{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T09:52:25Z","timestamp":1773481945991,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":81,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,6,30]],"date-time":"2020-06-30T00:00:00Z","timestamp":1593475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,6,30]]},"DOI":"10.1145\/3405962.3405989","type":"proceedings-article","created":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T04:24:02Z","timestamp":1598329442000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Scalable Machine Learning on High-Dimensional Vectors"],"prefix":"10.1145","author":[{"given":"Karima","family":"Echihabi","sequence":"first","affiliation":[{"name":"IRDA, Rabat IT Center, ENSIAS, Mohammed V University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kostas","family":"Zoumpatianos","sequence":"additional","affiliation":[{"name":"Harvard University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Themis","family":"Palpanas","sequence":"additional","affiliation":[{"name":"LIPADE, Universit\u00e9 de Paris & French University Institute (IUF)"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,8,24]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2018. Lernaean Hydra Archive. http:\/\/www.mi.parisdescartes.fr\/~themisp\/dsseval\/.  2018. Lernaean Hydra Archive. http:\/\/www.mi.parisdescartes.fr\/~themisp\/dsseval\/."},{"key":"e_1_3_2_1_2_1","unstructured":"2019. Lernaean Hydra Archive II. http:\/\/www.mi.parisdescartes.fr\/~themisp\/dsseval2\/.  2019. Lernaean Hydra Archive II. http:\/\/www.mi.parisdescartes.fr\/~themisp\/dsseval2\/."},{"key":"e_1_3_2_1_3_1","volume-title":"Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms.","author":"Agrawal Rakesh","unstructured":"Rakesh Agrawal , Christos Faloutsos , and Arun N. Swami . 1993. Efficient Similarity Search In Sequence Databases . In Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms. Rakesh Agrawal, Christos Faloutsos, and Arun N. Swami. 1993. Efficient Similarity Search In Sequence Databases. In Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms."},{"key":"e_1_3_2_1_4_1","volume-title":"POPL","author":"Alon Uri","year":"2019","unstructured":"Uri Alon , Meital Zilberstein , Omer Levy , and Eran Yahav . 2019. Code2vec: Learning Distributed Representations of Code. 3 , POPL ( 2019 ). Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav. 2019. Code2vec: Learning Distributed Representations of Code. 3, POPL (2019)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.14778\/3204028.3204034"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2361319"},{"key":"e_1_3_2_1_7_1","volume-title":"Data Series Management. Dagstuhl Reports (9(7)","author":"Bagnall Anthony J.","year":"2019","unstructured":"Anthony J. Bagnall , Richard L. Cole , Themis Palpanas , and Konstantinos Zoumpatianos . 9(7), 2019. Data Series Management. Dagstuhl Reports (9(7) , 2019 ). Anthony J. Bagnall, Richard L. Cole, Themis Palpanas, and Konstantinos Zoumpatianos. 9(7), 2019. Data Series Management. Dagstuhl Reports (9(7), 2019)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/93597.98741"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/361002.361007"},{"key":"e_1_3_2_1_10_1","volume-title":"The X-tree: An Index Structure for High-Dimensional Data. In VLDB.","author":"Berchtold Stefan","year":"1996","unstructured":"Stefan Berchtold , Daniel A. Keim , and Hans-Peter Kriegel . 1996 . The X-tree: An Index Structure for High-Dimensional Data. In VLDB. Stefan Berchtold, Daniel A. Keim, and Hans-Peter Kriegel. 1996. The X-tree: An Index Structure for High-Dimensional Data. In VLDB."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Paul Boniol Michele Linardi Federico Roncallo and Themis Palpanas. 