臺灣博碩士論文加值系統

資料倉儲是現今企業普遍採行的一個技術。企業從組織內外收集各式各樣的資料儲存在資料倉儲中，提供決策者在做決策時所需要的資料。然而由於資料來自於異質性的資料來源，資料不免雜亂，甚至會有錯誤的資料。為了確保資料倉儲的品質，在這些資料存入資料倉儲以前，必須要有一個適當的資料格式轉換及資料清理動作。本研究以高齡者生活品質智慧型維護系統為例，說明如何將資料格式作轉換及資料清理等動作。除了一般資料庫限制，本研究透過專家意見輔以邏輯法則限制來確保資料的正確性。本系統透過架設於高齡者周圍之非察覺式監測器，可以遠端監測高齡者之活動力及生活環境。高齡者親屬可以使用瀏覽器，經由網際網路查詢高齡者之日常生活品質；另一方面，醫師或分析師可以透過本系統之OLAP介面查詢高齡者異常的地方。本系統同時將這些資料存入資料倉儲中，再利關聯法則分析，找出各感應器之間的相關性。

Data warehouse is a sort of common technique that used by enterprises nowadays. Enterprises collect and save all kinds of data that from organizations into Data warehouse, and they provide necessary material to people who make decisions. Because of the heterogeneity, it is likely to be disordered and even has mistakes in it. There must be a suitable data format transformation and Data Clean process before we put the data in data warehouse for making sure the quality of it. This research gives Elder Intelligent Manatenance System for the quality of life as an example, and it states the process of data format transformation and data identified by the opinions of specialists and supported by the limitations of logical rules to ensure the accuracy of data. This system can monitor the activity and the surrounding environment of the elder remotely by setting imperceptible sensors nearby. Their relatives can use navigators to surf on the Internet to inquire the quality of daily life, and also doctors and analysts can seek abnormal situations of the elder with OLAP interface. At the same time, the system saves data in data warehouse and analyze with association rules, and then find out the relationship between sensors.

目錄
1. 緒論 1
1.1. 研究背景 1
1.2. 研究動機 2
1.3. 論文架構 3
2. 文獻探討 4
2.1. 資料萃取、轉換及載入 4
2.2. 資料清理 6
2.3. 資料倉儲與線上分析處理 7
2.4. 關聯法則 9
3. 系統模型與架構 11
3.1. 高齡者生活品質智慧型維護系統概述 11
3.2. 資料載入及綱要轉換 15
3.3. 資料清理 17
3.4. 資料倉儲中資料之應用 19
3.5. 研究限制 25
4. 系統展示 26
4.1. 前端網頁使用者介面 26
4.2. 後端知識資料倉儲 29
5. 結論與未來研究方向 35
5.1. 結論 35
5.2. 未來研究方向 35

參考文獻
1. Aluizio Haendchen Filho, Hércules A. Prado, Simão S. Toscani, Evolving a legacy data warehouse system to an Object-oriented architecture, Proceeding of XX International Conference of the Chilean on Computer Science Society, 2000, P 32-40
2. Ashok Sarasere, Edward Omiecinsky, and Shamkant Navathe. An efficient algorithm for mining association rules in large databases. In 21st Int'l Conf. on Very Large Databases (VLDB), Zurich, Switzerland, Sept. 1995
3. Atsushi Kawarada, Tesuyuki Takagi, Akira Tshukada, Kazuo Sasaki, asayuki Ishijima, Toshiyo Tamura, Tatsuo Togawa and Ken-ichi Yamakoshi. Evaluation of automated health monitoring system at the “welfare techno house”, Proceeding of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 20, No4, 1998
4. Celler, B.G., Earnshaw, W., Ilsar, E.D., Betbeder-Matibet, L., Harris, M., Clark, R., Hesketh, T., Lovell, N.H., 1995, "Remote monitoring of the elderly at home. A multidisciplinary project on aging at the University of New South Wales", Int. J. Biomed. Comp, Vol.40, 1995, 147-155
5. Charu Aggarwal and Phillip Yu. A new framework for itemset generation. In PODS 1998,
6. Erhard. Rahm and Hong-Hai. Do, Data Cleaning: Problems and Current Approaches, IEEE Bulletin of the Technical Committee on Data Engineering, Vol. 23 No. 4, December 2000.
7. Helena Galhardas, Daniela Florescu, Dennis Shasha, and Eric Simon. An extensible framework for data cleaning. In Proceedings of the International Conference on Data Engineering (ICDE), San Diego, CA, 2000
8. Jiawei Han, Yongjian Fu. Discovery of Multiple-level association rules from large databases, proceeding of the 21th International conference on very large databases, pases 420-431, 1995
9. Kimball, Ralph, The Data warehouse lifecycle toolkit, Wiley, 1998
10. Load, C.J., and Colvin D.P., Falls in the elderly: Detection and assessment, Proceedings of the annual conference on Engineering in Medicine and Biology, vol.13 n 4, p1938-1939
11. Masa Ishilima, Monitoring of Electrocardiograms in bed without utilizing body surface electrodes, IEEE transaction on biomedical engineering, vol. 40, no 6, Jun 1993
12. Rakesh Agrawal, Tomasz Imielinski, and Arun Swami. Mining association rules between sets of items in large databases. In Proc. of the ACM SIGMOD Conference on Management of Data, pages 207--216, Washington, D.C., May 1993.
13. Roberto J. Bayardo and Rakesh Agrawal. Mining the Most Interesting Rules. In Proc. of the 5th Int. Conf. on Knowledge Discovery and Data Mining, pages 145--154, San Diego, CA, USA, Aug. 1999.
14. Surajit Chaudhuri, Umeshwar Dayal, An overview of data warehousing and OLAP technology, SIGMOD Record 26(1): 65-74(1997)
15. Takehisa Yairi, Yoshikiyo Kato and Koichi Hori, Fault detection by mining association rules from house-keeping data. In Proc. of International Symposium on Arti cial Intelligence, Robotics and Automation in Space, 2001.
16. W.H. Inmon and R.D. Hackathorn. Using the Data Warehouse. New York, NY: John Wiley &; Sons, 1994.
17. 林玉子教授，日本國際醫療福祉大學醫療學科教授，東京都老人綜合研究所生活環境部問顧問，建築師雜誌，1998，7月。
18. 楊文昇，有效率的挖掘關聯法則之高頻物項集合演算法，逢甲大學資訊工程學系，民國89年
19. 民國91年至140年`台灣人口推計，http://www.cepd.gov.tw/ people/ index.htm，經建會人力資源規劃處。
20. 王恩琦，高齡者生活品質智慧型維護系統－資料載入與示程式之自動維護機制，元智大學資訊管理學系，2002
21. 老人生活狀況分析，內政部統計處，民國88年。
22. 老人福利需求模式之研究，內政部統計處，民國89年。
23. 臺閩地區老人福利機構需求概況調查，內政部統計處，民國88年
24. 行政院國家科學委員會專題研究計畫成果報告
25. 高齡者居家環境與健康狀況之遠端監測。http://desinger.mech.yzu. edu.tw