即時串流資料品質綜效架構之研發：以工業蒸汽鍋爐感測設備為例

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：14

、訪客IP：3.15.221.67

姓名

邱奕華(Yi-Hua Chiu) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

即時串流資料品質綜效架構之研發：以工業蒸汽鍋爐感測設備為例
(A Study of Multi-dimensional Data Quality Framework for Online Data Stream: A Case of Industrial Steam Boiler Sensing Equipment)

相關論文

★ 專案管理的溝通關鍵路徑探討─以某企業軟體專案為例	★ 運用並探討會議流如何促進敏捷發展過程中團隊溝通與文件化：以T銀行系統開發為例
★ 專案化資訊服務中人力連續派遣決策模式之研究─以高鐵行控資訊設備維護為例	★ 以組織正義觀點介入案件指派決策之研究
★ 應用協調理論建立系統軟體測試中問題改善之協作流程	★ 應用案例式推理於問題管理系統之研究 -以筆記型電腦產品為例
★ 運用限制理論於多專案開發模式的人力資源配置之探討	★ 應用會議流方法於軟體專案開發之個案研究：以翰昇科技公司為例
★ 多重專案、多期再規劃的軟體開發接案決策模式：以南亞科技資訊部門為例	★ 會議導向敏捷軟體開發及系統設計：以大學畢業專題為例
★ 一種基於物件、屬性導向之變更影響分析方法於差異化產品設計	★ 會議流方法對大學畢業專題的團隊合作品質影響之實驗研究
★ 實施敏捷式發展法於大學部畢業專題之行動研究 – 以中央大學資管系為例	★ 建立一個用來評核自然語言需求品質的線上資訊系統
★ 結合本體論與模糊分析網路程序法於軟體測試之風險與風險關聯辨識	★ 在軟體反向工程中針對UML結構模型圖之線上品質評核系統

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

在現今資訊化的時代下，資料品質之良窳對於企業營運具有重大且關鍵的影響，在工業領域中，工業感測設備串流資料的品質所扮演的角色亦是如此。然而，現有品質控制之常見方式，主要著重於錯誤偵測與遺漏值偵測，即僅針對資料產出的結果做考慮，卻忽略了在即時串流資料上可能也會引發相異的品質問題。因此，本研究旨在以串流資料的性質以及工業感測設備應用的訴求之下，提出一全面性的即時串流資料品質綜效架構(a Multi-dimensional Data Quality framework for online Data Stream, 簡稱MDQDS)。同時，透過這一架構所建立的三個品質構面指標—準確性、完整性、一致性，以及其具體測試規則協助設備操作人員能即時地從不同面向來掌握設備資料的各種品質。在實務應用上，本研究依據所提出的架構，實作出一個應用程式MDQDSS(MDQDS System)以幫助設備操作人員能適時且有效的提升與維護工業感測設備資料之品質，並以工業蒸汽鍋爐感測設備的串流資料為例。並且，為了驗證本研究提出的架構與所實作出之系統的可行性，本研究亦將MDQDSS應用於紡織工業蒸汽鍋爐之感測設備資料中，來展示系統功能與使用情形最後，針對現有的運用與研究限制進行討論，並提出未來研究的可能方向。

摘要(英)

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.

關鍵字(中)

★ 資料品質
★ 串流資料
★ 資料窗格
★ 工業感測設備資料
★ 工業蒸汽鍋爐

關鍵字(英)

★ Data Quality
★ Data Stream
★ Data Window
★ Industrial Sensing Equipment Data
★ Industrial Steam Boilers

論文目次

摘要 i
Abstract ii
致謝 iii
圖目錄 vi
表目錄 vii

第一章緒論 1
1.1. 研究背景 1
1.2. 研究動機與問題 3
1.3. 研究目的與預期效益 4
1.4. 論文架構 5

第二章文獻探討 6
2.1. 工業感測設備的串流資料 7
2.1.1. 工業感測設備資料 7
2.1.2. 串流資料的定義 7
2.1.3. 串流資料的架構 8
2.1.4. 串流資料的運用與相關議題 9
2.2. 資料品質 11
2.2.1. 資料品質的定義 11
2.2.2. 資料品質的衡量方式 12
2.3. 工業感測設備串流資料之現有品質管理方式 15
2.3.1. 品質問題分類 15
2.3.2. 資料品質控制 16
2.3.3. 評論 19

