11:30:45Z","timestamp":1672227045000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3494124.3494147"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11]]},"references-count":40,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["10.14778\/3494124.3494147"],"URL":"https:\/\/doi.org\/10.14778\/3494124.3494147","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2021,11]]}}}