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{{short description|Unordered set of n-time-series possibly of different lengths}}
{{Cleanup|reason=This article conflates NoSQL solutions and TSDBs. Most of the DBs listed at the end are not TSDBs, e.g. Neo4j is a graph database, and actual TSDBs such as opentsdb are not listed. I recommend deletion or a complete rewrite of this article.|date=November 2012}}
{{More citations needed|date=December 2018}}
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).<ref name = "Various, UCR," >{{ Cite book | url = https://www.cs.ucr.edu/~eamonn/EM.pdf | access-date = 31 July 2019 | first1 = Abdullah | last1 = Mueen | first2 = Eamonn | last2 = Keogh | first3 = Qiang | last3 = Zhu | first4 = Sydney | last4 = Cash | first5 = Brandon | last5 = Westover | title = Proceedings of the 2009 SIAM International Conference on Data Mining | chapter = Exact Discovery of Time Series Motifs | year = 2009 | volume = 2009 | pages = 473–484 | doi = 10.1137/1.9781611972795.41 | quote = Definition 2:A Time Series Database(D)is an unordered set of m time series possibly of different lengths. | pmid = 31656693 | pmc = 6814436 | isbn = 978-0-89871-682-5 | archive-url = https://web.archive.org/web/20100625200233/https://www.cs.ucr.edu/~eamonn/EM.pdf | archive-date = 25 June 2010 | df = dmy-all }}</ref> In some fields, ''[[time series]]'' may be called profiles, curves, traces or trends.<ref>{{cite journal |doi=10.1016/j.energy.2017.07.008 |title=Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis |journal=Energy |volume=137 |pages=118–128 |year=2017 |last1=Villar-Rodriguez |first1=Esther |last2=Del Ser |first2=Javier |last3=Oregi |first3=Izaskun |last4=Bilbao |first4=Miren Nekane |last5=Gil-Lopez |first5=Sergio |bibcode=2017Ene...137..118V |hdl=20.500.11824/693 |hdl-access=free }}</ref> Several early time series databases are associated with industrial applications which could efficiently store measured values from sensory equipment (also referred to as [[data historian]]s), 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 algorithm]]s to manage the data efficiently.<ref name="Gorilla">{{cite journal |doi=10.14778/2824032.2824078 |title=Gorilla |journal=Proceedings of the VLDB Endowment |volume=8 |issue=12 |pages=1816–1827 |year=2015 |last1=Pelkonen |first1=Tuomas |last2=Franklin |first2=Scott |last3=Teller |first3=Justin |last4=Cavallaro |first4=Paul |last5=Huang |first5=Qi |last6=Meza |first6=Justin |last7=Veeraraghavan |first7=Kaushik }}</ref><ref name="Lockerman 2020">{{cite web | last=Lockerman | first=Joshua | title=Time-series compression algorithms, explained | website=Timescale Blog | date=2020-04-22 | url=https://www.timescale.com/blog/time-series-compression-algorithms-explained/ | access-date=2022-10-07}}</ref> 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 database]]s which reduce discrete relationships through referential models.<ref name = "Asay, TechRepublic, 2019" >{{ Cite web | url = https://www.techrepublic.com/article/why-time-series-databases-are-exploding-in-popularity/ | title = Why time series databases are exploding in popularity | access-date = 31 July 2019 | first = Matt | last = Asay | date = June 26, 2019 | website = [[TechRepublic]] | quote = 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. | archive-url = https://web.archive.org/web/20190626143018/https://www.techrepublic.com/article/why-time-series-databases-are-exploding-in-popularity/ | archive-date = 26 June 2019 | df = dmy-all }}</ref>