第三章系統概念與設計 21
3.1. 系統架構 21
3.2. 架構之細部說明 23
3.2.1. 準確性(Accuracy) 23
3.2.2. 完整性(Completeness) 24
3.2.3. 一致性(Consistency) 25
3.3. 流程與功能架構 28
3.3.1. MDQDS：以系統功能觀點探討 28
3.3.2. MDQDS：以系統流程觀點 29
3.3.3. MDQDS：以物件與物件之互動關係 31

第四章系統實作與展示 33
4.1. 工業蒸汽鍋爐感測設備背景介紹 33
4.2. 系統開發架構與環境 35
4.3. 系統實作與說明 36
4.3.1. 初步設定 39
4.3.2. 系統執行 43

第五章系統驗證 51
5.1. 信賴度分析 51
5.2. 討論 54
5.3. 研究限制 56

第六章結論與未來展望 57
6.1. 研究結論與貢獻 57
6.2. 未來展望 57

參考文獻 59

參考文獻

李志杰. (2016). 鍋爐燃燒懸浮粒子排放減量與節能燃燒技術. 特種機械設備安全(44), 8.
Abadi, D. J., Carney, D., Cetintemel, U., Cherniack, M., Convey, C., Lee, S., . . . Zdonik, S. (2003). Aurora: a new model and architecture for data stream management. the VLDB Journal, 12(2), 120-139.
Alexandersson, H., & Moberg, A. (1997). Homogenization of Swedish temperature data. Part I: Homogeneity test for linear trends. International Journal of climatology, 17(1), 25-34.
Babcock, B., Babu, S., Datar, M., Motwani, R., & Widom, J. (2002). Models and issues in data stream systems. Paper presented at the Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems.
Ballou, D. P., & Pazer, H. L. (1985). Modeling data and process quality in multi-input, multi-output information systems. Management science, 31(2), 150-162.
Batini, C., Cappiello, C., Francalanci, C., & Maurino, A. (2009). Methodologies for data quality assessment and improvement. ACM computing surveys (CSUR), 41(3), 16.
Bonnet, P., Gehrke, J., & Seshadri, P. (2001). Towards sensor database systems. Paper presented at the International Conference on Mobile Data Management.
Carroll, O. (2017). Russian space programme close to collapse as latest failure exposes its fragility. The Independent. Retrieved from http://www.independent.co.uk/
Chen, C.-Y., Kuo, C.-Y., & Chen, P.-C. (2007). A Preliminary Study of Data Quality Measure with the Emphasis on Error Criticality. Paper presented at the IIE Annual Conference. Proceedings.
Chen, C.-Y., & Wolfe, P. (2005). An object-oriented quality framework and optimization models for comprehensively understanding and managing data quality in data warehouse applications. International Journal of Operations Research, 2(2), 1-8.
Fiebrich, C. A., Morgan, C. R., McCombs, A. G., Hall Jr, P. K., & McPherson, R. A. (2010). Quality assurance procedures for mesoscale meteorological data. Journal of Atmospheric and Oceanic Technology, 27(10), 1565-1582.
Geisler, S., Quix, C., Weber, S., & Jarke, M. (2016). Ontology-based data quality management for data streams. Journal of Data and Information Quality (JDIQ), 7(4), 18.
Geisler, S., Weber, S., & Quix, C. (2011). An ontology-based data quality framework for data stream applications. Paper presented at the 16th International Conference on Information Quality.
Gitzel, R. (2016). Data Quality in Time Series Data: An Experience Report. Paper presented at the CBI (Industrial Track).
Golab, L., & Ozsu, M. T. (2003). Issues in data stream management. ACM Sigmod Record, 32(2), 5-14.
Haque, A. (2017). Semi-supervised Adaptive Classification over Data Streams.
Hermann, M., Pentek, T., & Otto, B. (2016). Design principles for industrie 4.0 scenarios. Paper presented at the System Sciences (HICSS), 2016 49th Hawaii International Conference on.
Hubauer, T., Lamparter, S., Roshchin, M., Solomakhina, N., & Watson, S. (2013). Analysis of data quality issues in real-world industrial data. Paper presented at the Poster Presentation at the 2013 Annual Conference of the Prognostics and Health Management Society.
Jang, Y., Ishii, A. T., & Wang, R. Y. (1995). A qualitative approach to automatic data quality judgment. Journal of Organizational Computing and Electronic Commerce, 5(2), 101-121.
Janson, M. (1988). Data quality: the Achilles heel of end-user computing. Omega, 16(5), 491-502.