2020. Automated Anomaly Detection in Large Sequences. In ICDE.  Paul Boniol Michele Linardi Federico Roncallo and Themis Palpanas. 2020. Automated Anomaly Detection in Large Sequences. In ICDE.","DOI":"10.1109\/ICDE48307.2020.00182"},{"key":"e_1_3_2_1_12_1","volume-title":"Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series. PVLDB","author":"Boniol Paul","year":"2020","unstructured":"Paul Boniol and Themis Palpanas . 2020. Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series. PVLDB ( 2020 ). Paul Boniol and Themis Palpanas. 2020. Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series. PVLDB (2020)."},{"key":"e_1_3_2_1_13_1","volume-title":"Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions. JMLR 10","author":"Bubeck S\u00e9bastien","year":"2009","unstructured":"S\u00e9bastien Bubeck and Ulrike von Luxburg . 2009. Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions. JMLR 10 ( 2009 ). S\u00e9bastien Bubeck and Ulrike von Luxburg. 2009. Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions. JMLR 10 (2009)."},{"key":"e_1_3_2_1_14_1","volume-title":"Raftery","author":"Byers Simon","year":"1998","unstructured":"Simon Byers and Adrian E . Raftery . 1998 . Nearest-Neighbor Clutter Removal for Estimating Features in Spatial Point Processes. JASA 93, 442 (1998). Simon Byers and Adrian E. Raftery. 1998. Nearest-Neighbor Clutter Removal for Estimating Features in Spatial Point Processes. JASA 93, 442 (1998)."},{"key":"e_1_3_2_1_15_1","volume-title":"Keogh","author":"Camerra Alessandro","year":"2010","unstructured":"Alessandro Camerra , Themis Palpanas , Jin Shieh , and Eamonn J . Keogh . 2010 . iSAX 2.0: Indexing and Mining One Billion Time Series.. In ICDM. IEEE Computer Society , 58--67. Alessandro Camerra, Themis Palpanas, Jin Shieh, and Eamonn J. Keogh. 2010. iSAX 2.0: Indexing and Mining One Billion Time Series.. In ICDM. IEEE Computer Society, 58--67."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-012-0606-6"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Georgios Chatzigeorgakidis Dimitrios Skoutas Kostas Patroumpas Themis Palpanas Spiros Athanasiou and Spiros Skiadopoulos. 2019. Local Pair and Bundle Discovery over Co-Evolving Time Series. In SSTD.  Georgios Chatzigeorgakidis Dimitrios Skoutas Kostas Patroumpas Themis Palpanas Spiros Athanasiou and Spiros Skiadopoulos. 2019. Local Pair and Bundle Discovery over Co-Evolving Time Series. In SSTD.","DOI":"10.1145\/3340964.3340982"},{"key":"e_1_3_2_1_18_1","unstructured":"Paolo Ciaccia and Marco Patella. 2000. PAC Nearest Neighbor Queries: Approximate and Controlled Search in High-Dimensional and Metric Spaces. In ICDE. 244--255.  Paolo Ciaccia and Marco Patella. 2000. PAC Nearest Neighbor Queries: Approximate and Controlled Search in High-Dimensional and Metric Spaces. In ICDE. 244--255."},{"key":"e_1_3_2_1_19_1","volume-title":"Proceedings of the 23rd International Conference on Very Large Data Bases (VLDB'97)","author":"Ciaccia Paolo","year":"1997","unstructured":"Paolo Ciaccia , Marco Patella , and Pavel Zezula . 1997 . M-tree: An Efficient Access Method for Similarity Search in Metric Spaces . In Proceedings of the 23rd International Conference on Very Large Data Bases (VLDB'97) . 426--435. Paolo Ciaccia, Marco Patella, and Pavel Zezula. 1997. M-tree: An Efficient Access Method for Similarity Search in Metric Spaces. In Proceedings of the 23rd International Conference on Very Large Data Bases (VLDB'97). 426--435."