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| Title: | 從電子化政府建立政府統計知識挖掘系統模型架構之研究~以內政統計為例
Research into a System Framework for Knowledge Discovery in the Context of Statistics Tasks within e-Government – on Examples of Interior Statistic |
| Authors: | 江欣容
Chiang, Hsin Jung |
| Contributors: | 姜國輝
Johannes, Chiang
江欣容
Chiang, Hsin Jung |
| Keywords: | 電子化政府
資料挖掘
知識發現
知識管理
決策支援系統
高階主管系統
政府統計
Electronic Government
Data Mining
Knowledge Discovery
Knowledge Management
Decision Support System
Executive Information System
Government Statistics |
| Date: | 2003 |
| Issue Date: | 2009-09-14 09:09:10 (UTC+8) |
| Abstract: | 各國政府為提高國際競爭優勢，紛紛積極推動「電子化政府」。我國電子化政府建設自八十六年起開始推動，迄今已經行政院擴大為e-Taiwan計畫。電子化政府推動之業務電腦化，帶動政府業務資訊系統的快速發展，其彙集而成之大型資料庫，為政府統計工作帶來莫大的發展契機。
本研究從電子化政府的過程、內政業務行政程序、知識挖掘及採勘方法，提出參考資料模型，可能的統計軟體工具以及電子化政府中知識發現的實驗架構。再者，本研究藉臺閩地區外籍與大陸配偶結婚登記資料集，運用各種群集分析如K-means、ANN、TwoStep等，並利用我國人口數時間序列採用多模式方法進行人口預測，並將前述分析結果回饋資料庫，最後，作者實現一個知識發現系統雛型，其中包含了前端資料庫、資料集、知識庫以及EIS使用介面。
本研究成果總結如下：(1)資料挖掘工作產出之知識，除真實呈現社會現象外，亦作為政府政策之指南；(2)在本研究發展之系統中，新興資料挖掘技術及傳統資料分析方法，二者相輔相成；(3)某些資料挖掘技術適合相符的資料型態，例如文中人口預測資料較適合指數平滑法勝於ANN，亦即，我們可以籍由多模式分析比較其結果，來達到更佳的效果；(4)藉由知識庫模型的建立達成知識創造、共享與管理的目標；(5)資料挖掘工作可以回饋改善資訊系統或業務缺失。
In order to enhance international competitive advantages, most of the government authorities over the world are engaging in realizing their e-Government platforms. The ROC Government began to develope its e-Government- Infrastructure since 1997, and up-to-date is expanding the e-Taiwan Project as a whole by Executive Yuan. The computerization of administration processes within various government agencies push forward fast development of administration information systems via handling administrative works and lead to utmost opportunities for the government statistics by means of very large databases.
Starting from a survey on developements of e-Government, administrative processes for interior affairs, and knowledge mining as well as discovery techniques, this study brings out reference data models, potential statistical softwaretools, and an experimental framework as a whole for knowledge discovery in the context of e-Government. In the next step, this study experiments with applying clustering techniques such as K-means, ANN, and Twostep on datamart regarding marriage of foreigners ( including citizens from Mainland China ) in Taiwan, and with employeeing multi-modes approach on population forecasting. The results of aforementioned analysises are feed into backend database. At last, this author carries out a prototype of knowledge discovery system which includes front-end data base, data marts, knowledge base and interfaces to EIS.
The results of the research can be summarized as following: 1.Knowledge derived by means of data mining is capable to represent social events / affairs as well as to serve as a kind of guideline for developing government ploicies. 2. The modern data-ming techniques and classical data-analysis approaches complement with each other in the system developed in this research. 3. Certain mining technique is suitable of corresponding data pattern, for example, expotential smoothing is more suitable for our population data than ANN, which means that we may often achieve better result by multi-mode analysis and comprison with the outputs of different modes. 4. Knowledge creation, sharing, and management can be achieved by means of the knowledge discovery processes on the framework developed in this research. 5. We can figure out errorful raw data in the mining output and feedback to the data source to improve its quality. |
| Reference: | 中文部分
1.電子化政府推動方案（九十至九十三年度），行政院研究發展考核委員會，九十年四月
2.「電子化政府報告書(九十一年度)－電子化政府之挑戰與契機」,行政院研究發展考核委員會,民國九十二年
3.政府再造運動,行政院研究發展考核委員會主任委員暨筆小組召集人 魏啟林編撰,晨星出版
4.數位化政府,賴世培、項靖、宋餘俠、曾章瑞、馮震宇、吳秀光、詹中原,空中大學印行
5.「統計實務」,宋欽增、李慶泉,民國七十九年九月
6.戶政e網通計畫 內政部戶政司
7.地政e網通計畫 內政部地政司
8.全國建築管理建置計畫 內政部營建署
9.營建知識管理系統計畫 內政部營建署
10.防救災資訊系統 內政部消防署
11.資料採礦理論與實務 客戶關係管理的技巧與科學, Michael J. A. Berry, Gordon S. Linoff, 吳旭志、賴淑貞 譯
12.資料採礦 顅客關係管理暨電子行銷之應用, Michael J. A. Berry, Gordon S. Linoff, 彭文正 博士 譯
13.資料採礦之簡易系統-以流行病學為例 政治大學統計學研究所碩士論文,90年5月,羅家蓉 撰
14.KDD系統-以民國八十三年至八十八年之死亡資料庫為例 政治大學統計學研究所碩士論文,90年5月,陳怡伶 撰
15.資料探勘 Data Mining A Tutorial-Based Primer, Richard J. Roiger, Michael W. Geatz著, 曾新穆、李建億譯, 東華
16.Data Mining 概述 – 以Clementine 7.0 為例, 韋端,鄭宇庭,鄧家駒,匡宏波,謝邦昌編著
17.SQL 2000 Analysis Service資料採礦服務, 尹相志, 維科
18.中華民國臺灣地區民國91年至140年人口推計, 行政院經濟建設委員會
19.計量經濟與時間序列分析SAS/ETS之運用，周文賢
20.人口統計，范子華
21.時間數列分析的SPSS使用手冊，陳耀茂，鼎茂
22.類神經網路概論, 蔡瑞煌, 三民
23.決策支援系統, 梁定澎, 松崗
英文部分
1.Advances in Knowledge Discovery and Data Mining, Usama M. Fayyad,Gregory Piatetsky-Shapiro, Padhraic Smyth, and Ramasamy Uthurusamy, AAAI Press/THE MIT Press.
2.Data Warehousing, Data Mining, and OLAP, Alex Berson ,Stephen J. Smith
3.An Overview of Data Mining at Dun & Bradstreet, DIGWhite Paper 95/01, Data Intelligence Group Pilot Software, http://www.thearling.com/
4.A Comparison of Leading Data Mining Tools, John F. Elder IV & Dean W. Abbott Elder Research, 4th International Conference on Knowledge Discovery & Data Mining, 1998
5.An Overview of Data Mining Techniques, Alex Berson, Stephen Smith, and Kurt Thearling, http://www.thearling.com/text/dmtechniques/dmtechniques.htm
6.Data Mining Product Features, EXCLUSIVE ORE® INC., http://www.xore.com/prodtable.html
7.Management Knowledge Building Blocks for Success, Gilbert Probst Steffen Raub Kai Romhardt
8.How to Build a More Cost-Effective and Flexible Warehouse, R.A. Moeller |
| Description: | 碩士
國立政治大學
資訊管理研究所
89356026
92 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0089356026 |
| Data Type: | thesis |
| Appears in Collections: | [資訊管理學系] 學位論文
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