Research Papers

Apache IoTDB started at Tsinghua University, School of Software. IoTDB is a database for managing large amount of time series data with columnar storage, data encoding, pre-computation, and index techniques. It has SQL-like interface to write millions of data points per second per node and is optimized to get query results in few seconds over trillions of data points. It can also be easily integrated with Apache Hadoop MapReduce and Apache Spark for analytics.

The research papers related are in the following:

• PISA: An Index for Aggregating Big Time Series Data, Xiangdong Huang and Jianmin Wang and Raymond K. Wong and Jinrui Zhang and Chen Wang. CIKM 2016.
• Matching Consecutive Subpatterns over Streaming Time Series, 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, Jiaye Wu and Peng Wang and Chen Wang and Wei Wang and Jianmin Wang. ICDE 2019.
• The Design of Apache IoTDB distributed framework, 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, Jialin Qiao, Xiangdong Huang, Jianmin Wang, Raymond K Wong. IS 2020

Benchmark tools

We also developed Benchmark tools for time series databases

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