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1081870.1081966"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.14778\/2735461.2735463"},{"key":"e_1_3_2_1_22_1","volume-title":"Data Mining and Machine Learning in Cybersecurity","author":"Dua Sumeet","unstructured":"Sumeet Dua and Xian Du. 2011. Data Mining and Machine Learning in Cybersecurity ( 1 st ed.). Auerbach Publications , USA. Sumeet Dua and Xian Du. 2011. Data Mining and Machine Learning in Cybersecurity (1st ed.). Auerbach Publications, USA.","edition":"1"},{"key":"e_1_3_2_1_23_1","volume-title":"Distributed Representations of Tuples for Entity Resolution. VLDBJ 11, 11","author":"Ebraheem Muhammad","year":"2018","unstructured":"Muhammad Ebraheem , Saravanan Thirumuruganathan , Shafiq Joty , Mourad Ouzzani , and Nan Tang . 2018. Distributed Representations of Tuples for Entity Resolution. VLDBJ 11, 11 ( 2018 ). Muhammad Ebraheem, Saravanan Thirumuruganathan, Shafiq Joty, Mourad Ouzzani, and Nan Tang. 2018. Distributed Representations of Tuples for Entity Resolution. VLDBJ 11, 11 (2018)."},{"key":"e_1_3_2_1_24_1","volume-title":"Proceedings of the VLDB 2019 PhD Workshop, co-located with the 45th International Conference on Very Large Databases (VLDB","author":"Echihabi Karima","year":"2019","unstructured":"Karima Echihabi . 2019 . Truly Scalable Data Series Similarity Search . In Proceedings of the VLDB 2019 PhD Workshop, co-located with the 45th International Conference on Very Large Databases (VLDB 2019). Karima Echihabi. 2019. Truly Scalable Data Series Similarity Search. In Proceedings of the VLDB 2019 PhD Workshop, co-located with the 45th International Conference on Very Large Databases (VLDB 2019)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Karima Echihabi. 2020. High-Dimensional Vector Similarity Search: From Time Series to Deep Network Embeddings. In SIGMOD.  Karima Echihabi. 2020. High-Dimensional Vector Similarity Search: From Time Series to Deep Network Embeddings. In SIGMOD.","DOI":"10.1145\/3318464.3384402"},{"key":"e_1_3_2_1_26_1","volume-title":"The Lernaean Hydra of Data Series Similarity Search: An Experimental Evaluation of the State of the Art. PVLDB","author":"Echihabi Karima","year":"2018","unstructured":"Karima Echihabi , Kostas Zoumpatianos , Themis Palpanas , and Houda Benbrahim . 2018. The Lernaean Hydra of Data Series Similarity Search: An Experimental Evaluation of the State of the Art. PVLDB ( 2018 ). Karima Echihabi, Kostas Zoumpatianos, Themis Palpanas, and Houda Benbrahim. 2018. The Lernaean Hydra of Data Series Similarity Search: An Experimental Evaluation of the State of the Art. PVLDB (2018)."},{"key":"e_1_3_2_1_27_1","volume-title":"Return of the Lernaean Hydra: Experimental Evaluation of Data Series Approximate Similarity Search. PVLDB","author":"Echihabi Karima","year":"2019","unstructured":"Karima Echihabi , Kostas Zoumpatianos , Themis Palpanas , and Houda Benbrahim . 2019. Return of the Lernaean Hydra: Experimental Evaluation of Data Series Approximate Similarity Search. PVLDB ( 2019 ). Karima Echihabi, Kostas Zoumpatianos, Themis Palpanas, and Houda Benbrahim. 2019. Return of the Lernaean Hydra: Experimental Evaluation of Data Series Approximate Similarity Search. PVLDB (2019)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Hakan Ferhatosmanoglu Ertem Tuncel Divyakant Agrawal and Amr El Abbadi. 2000. Vector Approximation Based Indexing for Nonuniform High Dimensional Data Sets. In CIKM.  Hakan Ferhatosmanoglu Ertem Tuncel Divyakant Agrawal and Amr El Abbadi. 2000. Vector Approximation Based Indexing for Nonuniform High Dimensional Data Sets. In CIKM.","DOI":"10.1145\/354756.354820"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.14778\/3303753.3303754"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.240"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Anna Gogolou Theophanis Tsandilas Karima Echihabi Themis Palpanas and Anastasia Bezerianos. 