{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T17:34:20Z","timestamp":1783791260330,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819755684","type":"print"},{"value":"9789819755691","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-5569-1_27","type":"book-chapter","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T03:27:18Z","timestamp":1733974038000},"page":"409-425","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["AdpDM: Adaptive Data Model for Efficient Dynamic Management of Large-Scale High-Cardinality Time-Series Databases"],"prefix":"10.1007","author":[{"given":"Ziyue","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sutong","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Di","family":"Fei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liping","family":"Yi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenfei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoguang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenqing","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zijing","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaozhi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"key":"27_CR1","unstructured":"Time series benchmark suite, a tool for comparing and evaluating databases for time series data., https:\/\/github.com\/timescale\/tsbs"},{"key":"27_CR2","unstructured":"Prometheus - from metrics to insight, power your metrics and alerting with a leading open-source monitoring solution (2020), https:\/\/prometheus.io\/"},{"key":"27_CR3","unstructured":"Influxdb: Open source time series database. (2022), https:\/\/www.influxdata.com"},{"key":"27_CR4","unstructured":"Timescale: Time-series data simplified (2022), https:\/\/www.timescale.com"},{"issue":"12","key":"27_CR5","doi-asserted-by":"publisher","first-page":"3181","DOI":"10.14778\/3181-3194","volume":"13","author":"C Adams","year":"2020","unstructured":"Adams, C., Alonso, L., Atkin, B., Banning, J., Bhola, S., Buskens, R., Chen, M., Chen, X., Chung, Y., Jia, Q., et\u00a0al.: Monarch: Google\u2019s planet-scale in-memory time series database. Proceedings of the VLDB Endowment 13(12), 3181\u20133194 (2020)","journal-title":"Proceedings of the VLDB Endowment"},{"key":"27_CR6","unstructured":"Andersen, M.P., Culler, D.E.: Btrdb: Optimizing storage system design for timeseries processing. In: 14th $$\\{$$USENIX$$\\}$$ Conference on File and Storage Technologies ($$\\{$$FAST$$\\}$$ 16). pp. 39\u201352 (2016)"},{"key":"27_CR7","unstructured":"Bader, A., Kopp, O., Falkenthal, M.: Survey and comparison of open source time series databases. Datenbanksysteme f\u00fcr Business, Technologie und Web (BTW 2017)-Workshopband (2017)"},{"key":"27_CR8","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Machine learning 45, 5\u201332 (2001)","journal-title":"Random forests. Machine learning"},{"key":"27_CR9","doi-asserted-by":"crossref","unstructured":"Cao, W., Gao, Y., Li, F., Wang, S., Lin, B., Xu, K., Feng, X., Wang, Y., Liu, Z., Zhang, G.: Timon: A timestamped event database for efficient telemetry data processing and analytics. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. pp. 739\u2013753 (2020)","DOI":"10.1145\/3318464.3386136"},{"key":"27_CR10","unstructured":"Cloud Native Computing Foundation: Horizontally scalable, highly available, multi-tenant, long term Prometheus (2020), https:\/\/cortexmetrics.io\/"},{"issue":"16","key":"27_CR11","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1016\/j.ipl.2010.05.018","volume":"110","author":"A Colantonio","year":"2010","unstructured":"Colantonio, A., Di\u00a0Pietro, R.: Concise: Compressed \u2018n\u2019composable integer set. Information Processing Letters 110(16), 644\u2013650 (2010)","journal-title":"Information Processing Letters"},{"key":"27_CR12","doi-asserted-by":"crossref","unstructured":"Deri, L., Mainardi, S., Fusco, F.: tsdb: A compressed database for time series. In: Traffic Monitoring and Analysis: 4th International Workshop, TMA 2012, Vienna, Austria, March 12, 2012. Proceedings 4. pp. 143\u2013156. Springer (2012)","DOI":"10.1007\/978-3-642-28534-9_16"},{"key":"27_CR13","doi-asserted-by":"publisher","unstructured":"Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119\u2013139 (1997). https:\/\/doi.org\/10.1006\/jcss.1997.1504, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S002200009791504X","DOI":"10.1006\/jcss.1997.1504"},{"key":"27_CR14","unstructured":"Fuller, W.A.: Introduction to statistical time series. John Wiley & Sons (2009)"},{"key":"27_CR15","first-page":"3","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Machine learning 63, 3\u201342 (2006)","journal-title":"Extremely randomized trees. Machine learning"},{"key":"27_CR16","doi-asserted-by":"crossref","unstructured":"Huang, X., Wang, J., Wong, R., Zhang, J., Wang, C.: Pisa: An index for aggregating big time series data. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. pp. 979\u2013988 (2016)","DOI":"10.1145\/2983323.2983775"},{"key":"27_CR17","unstructured":"Inc. Timescale.: Time-series data simplified $$|$$ Timescale. (2020), https:\/\/www.timescale.com\/"},{"issue":"11","key":"27_CR18","doi-asserted-by":"publisher","first-page":"2581","DOI":"10.1109\/TKDE.2017.2740932","volume":"29","author":"SK Jensen","year":"2017","unstructured":"Jensen, S.K., Pedersen, T.B., Thomsen, C.: Time series management systems: A survey. IEEE Transactions on Knowledge and Data Engineering 29(11), 2581\u20132600 (2017)","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"27_CR19","doi-asserted-by":"crossref","unstructured":"Kirchg\u00e4ssner, G., Wolters, J., Hassler, U.: Introduction to modern time series analysis. Springer Science & Business Media (2012)","DOI":"10.1007\/978-3-642-33436-8"},{"key":"27_CR20","doi-asserted-by":"crossref","unstructured":"Leis, V., Kemper, A., Neumann, T.: The adaptive radix tree: Artful indexing for main-memory databases. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE). pp. 38\u201349. IEEE (2013)","DOI":"10.1109\/ICDE.2013.6544812"},{"key":"27_CR21","doi-asserted-by":"crossref","unstructured":"Lemire, D., Kaser, O., Kurz, N., Deri, L., O\u2019Hara, C., Saint-Jacques, F., Ssi-Yan-Kai, G.: Roaring bitmaps: Implementation of an optimized software library. Software: Practice and Experience 48(4), 867\u2013895 (2018)","DOI":"10.1002\/spe.2560"},{"key":"27_CR22","doi-asserted-by":"crossref","unstructured":"Liu, X., Wei, Z., Yu, W., Liu, S., Wang, G., Liu, X., Li, Y.: Khronos: A real-time indexing framework for time series databases on large-scale performance monitoring systems. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. pp. 1607\u20131616 (2023)","DOI":"10.1145\/3583780.3614944"},{"issue":"2","key":"27_CR23","first-page":"269","volume":"23","author":"M Mohri","year":"1997","unstructured":"Mohri, M.: Finite-state transducers in language and speech processing. Computational linguistics 23(2), 269\u2013311 (1997)","journal-title":"Computational linguistics"},{"key":"27_CR24","doi-asserted-by":"crossref","unstructured":"Shi, X., Feng, Z., Li, K., Zhou, Y., Jin, H., Jiang, Y., He, B., Ling, Z., Li, X.: Byteseries: an in-memory time series database for large-scale monitoring systems. In: Proceedings of the 11th ACM Symposium on Cloud Computing. pp. 60\u201373 (2020)","DOI":"10.1145\/3419111.3421289"},{"issue":"12","key":"27_CR25","doi-asserted-by":"publisher","first-page":"2901","DOI":"10.14778\/3415478.3415504","volume":"13","author":"C Wang","year":"2020","unstructured":"Wang, C., Huang, X., Qiao, J., Jiang, T., Rui, L., Zhang, J., Kang, R., Feinauer, J., McGrail, K.A., Wang, P., et\u00a0al.: Apache iotdb: time-series database for internet of things. Proceedings of the VLDB Endowment 13(12), 2901\u20132904 (2020)","journal-title":"Proceedings of the VLDB Endowment"},{"key":"27_CR26","doi-asserted-by":"crossref","unstructured":"Wang, Z., Shao, Z.: Timeunion: An efficient architecture with unified data model for timeseries management systems on hybrid cloud storage. In: Proceedings of the 2022 International Conference on Management of Data. pp. 1418\u20131432 (2022)","DOI":"10.1145\/3514221.3526175"},{"key":"27_CR27","doi-asserted-by":"crossref","unstructured":"Wlodarczyk, T.W.: Overview of time series storage and processing in a cloud environment. In: 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings. pp. 625\u2013628. IEEE (2012)","DOI":"10.1109\/CloudCom.2012.6427510"},{"key":"27_CR28","doi-asserted-by":"publisher","first-page":"142295","DOI":"10.1109\/ACCESS.2019.2943876","volume":"7","author":"Y Yang","year":"2019","unstructured":"Yang, Y., Cao, Q., Jiang, H.: Edgedb: An efficient time-series database for edge computing. IEEE Access 7, 142295\u2013142307 (2019)","journal-title":"IEEE Access"},{"key":"27_CR29","unstructured":"Yoshinaga, N., Kitsuregawa, M.: A self-adaptive classifier for efficient text-stream processing. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers. pp. 1091\u20131102 (2014)"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5569-1_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T04:46:34Z","timestamp":1733978794000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5569-1_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819755684","9789819755691"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5569-1_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"13 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gifu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2024a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dasfaa2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}