== Overview ==
A '''time series database (TSDB)''' is a software system that is optimized for handling [[time series|time series data]], arrays of numbers indexed by time (a datetime or a datetime range). In some fields these ''[[time series]]'' are called profiles, curves, or traces. A time series of stock prices might be called a price curve. A time series of energy consumption might be called a load profile. A log of temperature values over time might be called a temperature trace.
Time series datasets are relatively large and uniform compared to other datasets―usually being composed of a [[timestamp]] and associated data.<ref name="Wayner2021">{{cite news |last1=Wayner |first1=Peter |title=Database trends: The rise of the time-series database |url=https://venturebeat.com/2021/01/15/database-trends-the-rise-of-the-time-series-database/ |access-date=7 July 2021 |work=[[VentureBeat]] |date=15 January 2021}}</ref> Time series datasets can also have fewer relationships between data entries in different tables and don't require indefinite storage of entries.<ref name="Wayner2021"/> The unique properties of time series datasets mean that time series databases can provide significant improvements in storage space and performance over general purpose databases.<ref name="Wayner2021"/> For instance, due to the uniformity of time series data, specialized compression algorithms can provide improvements over regular compression algorithms designed to work on less uniform data.<ref name="Wayner2021"/> Time series databases can also be configured to regularly delete (or downsample) old data, unlike regular databases which are designed to store data indefinitely.<ref name="Wayner2021"/> Special [[database index|database indices]] can also provide boosts in query performance.<ref name="Wayner2021"/>


== List of time series databases ==
Despite the disparate names, many of the same mathematical operations, [[Query_language|queries]], or [[database transactions]] are useful for analysing all of them. The implementation of a database that can correctly, reliably, and efficiently implement these operations must be specialized for time-series data.
<!-- Entries in this list should have an article on English Wikipedia or at least an independent reliable source with non-trivial coverage. See https://en.wikipedia.org/enwiki/w/index.php?title=Talk:Time_series_database&oldid=997376318#RfC_on_inclusion_criteria for discussion (RfC). Items that do not fulfill these criteria will be removed. -->
The following database systems have functionality optimized for handling [[time series]] data.
{| class="wikitable sortable "
|+
|-
! Name !! License !! Language !! References
|-
|Amazon Timestream for LiveAnalytics
|Commercial
|[[Java (programming language)|Java]]
|<ref>{{Cite web|url=https://www.allthingsdistributed.com/2021/06/amazon-timestream-time-series-is-the-new-black.html|title=Amazon Timestream - Time series is the new black|date=June 2021 }}</ref>
|-
|[[Apache IoTDB]]
|[[Apache License 2.0]]
|[[Java (programming language)|Java]]
|<ref>{{Cite journal |last1=Wang |first1=Chen |last2=Huang |first2=Xiangdong |last3=Qiao |first3=Jialin |last4=Jiang |first4=Tian |last5=Rui |first5=Lei |last6=Zhang |first6=Jinrui |last7=Kang |first7=Rong |last8=Feinauer |first8=Julian |last9=McGrail |first9=Kevin A. |last10=Wang |first10=Peng |last11=Luo |first11=Diaohan |last12=Yuan |first12=Jun |last13=Wang |first13=Jianmin |last14=Sun |first14=Jiaguang |date=August 2020 |title=Apache IoTDB: time-series database for internet of things |url=https://dl.acm.org/doi/10.14778/3415478.3415504 |journal=Proceedings of the VLDB Endowment |language=en |volume=13 |issue=12 |pages=2901–2904 |doi=10.14778/3415478.3415504 |s2cid=221352039 |issn=2150-8097}}</ref>
|-
| [[Apache Kudu]] || [[Apache License 2.0]] || [[C++]] || <ref>{{Cite web|url=https://blog.cloudera.com/benchmarking-time-series-workloads-on-apache-kudu-using-tsbs/|title=Benchmarking Time Series workloads on Apache Kudu using TSBS|date=18 March 2020}}</ref>
|-
| [[Apache Pinot]]
| [[Apache License 2.0]]
| [[Java (programming language)|Java]]
| <ref>{{cite book |last1=Fu |first1=Yupeng |last2=Soman |first2=Chinmay |title=Proceedings of the 2021 International Conference on Management of Data |chapter=Real-time Data Infrastructure at Uber |date=9 June 2021 |pages=2503–2516 |doi=10.1145/3448016.3457552 |arxiv=2104.00087 |isbn=9781450383431 |s2cid=232478317 }}</ref>
|-
| [[ClickHouse]]
| [[Apache License 2.0]]
| [[C++]]
| <ref>{{Cite journal |last1=Schulze |first1=Robert |last2=Schreiber |first2=Tom |last3=Yatsishin |first3=Ilya |last4=Dahimene |first4=Ryadh |last5=Milovidov |first5=Alexey
|date=August 2024 |title=ClickHouse - Lightning Fast Analytics for Everyone |url=https://www.vldb.org/pvldb/vol17/p3731-schulze.pdf |journal=Proceedings of the VLDB Endowment |language=en |volume=17 |issue=12 |pages=3731–3744 |doi=10.14778/3685800.3685802}}</ref>
|-