2020. Data Series Progressive Similarity Search with Probabilistic Quality Guarantees. In SIGMOD.  Anna Gogolou Theophanis Tsandilas Karima Echihabi Themis Palpanas and Anastasia Bezerianos. 2020. Data Series Progressive Similarity Search with Probabilistic Quality Guarantees. In SIGMOD.","DOI":"10.1145\/3318464.3389751"},{"key":"e_1_3_2_1_32_1","volume-title":"Proceedings of the Workshops of the EDBT\/ICDT 2019 Joint Conference.","author":"Gogolou Anna","year":"2019","unstructured":"Anna Gogolou , Theophanis Tsandilas , Themis Palpanas , and Anastasia Bezerianos . 2019 . Progressive Similarity Search on Time Series Data . In Proceedings of the Workshops of the EDBT\/ICDT 2019 Joint Conference. Anna Gogolou, Theophanis Tsandilas, Themis Palpanas, and Anastasia Bezerianos. 2019. Progressive Similarity Search on Time Series Data. In Proceedings of the Workshops of the EDBT\/ICDT 2019 Joint Conference."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Antonin Guttman. 1984. R-Trees: A Dynamic Index Structure for Spatial Searching. In SIGMOD.  Antonin Guttman. 1984. R-Trees: A Dynamic Index Structure for Spatial Searching. In SIGMOD.","DOI":"10.1145\/602259.602266"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.14778\/2850469.2850470"},{"key":"e_1_3_2_1_35_1","volume-title":"Computational intelligence challenges and applications on large-scale astronomical time series databases. CIM","author":"Huijse Pablo","year":"2014","unstructured":"Pablo Huijse , Pablo A Estevez , Pavlos Protopapas , Jose C Principe , and Pablo Zegers . 2014. Computational intelligence challenges and applications on large-scale astronomical time series databases. CIM ( 2014 ). Pablo Huijse, Pablo A Estevez, Pavlos Protopapas, Jose C Principe, and Pablo Zegers. 2014. Computational intelligence challenges and applications on large-scale astronomical time series databases. CIM (2014)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Piotr Indyk and Rajeev Motwani. 1998. Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality. In STOC.  Piotr Indyk and Rajeev Motwani. 1998. Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality. In STOC.","DOI":"10.1145\/276698.276876"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.57"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"crossref","unstructured":"Kunio Kashino Gavin Smith and Hiroshi Murase. 1999. Time-series active search for quick retrieval of audio and video. In ICASSP.  Kunio Kashino Gavin Smith and Hiroshi Murase. 1999. Time-series active search for quick retrieval of audio and video. In ICASSP.","DOI":"10.1109\/ICASSP.1999.757470"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Shrikant Kashyap and Panagiotis Karras. 2011. Scalable kNN search on vertically stored time series.. In KDD. ACM 1334--1342.  Shrikant Kashyap and Panagiotis Karras. 2011. Scalable kNN search on vertically stored time series.. In KDD. ACM 1334--1342.","DOI":"10.1145\/2020408.2020607"},{"key":"e_1_3_2_1_40_1","volume-title":"A Probabilistic Approach to Fast Pattern Matching in Time Series Databases","author":"Keogh Eamonn","unstructured":"Eamonn Keogh and Padhraic Smyth . 1997. A Probabilistic Approach to Fast Pattern Matching in Time Series Databases . In KDD. AAAI Press , 24--30. Eamonn Keogh and Padhraic Smyth. 1997. A Probabilistic Approach to Fast Pattern Matching in Time Series Databases. In KDD. AAAI Press, 24--30."},{"key":"e_1_3_2_1_41_1","volume-title":"Keogh and Shruti Kasetty","author":"Eamonn","year":"2003","unstructured":"Eamonn J. Keogh and Shruti Kasetty . 2003 . On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. DAMI ( 2003). Eamonn J. Keogh and Shruti Kasetty. 2003. On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. DAMI (2003)."