臺灣博碩士論文加值系統

現在一般家庭的電腦愈來愈多，儲存的檔案也隨之增加，管理檔案已經是一件麻煩的事情。傳統的桌面搜尋軟體只針對單一電腦搜尋，很少針對多台電腦，如何有效管理多台電腦，將會是未來熱門的需求。而目前主流的桌面搜尋軟體，搜尋結果跟網頁搜尋比起來，效果並不好，主要的原因在於電腦上的檔案並不像網頁與網頁之間有連結，傳統的Page Rank方法並不適用於桌面搜尋，而且網頁搜尋引擎可以根據廣大使用者的Feedback重新排序搜尋結果。

本篇論文的方向著重在如何有效的搜尋多媒體檔案，以及同時搜尋多台電腦的檔案。大略的作法是，一開始將電腦檔案裡面相關的Metadata抽取出來並轉成Tag，如果多個檔案的Metadata內容相同，就關聯到同一個Tag。之後在搜尋時，將Query送到每一台電腦，每台電腦再搜尋相關的Tag，再從Tag搜尋檔案。最後系統收集到各台電腦的搜尋結果，再對結果進行最後的正規化、重新排序搜尋結果。本論文提出的方法對於多媒體檔案的搜尋有顯著的改善，而在分散式桌面搜尋也有初步成果，對於後續的研究有一定的參考價值

Many families have more and more computers now, storage of files have increased. The management of the files has become a trouble. The traditional desktop search software search only for a single computer and rarely for more than one computer. How to effectively manage more than one computer will be the next popular demand. The mainstream of desktop search software’s search results is not good than the web search results. The main reason is there are no links between computer files. Traditional method of PageRank can’t be directly applied to desktop search, and web search engines have feedback based on vast numbers of users to re-sort search results
This paper focuses on how to effectively search for files across multiple computers, and how to merge and re-rank search results from more than one computer. First, extract Metadata from computer files and convert into Tag. If many files have the same Metadata, then link to the same Tag. When searching, the system will send query to each computer, and each computer use Semantic search. Finally, the system collect result from each computer, and product final results with normalizing and re-sorting

中文摘要 i
英文摘要 ii
目錄 iii
圖目錄 v
第一章 緒論 1
1.1 桌面搜尋簡介 1
1.1.1 何謂桌面搜尋 1
1.1.2 目前主流的桌面搜尋軟體 2
1.1.3 桌面搜尋的困境 5
1.2 論文架構 6
第二章 相關文獻探討 7
2.1 PageRank 7
2.2 HITS algorithm 8
2.3 ObjectRank 11
2.4其它桌面搜尋相關論文探討 12
2.5分散式IR論文探討 15
第三章 議題探討 21
3.1現有桌面搜尋的問題 21
3.1.1 桌面搜尋的搜尋方式 21
3.1.2 分散式搜尋 21
3.2議題描述 22
第四章 方法討論 24
4.1 Overview 24
4.1.1 Metadata中繼資料 24
4.1.2 如何收集資訊 24
4.1.3 使用Tag記錄資訊 26
4.2系統架構 27
4.3前置作業 30
4.4搜尋方法 35
4.4.1 搜尋流程 35
4.4.2 Local tag-based ranking 37
4.4.3 Global ranking for distributed search 41
第五章 實驗討論 43
5.1環境建置 43
5.1.1 硬體設備與軟體環境 43
5.1.2 測試資料 43
5.2實驗方法 44
5.2.1 Query設計 44
5.2.2 Local Search 44
5.2.3 Global Search 45
5.3實驗結果討論 45
5.3.1 影片資料實驗結果 45
5.3.2 音樂資料實驗結果 49
5.3.3 分散式實驗 52
第六章 結論 56
參考文獻 57

[1] Google Desktop Search http://desktop.google.com/
[2] MSN Desktop Search http://www.microsoft.com/windows/products/winfamily/desktopsearch/default.mspx
[3] Beagle http://beagle-project.org/Main_Page
[4] Sergey Brin and Lawrence Page, "The anatomy of a large-scale hypertextual Web search engine", Proceedings of the Seventh International World Wide Web Conference, Volume 30, Issues 1-7, 1998, pp.107-117.
[5] Jon Kleinberg. “Authoritative Sources in a Hyperlinked Environment,” Journal of the ACM, Volume 46, Issue 5, 1999, pp. 604-632
[6] Andrey Balmin, Vagelis Hristidis, Yannis Papakonstantinou. “ObjectRank: Authority-Based Keyword Search in Databases”. In Very Large Data Base Conference, Canada, 2004
[7] Paul - Alexandru Chirita, Stefania Ghita, Wolfgang Nejdl, Raluca Paiu, “Semantically Enhanced Searching and Ranking on the Desktop”. ISWC 2005 SemanticDesktop Workshop, 2005, pp.1-15
[8] Paul Alexandru Chirita, Rita Gavriloaie, Stefania Ghita, Wolfgang Nejdl, Raluca Paiu. “Activity Based Metadata for Semantic Desktop Search”. Lecture Notes in Computer Science, Volume 3532, 2005, pp.439-454
[9] Tetsushi Morita, Tetsuo Hidaka, Tsuneko Kura, Keiichiro Ooura, Yasuhisa Kato. “Desktop search system based on the Action-Oriented algorism”. In Information and Telecommunication Technologies, 2005. 6th Asia-Pacific Symposium on.
[10] James P.Callan, Zhihong Lu, W.Bruce Croft, “Searching Distributed Collections With Inference Networks”, Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval, Washington, United States, 1995, pp.21-28
[11] Jamie Callan, “DISTRIBUTED INFORMATION RETRIEVAL”, Advances in Information Retrieval, 2006, pp.127-150
[12] Milad Shokouhi, Justin Zobel, "Robust Result Merging Using Sample-Based Score Estimates", ACM Transactions on Information Systems, Volume 27, Issue 3, 2009
[13] ID3 http://www.id3.org/
[14] YouTube http://www.youtube.com
[15] YouTube API http://code.google.com/intl/zh-TW/apis/youtube/overview.html
[16] IMDb http://www.imdb.com/
[17] freeDB http://www.freedb.org/