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# 研究论文

Apache IoTDB 始于清华大学软件学院。IoTDB是一个用于管理大量时间序列数据的数据库，它采用了列式存储、数据编码、预计算和索引技术，具有类SQL的接口，可支持每秒每节点写入数百万数据点，可以秒级获得超过数万亿个数据点的查询结果。它还可以很容易地与 ApacheHadoop、MapReduce 和 ApacheSpark 集成以进行分析。

相关研究论文如下：

* [PISA: An Index for Aggregating Big Time Series Data](https://dl.acm.org/citation.cfm?id=2983775&dl=ACM&coll=DL), Xiangdong Huang and Jianmin Wang and Raymond K. Wong and Jinrui Zhang and Chen Wang. CIKM 2016.
* [Matching Consecutive Subpatterns over Streaming Time Series](https://link.springer.com/chapter/10.1007/978-3-319-96893-3_8), Rong Kang and Chen Wang and Peng Wang and Yuting Ding and Jianmin Wang. APWeb/WAIM 2018.
* [KV-match: A Subsequence Matching Approach Supporting Normalization and Time Warping](https://www.semanticscholar.org/paper/KV-match%3A-A-Subsequence-Matching-Approach-and-Time-Wu-Wang/9ed84cb15b7e5052028fc5b4d667248713ac8592), Jiaye Wu and Peng Wang and Chen Wang and Wei Wang and Jianmin Wang. ICDE 2019.
* [The Design of Apache IoTDB distributed framework](http://ndbc2019.sdu.edu.cn/info/1002/1044.htm), Tianan Li, Jianmin Wang, Xiangdong Huang, Yi Xu, Dongfang Mao, Jun Yuan. NDBC 2019
* [Dual-PISA: An index for aggregation operations on time series data](https://www.sciencedirect.com/science/article/pii/S0306437918305489), Jialin Qiao, Xiangdong Huang, Jianmin Wang, Raymond K Wong. IS 2020

# Benchmark工具

我们还研发了面向时间序列数据库的Benchmark工具： 

https://github.com/thulab/iotdb-benchmark