},{"key":"e_1_3_2_1_42_1","first-page":"677","article-title":"Coconut: A Scalable Bottom-Up Approach for Building Data Series Indexes","volume":"11","author":"Kondylakis Haridimos","year":"2018","unstructured":"Haridimos Kondylakis , Niv Dayan , Kostas Zoumpatianos , and Themis Palpanas . 2018 . Coconut: A Scalable Bottom-Up Approach for Building Data Series Indexes . PVLDB 11 , 6 (2018), 677 -- 690 . Haridimos Kondylakis, Niv Dayan, Kostas Zoumpatianos, and Themis Palpanas. 2018. Coconut: A Scalable Bottom-Up Approach for Building Data Series Indexes. PVLDB 11, 6 (2018), 677--690.","journal-title":"PVLDB"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-019-00573-w"},{"key":"e_1_3_2_1_44_1","first-page":"2236","article-title":"Scalable, Variable-length Similarity Search in Data Series: The ULISSE Approach","volume":"11","author":"Linardi Michele","year":"2018","unstructured":"Michele Linardi and Themis Palpanas . 2018 . Scalable, Variable-length Similarity Search in Data Series: The ULISSE Approach . PVLDB 11 , 13 (2018), 2236 -- 2248 . Michele Linardi and Themis Palpanas. 2018. Scalable, Variable-length Similarity Search in Data Series: The ULISSE Approach. PVLDB 11, 13 (2018), 2236--2248.","journal-title":"PVLDB"},{"key":"e_1_3_2_1_45_1","volume-title":"ULISSE: ULtra compact Index for Variable-Length Similarity SEarch in Data Series. In ICDE.","author":"Linardi Michele","year":"2018","unstructured":"Michele Linardi and Themis Palpanas . 2018 . ULISSE: ULtra compact Index for Variable-Length Similarity SEarch in Data Series. In ICDE. Michele Linardi and Themis Palpanas. 2018. ULISSE: ULtra compact Index for Variable-Length Similarity SEarch in Data Series. In ICDE."},{"key":"e_1_3_2_1_46_1","volume-title":"Keogh","author":"Linardi Michele","year":"2020","unstructured":"Michele Linardi , Yan Zhu , Themis Palpanas , and Eamonn J . Keogh . 2020 . Matrix Profile Goes MAD: Variable- Length Motif And Discord Discovery in Data Series. In DAMI. Michele Linardi, Yan Zhu, Themis Palpanas, and Eamonn J. Keogh. 2020. Matrix Profile Goes MAD: Variable-Length Motif And Discord Discovery in Data Series. In DAMI."},{"key":"e_1_3_2_1_47_1","unstructured":"Yury A. Malkov and D. A. Yashunin. 2016. Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs. CoRR abs\/1603.09320 (2016).  Yury A. Malkov and D. A. Yashunin. 2016. Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs. CoRR abs\/1603.09320 (2016)."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.14778\/3229863.3240491"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"crossref","unstructured":"Abdullah Mueen Suman Nath and Jie Liu. 2010. Fast approximate correlation for massive time-series data. In SIGMOD.  Abdullah Mueen Suman Nath and Jie Liu. 2010. Fast approximate correlation for massive time-series data. In SIGMOD.","DOI":"10.1145\/1807167.1807188"},{"key":"e_1_3_2_1_50_1","unstructured":"Abdullah Mueen Yan Zhu Michael Yeh Kaveh Kamgar Krishnamurthy Viswanathan Chetan Gupta and Eamonn Keogh. 2017. The Fastest Similarity Search Algorithm for Time Series Subsequences under Euclidean Distance.  Abdullah Mueen Yan Zhu Michael Yeh Kaveh Kamgar Krishnamurthy Viswanathan Chetan Gupta and Eamonn Keogh. 2017. The Fastest Similarity Search Algorithm for Time Series Subsequences under Euclidean Distance."},{"key":"e_1_3_2_1_51_1","volume-title":"VISAPP International Conference on Computer Vision Theory and Applications. 331--340","author":"Muja Marius","unstructured":"Marius Muja and David G. Lowe . 2009. Fast approximate nearest neighbors with automatic algorithm configuration . In VISAPP International Conference on Computer Vision Theory and Applications. 