IoTDQ: An Industrial IoT Data Analysis Library for Apache IoTDB

作     者：Pengyu Chen Wendi He Wenxuan Ma Xiangdong Huang Chen Wang 

作者机构：School of SoftwareTsinghua UniversityBeijing 100084China National Engineering Research Center for Big Data Software(NERCBDS)Tsinghua UniversityBeijing 100084China 

出 版 物：《Big Data Mining and Analytics》 (大数据挖掘与分析（英文）)

年 卷 期：2024年第7卷第1期

页      面：29-41页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：industrial big data data quality data mining and analytics 

摘      要：There is a growing demand for time series data analysis in industry *** loTDB is a time series database designed for the Internet of Things(loT)with enhanced storage and I/O *** User-Defined Functions(UDF)provided,computation for time series can be executed on Apache loTDB *** satisfy most of the common requirements in industrial time series analysis,we create a UDF library,loTDQ,on Apache *** library integrates stream computation functions on data quality analysis,data profiling,anomaly detection,data repairing,*** enables users to conduct a wide range of analyses,such as monitoring,error diagnosis,equipment reliability *** provides a framework for users to examine loT time series with data quality *** show that loTDQ keeps the same level of performance compared to mainstream alternatives,and shortens I/O consumption for Apache loTDB users.