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 IoT...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2024-03-01
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| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2023.9020010 |
| _version_ | 1797370354121834496 |
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| author | Pengyu Chen Wendi He Wenxuan Ma Xiangdong Huang Chen Wang |
| author_facet | Pengyu Chen Wendi He Wenxuan Ma Xiangdong Huang Chen Wang |
| author_sort | Pengyu Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T18:01:17Z |
| format | Article |
| id | doaj.art-6aa7d7abdd4c45f49ef93079c1fed53f |
| institution | Directory Open Access Journal |
| issn | 2096-0654 |
| language | English |
| last_indexed | 2024-03-08T18:01:17Z |
| publishDate | 2024-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj.art-6aa7d7abdd4c45f49ef93079c1fed53f2024-01-02T01:34:00ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-03-0171294110.26599/BDMA.2023.9020010IoTDQ: An Industrial IoT Data Analysis Library for Apache IoTDBPengyu Chen0Wendi He1Wenxuan Ma2Xiangdong Huang3Chen Wang4School of Software, Tsinghua University, Beijing 100084, ChinaSchool of Software, Tsinghua University, Beijing 100084, ChinaSchool of Software, Tsinghua University, Beijing 100084, ChinaSchool of Software, Tsinghua University, Beijing 100084, ChinaNational Engineering Research Center for Big Data Software (NERCBDS), Tsinghua University, Beijing 100084, ChinaThere 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.https://www.sciopen.com/article/10.26599/BDMA.2023.9020010industrial big datadata qualitydata mining and analytics |
| spellingShingle | Pengyu Chen Wendi He Wenxuan Ma Xiangdong Huang Chen Wang IoTDQ: An Industrial IoT Data Analysis Library for Apache IoTDB Big Data Mining and Analytics industrial big data data quality data mining and analytics |
| title | IoTDQ: An Industrial IoT Data Analysis Library for Apache IoTDB |
| title_full | IoTDQ: An Industrial IoT Data Analysis Library for Apache IoTDB |
| title_fullStr | IoTDQ: An Industrial IoT Data Analysis Library for Apache IoTDB |
| title_full_unstemmed | IoTDQ: An Industrial IoT Data Analysis Library for Apache IoTDB |
| title_short | IoTDQ: An Industrial IoT Data Analysis Library for Apache IoTDB |
| title_sort | iotdq an industrial iot data analysis library for apache iotdb |
| topic | industrial big data data quality data mining and analytics |
| url | https://www.sciopen.com/article/10.26599/BDMA.2023.9020010 |
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