331--340 . Marius Muja and David G. Lowe. 2009. Fast approximate nearest neighbors with automatic algorithm configuration. In VISAPP International Conference on Computer Vision Theory and Applications. 331--340."},{"key":"e_1_3_2_1_52_1","volume-title":"Cartesian K-Means. In Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR '13)","author":"Norouzi Mohammad","unstructured":"Mohammad Norouzi and David J. Fleet . 2013 . Cartesian K-Means. In Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR '13) . 3017--3024. Mohammad Norouzi and David J. Fleet. 2013. Cartesian K-Means. In Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR '13). 3017--3024."},{"key":"e_1_3_2_1_53_1","volume-title":"Data Series Management: The Road to Big Sequence Analytics. SIGMOD Record","author":"Palpanas Themis","year":"2015","unstructured":"Themis Palpanas . 2015. Data Series Management: The Road to Big Sequence Analytics. SIGMOD Record ( 2015 ). Themis Palpanas. 2015. Data Series Management: The Road to Big Sequence Analytics. SIGMOD Record (2015)."},{"key":"e_1_3_2_1_54_1","volume-title":"Evolution of a Data Series Index. CCIS 1197","author":"Palpanas Themis","year":"2020","unstructured":"Themis Palpanas . 2020. Evolution of a Data Series Index. CCIS 1197 ( 2020 ). Themis Palpanas. 2020. Evolution of a Data Series Index. CCIS 1197 (2020)."},{"key":"e_1_3_2_1_55_1","volume-title":"Report on the First and Second Interdisciplinary Time Series Analysis Workshop (ITISA). SIGMOD Rec. 48","author":"Palpanas Themis","year":"2019","unstructured":"Themis Palpanas and Volker Beckmann . 2019 . Report on the First and Second Interdisciplinary Time Series Analysis Workshop (ITISA). SIGMOD Rec. 48 , 3 (2019). Themis Palpanas and Volker Beckmann. 2019. Report on the First and Second Interdisciplinary Time Series Analysis Workshop (ITISA). SIGMOD Rec. 48, 3 (2019)."},{"key":"e_1_3_2_1_56_1","volume-title":"ParIS: The Next Destination for Fast Data Series Indexing and Query Answering","author":"Peng Botao","year":"2018","unstructured":"Botao Peng , Panagiota Fatourou , and Themis Palpanas . 2018. ParIS: The Next Destination for Fast Data Series Indexing and Query Answering . IEEE BigData ( 2018 ). Botao Peng, Panagiota Fatourou, and Themis Palpanas. 2018. ParIS: The Next Destination for Fast Data Series Indexing and Query Answering. IEEE BigData (2018)."},{"key":"e_1_3_2_1_57_1","volume-title":"MESSI: In-Memory Data Series Indexing. In ICDE.","author":"Peng Botao","year":"2020","unstructured":"Botao Peng , Themis Palpanas , and Panagiota Fatourou . 2020 . MESSI: In-Memory Data Series Indexing. In ICDE. Botao Peng, Themis Palpanas, and Panagiota Fatourou. 2020. MESSI: In-Memory Data Series Indexing. In ICDE."},{"key":"e_1_3_2_1_58_1","volume-title":"Data Series Indexing on Multi-core Architectures. TKDE","author":"Peng Botao","year":"2020","unstructured":"Botao Peng , Themis Palpanas , and Panagiota Fatourou . 2020. ParIS+ : Data Series Indexing on Multi-core Architectures. TKDE ( 2020 ). Botao Peng, Themis Palpanas, and Panagiota Fatourou. 2020. ParIS+: Data Series Indexing on Multi-core Architectures. TKDE (2020)."},{"key":"e_1_3_2_1_59_1","volume-title":"Keogh","author":"Petitjean Fran\u00e7ois","year":"2014","unstructured":"Fran\u00e7ois Petitjean , Germain Forestier , Geoffrey I. Webb , Ann E. Nicholson , Yanping Chen , and Eamonn J . Keogh . 2014 . Dynamic Time Warping Averaging of Time Series Allows Faster and More Accurate Classification. In ICDM. Fran\u00e7ois Petitjean, Germain Forestier, Geoffrey I. Webb, Ann E. Nicholson, Yanping Chen, and Eamonn J. Keogh. 2014. Dynamic Time Warping Averaging of Time Series Allows Faster and More Accurate Classification. In ICDM."},{"key":"e_1_3_2_1_60_1","unstructured":"Davood Rafiei. 