A Study of Multi-dimensional Data Quality Framework for Online Data Stream: A Case of Industrial Steam Boiler Sensing Equipment
| Autor: | Yi-Hua Chiu, 邱奕華 |
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| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 In this information era, the quality of data is significant to enterprises. In industry, the quality of streaming data of industrial Sensing Equipment also plays an important role in decision-making. However, the existing methods of streaming data quality control focus on error detection and missing values detection, which aim at neglect the quality problems arises from the data processes. Therefore, this research aims to propose a comprehensive framework of the quality of streaming data of industrial Sensing Equipment, termed Multi-dimensional Data Quality framework for online Data Stream (MDQDS). This framework is based on online state, especially focusing on the demand of the system characteristics of industrial sensing equipment and streaming data. Simultaneously, by the three quality dimensions—accuracy, completeness, and consistency—built by this framework, MDQDS can help observers from different views to handle a variety of online streaming data qualities. In the practical application, according to the proposed framework, this research implements an application, MDQDSS (MDQDS System), to help observers to increase and maintain the quality of streaming data of industrial sensing equipment. Moreover, to verify the feasibility of the proposed framework and the implemented system, this research applies MDQDS to the industrial steam boiler sensing equipment in H textile factory to demonstrate the proposed system and the test situation. Finally, discussion and suggestions are presented for the existing application and this research proposes the probable direction of the future work. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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