{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T02:35:53Z","timestamp":1772850953206,"version":"3.50.1"},"reference-count":10,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:p>\n            Renewable Energy Sources (RESs) are monitored by many high-quality sensors that produce vast amounts of high-frequency time series data. This can be used to increase the renewable energy production and longevity of the RESs, e.g., yaw misalignment detection and predictive maintenance for wind turbines. It is currently not possible for wind turbine manufacturers and owners to use this data due to limits on bandwidth and storage that are infeasible to increase. Thus, they store simple aggregates which remove valuable outliers and fluctuations. As a remedy, we demonstrate the new model-based Time Series Management System (TSMS) ModelarDB. The participants can experience how ModelarDB ingests time series on the edge and compresses them as\n            <jats:italic toggle=\"yes\">segments<\/jats:italic>\n            with metadata and so-called\n            <jats:italic toggle=\"yes\">models.<\/jats:italic>\n            The models represent values within a user-defined absolute or relative error bound (even 0 or 0%). Participants can adjust many parameters and see how the segments are transferred to the cloud using much less bandwidth and storage than other popular solutions like Apache Parquet and Apache TsFile, e.g., up to 90%\u201399% less than Apache Parquet. Participants can analyze the time series on the edge, in the cloud, and on the client using SQL or Python. On the client, ModelarDB runs in-process to integrate with, e.g., Python. Thus, participants can see how ModelarDB efficiently manages high-frequency time series across edge, cloud, and client.\n          <\/jats:p>","DOI":"10.14778\/3750601.3750643","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:38:05Z","timestamp":1758029885000},"page":"5247-5250","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Demonstration of ModelarDB: Model-Based Management of High-Frequency Time Series Across Edge, Cloud, and Client"],"prefix":"10.14778","volume":"18","author":[{"given":"S\u00f8ren Kejser","family":"Jensen","sequence":"first","affiliation":[{"name":"Aalborg University, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian Schmidt","family":"Godiksen","sequence":"additional","affiliation":[{"name":"Aalborg University, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Thomsen","sequence":"additional","affiliation":[{"name":"Aalborg University, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Torben Bach","family":"Pedersen","sequence":"additional","affiliation":[{"name":"Aalborg University, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.14778\/3704965.3704978"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.14778\/1687627.1687645"},{"key":"e_1_2_1_3_1","series-title":"Time Series Management Systems: A 2022 Survey","volume-title":"Torben Bach Pedersen, and Christian Thomsen. [n.d.]","author":"Jensen S\u00f8ren Kejser","year":"2022","unstructured":"S\u00f8ren Kejser Jensen, Torben Bach Pedersen, and Christian Thomsen. [n.d.]. Time Series Management Systems: A 2022 Survey. In Data Series Management and Analytics (Forthcoming), Themis Palpanas and Kostas Zoumpatianos (Eds.). ACM. Preprint is at: https:\/\/vbn.aau.dk\/da\/publications\/time-series-management-systems-a-2022-survey."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2740932"},{"key":"e_1_2_1_5_1","volume-title":"ADBIS (Short Papers)","author":"Jensen S\u00f8ren Kejser","unstructured":"S\u00f8ren Kejser Jensen and Christian Thomsen. 2023. Holistic Analytics of Sensor Data from Renewable Energy Sources: A Vision Paper. In ADBIS (Short Papers). Springer, 360\u2013366."},{"key":"e_1_2_1_6_1","volume-title":"Carlos Enrique Mu\u00f1iz-Cuza, and Abduvoris Abduvakhobov.","author":"Jensen S\u00f8ren Kejser","year":"2024","unstructured":"S\u00f8ren Kejser Jensen, Christian Thomsen, Torben Bach Pedersen, Carlos Enrique Mu\u00f1iz-Cuza, and Abduvoris Abduvakhobov. 2024. Why Model-Based Lossy Compression is Great for Wind Turbine Analytics. In ICDE. IEEE, 5667\u20135668."},{"key":"e_1_2_1_7_1","volume-title":"Capturing Sensor-Generated Time Series with Quality Guarantees","author":"Lazaridis Iosif","unstructured":"Iosif Lazaridis and Sharad Mehrotra. 2003. Capturing Sensor-Generated Time Series with Quality Guarantees. In ICDE. IEEE, 429\u2013440."},{"key":"e_1_2_1_8_1","unstructured":"ModelarDB 2025. https:\/\/github.com\/ModelarData\/ModelarDB-RS. Commit: 66a4abf7f10f7a790dd195f0d962fe44183100cf Viewed: 2025-07-24."},{"key":"e_1_2_1_9_1","volume-title":"Jonas Brusokas, Nguyen Ho, and Torben Bach Pedersen.","author":"Mu\u00f1iz-Cuza Carlos Enrique","year":"2024","unstructured":"Carlos Enrique Mu\u00f1iz-Cuza, S\u00f8ren Kejser Jensen, Jonas Brusokas, Nguyen Ho, and Torben Bach Pedersen. 2024. Evaluating the Impact of Error-Bounded Lossy Compression on Time Series Forecasting. In EDBT. OpenProceedings.org, 650\u2013663."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.14778\/2824032.2824078"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3750601.3750643","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:38:52Z","timestamp":1758029932000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3750601.3750643"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8]]},"references-count":10,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["10.14778\/3750601.3750643"],"URL":"https:\/\/doi.org\/10.14778\/3750601.3750643","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2025,8]]},"assertion":[{"value":"2025-09-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}