IoTDQ: An Industrial IoT Data Analysis Library for Apache IoTDB  

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

机构地区：[1]School of Software,Tsinghua University,Beijing 100084,China [2]National Engineering Research Center for Big Data Software(NERCBDS),Tsinghua University,Beijing 100084,China

出　　处：《Big Data Mining and Analytics》2024年第1期29-41,共13页大数据挖掘与分析（英文）

摘　　要：There is a growing demand for time series data analysis in industry areas.Apache loTDB is a time series database designed for the Internet of Things(loT)with enhanced storage and I/O performance.With User-Defined Functions(UDF)provided,computation for time series can be executed on Apache loTDB directly.To satisfy most of the common requirements in industrial time series analysis,we create a UDF library,loTDQ,on Apache loTDB.This library integrates stream computation functions on data quality analysis,data profiling,anomaly detection,data repairing,etc.loTDQ 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 loT time series with data quality problems.Experiments show that loTDQ keeps the same level of performance compared to mainstream alternatives,and shortens I/O consumption for Apache loTDB users.

关 键 词：industrial big data data quality data mining and analytics 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]