TSDBs are databases that are optimized for [[time series]] data. Software with complex logic or business rules and high [[database transaction|transaction]] volume for time series data are not practical with the alternative to TSDBs, [[relational database management systems]]. [[Flat file database]]s are not a viable option either, if the data and transaction volume reaches a maximum threshold determined by the capacity of individual servers (processing power and storage capacity). Queries for historical data, replete with time ranges and roll ups and arbitrary time zone conversions are difficult in a relational database. Compositions of those rules are even more difficult. This is a problem compounded by the free nature of relational systems themselves. Many relational systems are often not modelled correctly with respect to time series data. TSBS on the other hand impose a model and this allows them to provide more features for doing so.


Ideally, these repositories are often natively implemented using specialized database algorithms. However, it is possible to store time series as [[binary large object]]s (BLOBs) in a ''[[relational database]]'' or by using a ''[[very large database|VLDB]]'' approach coupled with a pure ''[[star schema]]''. Efficiency is often improved if time is treated as a discrete quantity rather than as a continuous mathematical dimension. [[Database join]]s across multiple [[time series]] data sets is only practical when the time tag associated with each data entry spans the same set of discrete times for all data sets across which the [[database join|join]] is performed.<ref>http://www.julianbrowne.com/article/viewer/brewers-cap-theorem</ref>