1999. On Similarity-Based Queries for Time Series Data. In ICDE. 410--417.  Davood Rafiei. 1999. On Similarity-Based Queries for Time Series Data. In ICDE. 410--417."},{"key":"e_1_3_2_1_61_1","volume-title":"Keogh","author":"Rakthanmanon Thanawin","year":"2012","unstructured":"Thanawin Rakthanmanon , Bilson J. L. Campana , Abdullah Mueen , Gustavo E. A. P. A. Batista , M. Brandon Westover , Qiang Zhu , Jesin Zakaria , and Eamonn J . Keogh . 2012 . Searching and mining trillions of time series subsequences under dynamic time warping.. In KDD. ACM , 262--270. Thanawin Rakthanmanon, Bilson J. L. Campana, Abdullah Mueen, Gustavo E. A. P. A. Batista, M. Brandon Westover, Qiang Zhu, Jesin Zakaria, and Eamonn J. Keogh. 2012. Searching and mining trillions of time series subsequences under dynamic time warping.. In KDD. ACM, 262--270."},{"key":"e_1_3_2_1_62_1","volume-title":"Practical data prediction for real-world wireless sensor networks. TKDE","author":"Raza Usman","year":"2015","unstructured":"Usman Raza , Alessandro Camerra , Amy L Murphy , Themis Palpanas , and Gian Pietro Picco . 2015. Practical data prediction for real-world wireless sensor networks. TKDE ( 2015 ). Usman Raza, Alessandro Camerra, Amy L Murphy, Themis Palpanas, and Gian Pietro Picco. 2015. Practical data prediction for real-world wireless sensor networks. TKDE (2015)."},{"key":"e_1_3_2_1_63_1","volume-title":"Foundations of Multidimensional and Metric Data Structures","author":"Samet Hanan","unstructured":"Hanan Samet . 2005. Foundations of Multidimensional and Metric Data Structures . Morgan Kaufmann Publishers Inc . Hanan Samet. 2005. Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann Publishers Inc."},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/2247596.2247656"},{"key":"e_1_3_2_1_65_1","volume-title":"Tuning time series queries in finance: Case studies and recommendations","author":"Shasha Dennis","year":"1999","unstructured":"Dennis Shasha . 1999. Tuning time series queries in finance: Case studies and recommendations . IEEE Data Eng. Bull . ( 1999 ). Dennis Shasha. 1999. Tuning time series queries in finance: Case studies and recommendations. IEEE Data Eng. Bull. (1999)."},{"key":"e_1_3_2_1_66_1","unstructured":"H. Shatkay and S. B. Zdonik. 1996. Approximate queries and representations for large data sequences. In ICDE. 536--545.  H. Shatkay and S. B. Zdonik. 1996. Approximate queries and representations for large data sequences. In ICDE. 536--545."},{"key":"e_1_3_2_1_67_1","unstructured":"Jin Shieh and Eamonn Keogh. 2008. iSAX: Indexing and Mining Terabyte Sized Time Series. In SIGKDD. ACM 623--631.  Jin Shieh and Eamonn Keogh. 2008. iSAX: Indexing and Mining Terabyte Sized Time Series. In SIGKDD. ACM 623--631."},{"key":"e_1_3_2_1_68_1","unstructured":"Jin Shieh and Eamonn Keogh. 2008. iSAX: Indexing and Mining Terabyte Sized Time Series. In KDD.  Jin Shieh and Eamonn Keogh. 2008. iSAX: Indexing and Mining Terabyte Sized Time Series. In KDD."},{"key":"e_1_3_2_1_69_1","volume-title":"SRS: Solving c-approximate Nearest Neighbor Queries in High Dimensional Euclidean Space with a Tiny Index. PVLDB 8, 1","author":"Sun Yifang","year":"2014","unstructured":"Yifang Sun , Wei Wang , Jianbin Qin , Ying Zhang , and Xuemin Lin . 2014 . SRS: Solving c-approximate Nearest Neighbor Queries in High Dimensional Euclidean Space with a Tiny Index. PVLDB 8, 1 (2014). Yifang Sun, Wei Wang, Jianbin Qin, Ying Zhang, and Xuemin Lin. 2014. SRS: Solving c-approximate Nearest Neighbor Queries in High Dimensional Euclidean Space with a Tiny Index. PVLDB 8, 1 (2014)."},{"key":"e_1_3_2_1_70_1","unstructured":"Changzhou Wang and Xiaoyang Sean Wang. 2000. Supporting content-based searches on time series via approximation. In SSDBM. 