Time series database

A time series database is a software system that is optimized for storing and serving time series through associated pairs of time(s) and value(s).[1] In some fields, time series may be called profiles, curves, traces or trends.[2] Several early time series databases are associated with industrial applications which could efficiently store measured values from sensory equipment (also referred to as data historians), but now are used in support of a much wider range of applications. In many cases, the repositories of time-series data will utilize compression algorithms to manage the data efficiently.[3][4] Although it is possible to store time-series data in many different database types, the design of these systems with time as a key index is distinctly different from relational databases which reduce discrete relationships through referential models.[5]

  1. ^ Mueen, Abdullah; Keogh, Eamonn; Zhu, Qiang; Cash, Sydney; Westover, Brandon (2009). "Exact Discovery of Time Series Motifs" (PDF). University of California, Riverside. 2009: 473–484. doi:10.1137/1.9781611972795.41. ISBN 978-0-89871-682-5. PMC 6814436. PMID 31656693. Archived from the original (PDF) on 25 June 2010. Retrieved 31 July 2019. Definition 2:A Time Series Database(D)is an unordered set of m time series possibly of different lengths.
  2. ^ Villar-Rodriguez, Esther; Del Ser, Javier; Oregi, Izaskun; Bilbao, Miren Nekane; Gil-Lopez, Sergio (2017). "Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis". Energy. 137: 118–128. doi:10.1016/j.energy.2017.07.008. hdl:20.500.11824/693.
  3. ^ Pelkonen, Tuomas; Franklin, Scott; Teller, Justin; Cavallaro, Paul; Huang, Qi; Meza, Justin; Veeraraghavan, Kaushik (2015). "Gorilla". Proceedings of the VLDB Endowment. 8 (12): 1816–1827. doi:10.14778/2824032.2824078.
  4. ^ Lockerman, Joshua (2020-04-22). "Time-series compression algorithms, explained". Timescale Blog. Retrieved 2022-10-07.
  5. ^ Asay, Matt (26 June 2019). "Why time series databases are exploding in popularity". TechRepublic. Archived from the original on 26 June 2019. Retrieved 31 July 2019. Relational databases and NoSQL databases can be used for time series data, but arguably developers will get better performance from purpose-built time series databases, rather than trying to apply a one-size-fits-all database to specific workloads.