IoTDQ: An Industrial IoT Data Analysis Library for Apache IoTDB

Autor: Pengyu Chen, Wendi He, Wenxuan Ma, Xiangdong Huang, Chen Wang
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Big Data Mining and Analytics, Vol 7, Iss 1, Pp 29-41 (2024)
Druh dokumentu: article
ISSN: 2096-0654
DOI: 10.26599/BDMA.2023.9020010
Popis: There is a growing demand for time series data analysis in industry areas. Apache IoTDB is a time series database designed for the Internet of Things (IoT) with enhanced storage and I/O performance. With User-Defined Functions (UDF) provided, computation for time series can be executed on Apache IoTDB directly. To satisfy most of the common requirements in industrial time series analysis, we create a UDF library, IoTDQ, on Apache IoTDB. This library integrates stream computation functions on data quality analysis, data profiling, anomaly detection, data repairing, etc. IoTDQ enables users to conduct a wide range of analyses, such as monitoring, error diagnosis, equipment reliability analysis. It provides a framework for users to examine IoT time series with data quality problems. Experiments show that IoTDQ keeps the same level of performance compared to mainstream alternatives, and shortens I/O consumption for Apache IoTDB users.
Databáze: Directory of Open Access Journals