{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T06:44:38Z","timestamp":1782197078875,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":43,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,6,22]]},"DOI":"10.1145\/3736733.3736742","type":"proceedings-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T19:03:51Z","timestamp":1752001431000},"page":"1-8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["From Similarities to Insights: Approaching Time Series Integration from a User Perspective"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6877-6935","authenticated-orcid":false,"given":"Lucas","family":"Weber","sequence":"first","affiliation":[{"name":"Computer Science - Computer Science 6, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1551-4824","authenticated-orcid":false,"given":"Richard","family":"Lenz","sequence":"additional","affiliation":[{"name":"Computer Science - Computer Science 6, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,7,8]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Improved visual saliency of graph clusters with orderable node-link layouts","author":"Al-Naami Nora","year":"2024","unstructured":"Nora Al-Naami, Nicolas M\u00e9doc, Matteo Magnani, and Mohammad Ghoniem. 2024. Improved visual saliency of graph clusters with orderable node-link layouts. IEEE Transactions on Visualization and Computer Graphics (2024)."},{"key":"e_1_3_2_1_2_1","volume-title":"2008 12th International Conference Information Visualisation. IEEE, 280\u2013286","author":"Alencar Aretha Barbosa","year":"2008","unstructured":"Aretha Barbosa Alencar, Fernando Vieira Paulovich, Rosane Minghim, Marinho Gomes de Andrade Filho, and Maria Cristina Ferreira de Oliveira. 2008. Similarity-based visualization of time series collections: An application to analysis of streamflows. In 2008 12th International Conference Information Visualisation. IEEE, 280\u2013286."},{"key":"e_1_3_2_1_3_1","first-page":"907","article-title":"Understanding barriers to network exploration with visualization: A report from the trenches","volume":"29","author":"AlKadi Mashael","year":"2022","unstructured":"Mashael AlKadi, Vanessa Serrano, James Scott-Brown, Catherine Plaisant, Jean-Daniel Fekete, Uta Hinrichs, and Benjamin Bach. 2022. Understanding barriers to network exploration with visualization: A report from the trenches. IEEE Transactions on Visualization and Computer Graphics 29, 1 (2022), 907\u2013917.","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2470654.2470724"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2993422.2993577"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12935"},{"key":"e_1_3_2_1_7_1","volume-title":"Lin Shao, Mennatallah El-Assady, Johannes Fuchs, Daniel Seebacher, Alexandra Diehl, Ulrik Brandes, Hanspeter Pfister, et al.","author":"Behrisch Michael","year":"2018","unstructured":"Michael Behrisch, Michael Blumenschein, Nam Wook Kim, Lin Shao, Mennatallah El-Assady, Johannes Fuchs, Daniel Seebacher, Alexandra Diehl, Ulrik Brandes, Hanspeter Pfister, et al. 2018. Quality metrics for information visualization. In Computer Graphics Forum, Vol. 37. Wiley Online Library, 625\u2013662."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2024.114637"},{"key":"e_1_3_2_1_9_1","volume-title":"Modern multidimensional scaling: Theory and applications","author":"Borg Ingwer","unstructured":"Ingwer Borg and Patrick JF Groenen. 2007. Modern multidimensional scaling: Theory and applications. Springer Science & Business Media."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/MCG.2020.3033401","article-title":"Cartolabe: A web-based scalable visualization of large document collections","volume":"41","author":"Caillou Philippe","year":"2020","unstructured":"Philippe Caillou, Jonas Renault, Jean-Daniel Fekete, Anne-Catherine Letournel, and Mich\u00e8le Sebag. 2020. Cartolabe: A web-based scalable visualization of large document collections. IEEE Computer Graphics and Applications 41, 2 (2020), 76\u201388.","journal-title":"IEEE Computer Graphics and Applications"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2020.2990566"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10844-014-0304-9"},{"key":"e_1_3_2_1_13_1","volume-title":"Empirical Mode Decomposition for Intrinsic-Relationship Extraction in Large Sensor Deployments. In Workshop on Internet of Things Application.","author":"Fontugne Romain","year":"2012","unstructured":"Romain Fontugne, Jorge Ortiz, David Culler, and Hiroshi Esaki. 2012. Empirical Mode Decomposition for Intrinsic-Relationship Extraction in Large Sensor Deployments. In Workshop on Internet of Things Application."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOM.2015.7146503"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2016.08.066"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v025.i03"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3360322.3360852"},{"key":"e_1_3_2_1_18_1","volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. 642\u2013651","author":"Hong Dezhi","year":"2017","unstructured":"Dezhi Hong, Quanquan Gu, and Kamin Whitehouse. 2017. High-dimensional Time Series Clustering via Cross-Predictability. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. 642\u2013651."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2528282.2528302"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.14778\/3611540.3611570"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2674061.2674075"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5900"},{"key":"e_1_3_2_1_23_1","unstructured":"Laurens Van Der Maaten. 2009. Learning a parametric embedding by preserving local structure. In Artificial intelligence and statistics. PMLR 384\u2013391."},{"key":"e_1_3_2_1_24_1","first-page":"2579","article-title":"Visualizing data using t-SNE","author":"van der Maaten Laurens","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research 9, Nov (2008), 2579\u20132605.","journal-title":"Journal of machine learning research 9"},{"key":"e_1_3_2_1_25_1","volume-title":"International Conference on Augmented Cognition. Springer, 322\u2013332","author":"Martin Shawn","year":"2016","unstructured":"Shawn Martin and Tu-Toan Quach. 