Jeffrey, S. J., Carter, J. O., Moodie, K. B., & Beswick, A. R. (2001). Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environmental Modelling & Software, 16(4), 309-330.
Jose Tari, J. (2005). Components of successful total quality management. The TQM magazine, 17(2), 182-194.
Judah, S., & Friedman, T. (2014). Magic quadrant for data quality tools. Gartner.
Kesh, S. (1995). Evaluating the quality of entity relationship models. Information and Software Technology, 37(12), 681-689.
Klein, A., & Lehner, W. (2009). Representing data quality in sensor data streaming environments. Journal of Data and Information Quality (JDIQ), 1(2), 10.
Lee, Y. W., Strong, D. M., Kahn, B. K., & Wang, R. Y. (2002). AIMQ: a methodology for information quality assessment. Information & Management, 40(2), 133-146.
Madnick, S. E., Wang, R. Y., Lee, Y. W., & Zhu, H. (2009). Overview and framework for data and information quality research. Journal of Data and Information Quality (JDIQ), 1(1), 2.
Martino, G. D., Fontana, N., Marini, G., & Singh, V. P. (2012). Variability and trend in seasonal precipitation in the continental United States. Journal of Hydrologic Engineering, 18(6), 630-640.
Meek, D., & Hatfield, J. (1994). Data quality checking for single station meteorological databases. Agricultural and Forest Meteorology, 69(1-2), 85-109.
Orr, K. (1998). Data quality and systems theory. Communications of the ACM, 41(2), 66-71.
Peck, E. L. (1997). Quality of hydrometeorological data in cold regions. JAWRA Journal of the American Water Resources Association, 33(1), 125-134.
Peterson, T. C., Easterling, D. R., Karl, T. R., Groisman, P., Nicholls, N., Plummer, N., . . . Gullett, D. (1998). Homogeneity adjustments of in situ atmospheric climate data: a review. International Journal of climatology, 18(13), 1493-1517.
Pingale, S. M., Khare, D., Jat, M. K., & Adamowski, J. (2014). Spatial and temporal trends of mean and extreme rainfall and temperature for the 33 urban centers of the arid and semi-arid state of Rajasthan, India. Atmospheric Research, 138, 73-90.
Raghunathan, S. (1999). Impact of information quality and decision-maker quality on decision quality: a theoretical model and simulation analysis. Decision Support Systems, 26(4), 275-286.
Redman, T. C., & Blanton, A. (1997). Data quality for the information age: Artech House, Inc.
Sargent, P. (1992). Data quality in materials information systems. Computer-Aided Design, 24(9), 477-490.
Shankar, K. G. (2008). Control of boiler operation using PLC–SCADA. Paper presented at the Proceedings of the International MultiConference of Engineers and Computer Scientists.
Sila, I., & Ebrahimpour, M. (2005). Critical linkages among TQM factors and business results. International journal of operations & production management, 25(11), 1123-1155.
Strong, D. M. (1997). IT process designs for improving information quality and reducing exception handling: A simulation experiment. Information & Management, 31(5), 251-263.
Tayi, G. K., & Ballou, D. P. (1998). Examining data quality. Communications of the ACM, 41(2), 54-57.
Thai Hoang, D., Igel, B., & Laosirihongthong, T. (2006). The impact of total quality management on innovation: Findings from a developing country. International Journal of Quality & Reliability Management, 23(9), 1092-1117.
Wand, Y., & Wang, R. Y. (1996). Anchoring data quality dimensions in ontological foundations. Commun. ACM, 39(11), 86-95. doi:10.1145/240455.240479
Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 12(4), 5-33.
Yeh, C.-F., Wang, J., Yeh, H.-F., & Lee, C.-H. (2015). Spatial and temporal streamflow trends in northern Taiwan. Water, 7(2), 634-651.
Zahumensky, I. (2004). Guidelines on quality control procedures for data from automatic weather stations. World Meteorological Organization, Switzerland.

指導教授

陳仲儼(C.Y. Chen)

審核日期

2018-7-26

推文