IoTDQ：适用于 Apache IoTDB 的工业物联网数据分析库

IF 7.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Big Data Mining and Analytics Pub Date : 2023-12-25 DOI:10.26599/BDMA.2023.9020010

Pengyu Chen;Wendi He;Wenxuan Ma;Xiangdong Huang;Chen Wang

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引用次数: 0

摘要

工业领域对时间序列数据分析的需求日益增长。Apache IoTDB 是专为物联网（IoT）设计的时间序列数据库，具有更强的存储和 I/O 性能。通过提供用户自定义函数（UDF），可以直接在 Apache IoTDB 上执行时间序列计算。为了满足工业时间序列分析中的大多数常见要求，我们在 Apache IoTDB 上创建了一个 UDF 库 IoTDQ。该库集成了数据质量分析、数据剖析、异常检测、数据修复等流计算功能。IoTDQ 使用户能够进行各种分析，如监控、错误诊断、设备可靠性分析等。它为用户检查存在数据质量问题的物联网时间序列提供了一个框架。实验表明，与主流替代方案相比，IoTDQ 保持了相同的性能水平，并缩短了 Apache IoTDB 用户的 I/O 消耗。

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IoTDQ: An Industrial IoT Data Analysis Library for Apache IoTDB

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.

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来源期刊

Big Data Mining and Analytics Computer Science-Computer Science Applications

CiteScore

20.90

自引率

2.20%

发文量

期刊介绍： Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge. Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications. Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more. With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.