| [[CrateDB]]
==Overview==
| [[Apache License 2.0]]
The TSDB allows users to create, enumerate, update and destroy various time series and organize them in some fashion. These series may be organized hierarchically and optionally have companion metadata available with them. The server often supports a number of basic calculations that work on a series as a whole, such as multiplying, adding, or otherwise combining various time series into a new time series. They can also filter on arbitrary patterns defined by the day of the week, low value filters, high value filters, or even have the values of one series filter another. Some TSDBs also build in a wealth of statistical functions.
| [[Java (programming language)|Java]]
| <ref>{{Cite web |title=DB-Engines Ranking |url=https://db-engines.com/en/ranking/time+series+dbms |access-date=2023-01-22 |website=DB-Engines |language=en}}</ref><ref>{{Cite web |title=Anforderungen für Zeitreihendatenbanken im industriellen IoT |url=https://www.springerprofessional.de/anforderungen-fuer-zeitreihendatenbanken-im-industriellen-iot/19119282 |access-date=2023-01-22 |website=springerprofessional.de |language=de}}</ref>
|-
|[[eXtremeDB]]
|Commercial
|SQL, [[Python (programming language)|Python]], [[C (programming language)|C]] / [[C++]], [[Java (programming language)|Java]], and [[C Sharp (programming language)|C#]]
|<ref name="redmonk">{{Cite web|url=https://redmonk.com/rstephens/2018/04/03/the-state-of-the-time-series-database-market/|title=State of the Time Series Database Market|last=Stephens|first=Rachel|access-date=2018-10-03|date=2018-04-03}}</ref>
|-
| [[InfluxDB]] || [[MIT License|MIT]].<ref name="MITgithub">{{Cite web|url=https://github.com/influxdata/influxdb/blob/master/LICENSE|title=influxdb license|website=GitHub|access-date=2016-08-14}}</ref> Chronograf [[Affero General Public License|AGPLv3]], Clustering Commercial<ref>{{Cite web|url=https://www.influxdata.com/influxdb-clustering/|title=influxdb clustering|last=|first=|date=|website=influxdata.com|access-date=2016-03-10}}</ref> || [[Go (programming language)|Go]] (version 2), [[Rust (programming language)|Rust]] (version 3)<ref>{{Cite web |first=Jessica |last=Wachtel |date=2023-07-06 |title=Meet the Founders Who Rewrote in Rust |url=https://www.influxdata.com/blog/meet-founders-who-rewrote-in-rust/ |access-date=2023-10-05 |website=InfluxData}}</ref>|| <ref name="redmonk"/><ref name="processing time series data">{{Cite web|url=https://www.zdnet.com/article/processing-time-series-data-what-are-the-options/|title=Processing time series data: What are the options?|last=Anadiotis|first=George|date=2018-09-28|website=[[ZDNet]]|access-date=2016-03-10}}</ref>
|-
| [[IBM Informix|Informix TimeSeries]] || Commercial || [[C (programming language)|C]] / [[C++]] || <ref name="redmonk"/><ref>{{cite book |last1=Dantale |first1=Viabhav |title=Solving Business Problems with Informix TimeSeries |publisher=IBM Redbooks |isbn=9780738437231 |url=http://www.redbooks.ibm.com/redbooks/pdfs/sg248021.pdf|date=2012-09-21 }}</ref>
|-
| [[Kdb+|Kx kdb+]] || Commercial || [[Q (programming language from Kx Systems)|Q]] || <ref name="redmonk"/>
|-
| [[MongoDB]] || [[Server Side Public License]] || [[C++]], [[JavaScript]], [[Python (programming language)|Python]] || <ref>{{Cite news|url=https://www.mongodb.com/developer/how-to/new-time-series-collections/|title=MongoDB's New Time Series Collections}}</ref>
|-
| [[Prometheus (software)|Prometheus]] || [[Apache License 2.0]] || [[Go (programming language)|Go]] || <ref name="redmonk"/>
|-
| [[Redis|RedisTimeSeries]] || RSALv2/[[Server Side Public License|SSPLv1]]<ref>{{Cite web |title=RedisTimeSeries/LICENSE.txt at master · RedisTimeSeries/RedisTimeSeries |url=https://github.com/RedisTimeSeries/RedisTimeSeries/blob/master/LICENSE.txt |access-date=2023-10-05 |website=GitHub |language=en}}</ref>|| [[C (programming language)|C]] || <ref>{{Cite web |title=RedisTimeSeries |author= |work=Redis |date= |access-date=12 June 2023 |url= https://redis.com/modules/redis-timeseries/}}</ref>
|-
| [[Riak]]-TS || [[Apache License 2.0]] || [[Erlang (programming language)|Erlang]] || <ref name="redmonk"/>
|-
| [[RRDtool]] || [[GPLv2]] || [[C (programming language)|C]] || <ref name="redmonk"/>
|-
| [[TimescaleDB]] || [[Apache License 2.0]] || [[C (programming language)|C]] || <ref>{{cite book |title=Design Recommendations for Intelligent Tutoring Systems: Volume 8 - Data Visualization |date=December 29, 2020 |publisher=Army Research Laboratory |isbn=9780997725780 |page=50 |url=https://books.google.com/books?id=TxY6EAAAQBAJ&dq=%22TimescaleDB%22+-wikipedia&pg=PA50}}</ref>
|-
| Whisper ([[Graphite (software)|Graphite]]) || [[Apache License 2.0]] || [[Python (programming language)|Python]] ||<ref name=Joshi>{{cite book |last1=Joshi |first1=Nishes |hdl=10852/9085 |title=Interoperability in monitoring and reporting systems |date=May 23, 2012 |type=Thesis }}</ref>
|}


== See also ==
For example, consider the following hypothetical "time series" or "profile" expression:
* [[Operational historian]]
<source lang=sql>
* [[Delta encoding]]
select nymex/gold_price * nymex/gold_volume
** [[Differential backup]]
</source>

To analyze this, the TSDB would join the two series nymex/gold_price and nymex/gold_volume based on the overlapping areas of time for each, multiply the values where they intersect, and then output a single composite time series.

Obviously, more complex expressions are allowed. TSDBs often allow users to manage a repository of filters or masks that specify in some way a pattern based on the day of a week and a set of holidays. In this way, one can readily assemble time series data. Assuming such a filter exists, one might hypothetically write
<source lang=sql>
select onpeak( cellphoneusage )
</source>

which would extract out the time series of cellphoneusage that only intersects that of 'onpeak'. Some systems might generalize the filter to be a time series itself.