2016. Interactive visualization of multivariate time series data. In International Conference on Augmented Cognition. Springer, 322\u2013332."},{"key":"e_1_3_2_1_26_1","volume-title":"Correlation networks: Interdisciplinary approaches beyond thresholding. arXiv preprint arXiv:2311.09536","author":"Masuda Naoki","year":"2023","unstructured":"Naoki Masuda, Zachary M Boyd, Diego Garlaschelli, and Peter J Mucha. 2023. Correlation networks: Interdisciplinary approaches beyond thresholding. arXiv preprint arXiv:2311.09536 (2023)."},{"key":"e_1_3_2_1_27_1","volume-title":"Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426","author":"McInnes Leland","year":"2018","unstructured":"Leland McInnes, John Healy, and James Melville. 2018. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 (2018)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvlc.2006.09.002"},{"key":"e_1_3_2_1_29_1","volume-title":"ASHRAE Winter Conference Proceedings. https:\/\/www.researchgate.net\/publication\/322661985","author":"Park June Young","year":"2018","unstructured":"June Young Park, Bertrand Lasternas, and Azizan Aziz. 2018. Data-Driven Framework to Find the Physical Association between AHU and VAV Terminal Unit-Pilot Study. In ASHRAE Winter Conference Proceedings. https:\/\/www.researchgate.net\/publication\/322661985"},{"key":"e_1_3_2_1_30_1","volume-title":"Empirical guidance on scatterplot and dimension reduction technique choices","author":"Sedlmair Michael","year":"2013","unstructured":"Michael Sedlmair, Tamara Munzner, and Melanie Tory. 2013. Empirical guidance on scatterplot and dimension reduction technique choices. IEEE transactions on visualization and computer graphics 19, 12 (2013), 2634\u20132643."},{"key":"e_1_3_2_1_31_1","volume-title":"Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. 623\u2013631","author":"Shieh Jin","year":"2008","unstructured":"Jin Shieh and Eamonn Keogh. 2008. iSAX: indexing and mining terabyte sized time series. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. 623\u2013631."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Marina Sofos Jared Langevin Michael Deru Erika Gupta Kyle Benne David Blum Ted Bohn Robert Fares Nick Fernandez Glenn Fink Steven Frank Jennifer Gerbi Jessica Granderson Dale Hoffmeyer Tianzhen Hong Amy Jiron Stephanie Johnson Srinivas Katipamula Teja Kuruganti Jared Langevin William Livingood Ralph Muehleisen Monica Neukomm Valerie Nubbe Patrick Phelan MaryAnn Piette Janet Reyna Amir Roth Aven Satre-Meloy Michael Specian Draguna Vrabie Michael Wetter and Steve Widergren. 2020. Innovations in Sensors and Controls for Building Energy Management: Research and Development Opportunities Report for Emerging Technologies. Technical Report DOE\/GO-102019-5234. National Renewable Energy Laboratory (NREL). 10.2172\/1601591","DOI":"10.2172\/1601591"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2022.104248"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1343\/1\/012041"},{"key":"e_1_3_2_1_35_1","unstructured":"Hendrik Strobelt and Benjamin Hoover. 2022. Interactive Corpora Visualization for 60 Years of AI Research. In NeurIPS 2021 Competitions and Demonstrations Track. PMLR 278\u2013282."},{"key":"e_1_3_2_1_36_1","volume-title":"A global geometric framework for nonlinear dimensionality reduction. science 290, 5500","author":"Tenenbaum Joshua B","year":"2000","unstructured":"Joshua B Tenenbaum, Vin de Silva, and John C Langford. 2000. A global geometric framework for nonlinear dimensionality reduction. science 290, 5500 (2000), 2319\u20132323."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.buildenv.2023.110968"},{"key":"e_1_3_2_1_38_1","first-page":"1","article-title":"Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization","volume":"22","author":"Wang Yingfan","year":"2021","unstructured":"Yingfan Wang, Haiyang Huang, Cynthia Rudin, and Yaron Shaposhnik. 2021. Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization. Journal of Machine Learning Research 22, 201 (2021), 1\u201373. http:\/\/jmlr.org\/papers\/v22\/20-1061.html","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1515\/itit-2023-0051"},{"key":"e_1_3_2_1_40_1","volume-title":"Datenbanksysteme f\u00fcr Business, Technologie und Web (BTW","author":"Weber Lucas","year":"2025","unstructured":"Lucas Weber and Richard Lenz. 2025. Relationship Discovery for Heterogeneous Time Series Integration: A Comparative Analysis for Industrial and Building Data. In Datenbanksysteme f\u00fcr Business, Technologie und Web (BTW 2025). Gesellschaft f\u00fcr Informatik, Bonn, 475\u2013498."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3600100.3623736"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2021.3119871"},{"key":"e_1_3_2_1_43_1","unstructured":"Jiangning Zhu Zheng Wang Zhiyang Shen Lai Wei Fengyuan Tian Mengchen Liu and Shixia Liu. 2024. ReorderBench: A Benchmark for Matrix Reordering. arXiv:2408.12169 [cs.HC] https:\/\/arxiv.org\/abs\/2408.12169"}],"event":{"name":"HILDA '25: Workshop on Human-In-the-Loop Data Analytics","location":"Intercontinental Berlin Berlin Germany","acronym":"HILDA '25","sponsor":["SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the Workshop on Human-In-the-Loop Data Analytics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3736733.3736742","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T19:04:35Z","timestamp":1752001475000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3736733.3736742"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,22]]},"references-count":43,"alternative-id":["10.1145\/3736733.3736742","10.1145\/3736733"],"URL":"https:\/\/doi.org\/10.1145\/3736733.3736742","relation":{},"subject":[],"published":{"date-parts":[[2025,6,22]]},"assertion":[{"value":"2025-07-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}