69--81.  Changzhou Wang and Xiaoyang Sean Wang. 2000. Supporting content-based searches on time series via approximation. In SSDBM. 69--81."},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"crossref","unstructured":"Jizhe Wang Pipei Huang Huan Zhao Zhibo Zhang Binqiang Zhao and Dik Lun Lee. 2018. Billion-Scale Commodity Embedding for E-Commerce Recommendation in Alibaba. In KDD.  Jizhe Wang Pipei Huang Huan Zhao Zhibo Zhang Binqiang Zhao and Dik Lun Lee. 2018. Billion-Scale Commodity Embedding for E-Commerce Recommendation in Alibaba. In KDD.","DOI":"10.1145\/3219819.3219869"},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.14778\/2536206.2536208"},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.5555\/645924.671192"},{"key":"e_1_3_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.424"},{"key":"e_1_3_2_1_75_1","doi-asserted-by":"crossref","unstructured":"D. E. Yagoubi R. Akbarinia F. Masseglia and T. Palpanas. 2017. DPiSAX: Massively Distributed Partitioned iSAX. In ICDM. 1135--1140.  D. E. Yagoubi R. Akbarinia F. Masseglia and T. Palpanas. 2017. DPiSAX: Massively Distributed Partitioned iSAX. In ICDM. 1135--1140.","DOI":"10.1109\/ICDM.2017.151"},{"key":"e_1_3_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2880215"},{"key":"e_1_3_2_1_77_1","unstructured":"Lexiang Ye and Eamonn Keogh. 2009. Time series shapelets: a new primitive for data mining. In SIGKDD. ACM.  Lexiang Ye and Eamonn Keogh. 2009. Time series shapelets: a new primitive for data mining. In SIGKDD. ACM."},{"key":"e_1_3_2_1_78_1","volume-title":"TARDIS: Distributed Indexing Framework for Big Time Series Data. In ICDE.","author":"Zhang L.","year":"2019","unstructured":"L. Zhang , N. Alghamdi , M. Y. Eltabakh , and E. A. Rundensteiner . 2019 . TARDIS: Distributed Indexing Framework for Big Time Series Data. In ICDE. L. Zhang, N. Alghamdi, M. Y. Eltabakh, and E. A. Rundensteiner. 2019. TARDIS: Distributed Indexing Framework for Big Time Series Data. In ICDE."},{"key":"e_1_3_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-016-0442-5"},{"key":"e_1_3_2_1_80_1","doi-asserted-by":"crossref","unstructured":"Kostas Zoumpatianos Yin Lou Ioana Ileana Themis Palpanas and Johannes Gehrke. 2018. Generating data series query workloads. VLDB J. (2018).  Kostas Zoumpatianos Yin Lou Ioana Ileana Themis Palpanas and Johannes Gehrke. 2018. Generating data series query workloads. VLDB J. (2018).","DOI":"10.1007\/s00778-018-0513-x"},{"key":"e_1_3_2_1_81_1","doi-asserted-by":"crossref","unstructured":"Kostas Zoumpatianos and Themis Palpanas. 2018. Data Series Management: Fulfilling the Need for Big Sequence Analytics. In ICDE.  Kostas Zoumpatianos and Themis Palpanas. 2018. Data Series Management: Fulfilling the Need for Big Sequence Analytics. In ICDE.","DOI":"10.1109\/ICDE.2018.00211"}],"event":{"name":"WIMS 2020: The 10th International Conference on Web Intelligence, Mining and Semantics","location":"Biarritz France","acronym":"WIMS 2020"},"container-title":["Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3405962.3405989","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3405962.3405989","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:31:51Z","timestamp":1750195911000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3405962.3405989"}},"subtitle":["From Data Series to Deep Network Embeddings"],"short-title":[],"issued":{"date-parts":[[2020,6,30]]},"references-count":81,"alternative-id":["10.1145\/3405962.3405989","10.1145\/3405962"],"URL":"https:\/\/doi.org\/10.1145\/3405962.3405989","relation":{},"subject":[],"published":{"date-parts":[[2020,6,30]]},"assertion":[{"value":"2020-08-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}