This syntactical simplicity drives the appeal of the TSBS. For example, a simple utility bill might be implemented using a query such as:
<source lang=sql>
select max( onpeak( powerusagekw ) ) * demand_charge;

select sum( onpeak( powerusagekwh ) ) * energy_charge;
</source>

TSBS also generally have conversions to and from specific time zones implemented at the server level.

==Example==
A workable implementation of a time series database can be easily deployed in a conventional [[SQL]]-based relational database provided that the database software supports both [[binary large object]]s (BLOBs) and user-defined functions. SQL statements that operate on one or more time series quantities on the same row of a table or join can easily be written, as the user-defined time series functions operate comfortably inside of a [[Select (SQL)|SELECT]] statement. However, time series functionality such as a SUM function operating in the context of a [[Group by (SQL)|GROUP BY]] clause cannot be easily achieved.

Specialized database systems designed for TSDBs are often dubbed ''[[NoSQL]]'', because of their break from [[SQL]], the language of relational database management system queries. Some example database software packages optimized for dealing with large volumes of time series data include:

* 1248's [http://1248.io/geras.php Geras] [[Internet_of_Things]] TSDB
* [[Apache Accumulo]]
* [[Apache Cassandra]]
* [ http://kx.com/ KDB+ ]
* [[CouchDB]]
* [[Couchbase Server]]
* [[FAME (database)|FAME]]
* [[HBase]] (Hadoop)
* [http://influxdb.org/ InfluxDB]
* [[IBM Informix]]
* [https://code.google.com/p/kairosdb/ KairosDB]
* [http://www.opentsdb.net/ OpenTSDB]
* [http://www.sitewhere.org/ SiteWhere]
* [[Riak]]
* [[Redis]]
* [[tsdb]]<ref>[http://link.springer.com/chapter/10.1007%2F978-3-642-28534-9_16 tsdb: A Compressed Database for Time Series] Luca Deri, Simone Mainardi, Francesco Fusco: ''Proceedings of the 4th International Workshop on Traffic Monitoring and Analysis (TMA)'', Wien, 2012</ref>
* [http://tempo-db.com/ TempoDB]
* [http://www.treasure-data.com/ Treasure Data]

==See also==
*[[Operational Historian|Historian database]]
*[[NoSQL]]


== References ==
== References ==
{{Reflist}}
<references/>
[[Category:Time series software| ]]

{{DEFAULTSORT:Time Series Database}}
[[Category:Time series databases| ]]

Latest revision as of 14:49, 18 December 2024

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]

Overview

[edit]

Time series datasets are relatively large and uniform compared to other datasets―usually being composed of a timestamp and associated data.[6] Time series datasets can also have fewer relationships between data entries in different tables and don't require indefinite storage of entries.[6] The unique properties of time series datasets mean that time series databases can provide significant improvements in storage space and performance over general purpose databases.[6] For instance, due to the uniformity of time series data, specialized compression algorithms can provide improvements over regular compression algorithms designed to work on less uniform data.[6] Time series databases can also be configured to regularly delete (or downsample) old data, unlike regular databases which are designed to store data indefinitely.[6] Special database indices can also provide boosts in query performance.[6]

List of time series databases

[edit]

The following database systems have functionality optimized for handling time series data.

Name License Language References
Amazon Timestream for LiveAnalytics Commercial Java [7]
Apache IoTDB Apache License 2.0 Java [8]
Apache Kudu Apache License 2.0 C++ [9]
Apache Pinot Apache License 2.0 Java [10]
ClickHouse Apache License 2.0 C++ [11]
CrateDB Apache License 2.0 Java [12][13]
eXtremeDB Commercial SQL, Python, C / C++, Java, and C# [14]
InfluxDB MIT.[15] Chronograf AGPLv3, Clustering Commercial[16] Go (version 2), Rust (version 3)[17] [14][18]
Informix TimeSeries Commercial C / C++ [14][19]
Kx kdb+ Commercial Q [14]
MongoDB Server Side Public License C++, JavaScript, Python [20]
Prometheus Apache License 2.0 Go [14]
RedisTimeSeries RSALv2/SSPLv1[21] C [22]
Riak-TS Apache License 2.0 Erlang [14]
RRDtool GPLv2 C [14]
TimescaleDB Apache License 2.0 C [23]
Whisper (Graphite) Apache License 2.0 Python [24]

See also

[edit]

References

[edit]
  1. ^ Mueen, Abdullah; Keogh, Eamonn; Zhu, Qiang; Cash, Sydney; Westover, Brandon (2009). "Exact Discovery of Time Series Motifs". Proceedings of the 2009 SIAM International Conference on Data Mining (PDF). Vol. 2009. pp. 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. Bibcode:2017Ene...137..118V. 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.
  6. ^ a b c d e f Wayner, Peter (15 January 2021). "Database trends: The rise of the time-series database". VentureBeat. Retrieved 7 July 2021.
  7. ^ "Amazon Timestream - Time series is the new black". June 2021.
  8. ^ Wang, Chen; Huang, Xiangdong; Qiao, Jialin; Jiang, Tian; Rui, Lei; Zhang, Jinrui; Kang, Rong; Feinauer, Julian; McGrail, Kevin A.; Wang, Peng; Luo, Diaohan; Yuan, Jun; Wang, Jianmin; Sun, Jiaguang (August 2020). "Apache IoTDB: time-series database for internet of things". Proceedings of the VLDB Endowment. 13 (12): 2901–2904. doi:10.14778/3415478.3415504. ISSN 2150-8097. S2CID 221352039.
  9. ^ "Benchmarking Time Series workloads on Apache Kudu using TSBS". 18 March 2020.
  10. ^ Fu, Yupeng; Soman, Chinmay (9 June 2021). "Real-time Data Infrastructure at Uber". Proceedings of the 2021 International Conference on Management of Data. pp. 2503–2516. arXiv:2104.00087. doi:10.1145/3448016.3457552. ISBN 9781450383431. S2CID 232478317.
  11. ^ Schulze, Robert; Schreiber, Tom; Yatsishin, Ilya; Dahimene, Ryadh; Milovidov, Alexey (August 2024). "ClickHouse - Lightning Fast Analytics for Everyone" (PDF). Proceedings of the VLDB Endowment. 17 (12): 3731–3744. doi:10.14778/3685800.3685802.
  12. ^ "DB-Engines Ranking". DB-Engines. Retrieved 2023-01-22.
  13. ^ "Anforderungen für Zeitreihendatenbanken im industriellen IoT". springerprofessional.de (in German). Retrieved 2023-01-22.
  14. ^ a b c d e f g Stephens, Rachel (2018-04-03). "State of the Time Series Database Market". Retrieved 2018-10-03.
  15. ^ "influxdb license". GitHub. Retrieved 2016-08-14.
  16. ^ "influxdb clustering". influxdata.com. Retrieved 2016-03-10.
  17. ^ Wachtel, Jessica (2023-07-06). "Meet the Founders Who Rewrote in Rust". InfluxData. Retrieved 2023-10-05.
  18. ^ Anadiotis, George (2018-09-28). "Processing time series data: What are the options?". ZDNet. Retrieved 2016-03-10.
  19. ^ Dantale, Viabhav (2012-09-21). Solving Business Problems with Informix TimeSeries (PDF). IBM Redbooks. ISBN 9780738437231.
  20. ^ "MongoDB's New Time Series Collections".
  21. ^ "RedisTimeSeries/LICENSE.txt at master · RedisTimeSeries/RedisTimeSeries". GitHub. Retrieved 2023-10-05.
  22. ^ "RedisTimeSeries". Redis. Retrieved 12 June 2023.
  23. ^ Design Recommendations for Intelligent Tutoring Systems: Volume 8 - Data Visualization. Army Research Laboratory. December 29, 2020. p. 50. ISBN 9780997725780.
  24. ^ Joshi, Nishes (May 23, 2012). Interoperability in monitoring and reporting systems (Thesis). hdl:10852/9085.
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