隨著全球網際網路(WWW)爆炸性的發展，許多使用者都曾有過資訊過載的經驗。因此，許多的搜尋引擎已被研發出來，來幫助使用者從大量的資料中搜尋有用的資訊。然而，使用者在不同的情況下，可能會有不同的資訊需求。相對於資訊檢索領域中，使用者「主動」地搜尋資料庫；在資訊過濾(IF)中，使用者是處於「被動」的狀態，等待所需的資料由伺服器透過廣播的媒體發送過來。因此，資料庫中所存放的，是記錄著使用者興趣與習慣之用戶資料。為了儲存更多的用戶資料，並以更簡便的方式過濾不相關的用戶，許多簽章式(signature-based)技術的方法已經被應用在IF的系統架構中。透過這些使用者的摘要，IF在進行過濾的過程便不需完整地比較資料庫中的每一筆檔案。然而，由於簽章只是資料的片段，要單純的利用這種技術來回答複雜的搜尋，是一項非常困難的挑戰。因此，如何萃取使用者的簽章，並有效地將這些簽章加以索引，在簽章式IF系統架構中，儼然是一項重要的議題。在簽章式IF系統中，通常必須處理兩種型態的查詢方式，分別是非精確過濾(inexact filtering)與近似度搜尋(similarity search)。在非精確過濾中，搜尋的主體是伺服器所接受到的新文件，而搜尋的對象，則是從資料庫中找尋出興趣選項完全包含於此文件的用戶資料。另一方面，在近似度搜尋中，搜尋的主體則是一個使用者，而搜尋的目標則是找出資料庫中與此使用者具有相似興趣的用戶資料。在這篇論文中，我們提出了一個名為「鑑識樹」(ID-tree)的索引方式來儲存用戶簽章。鑑識樹根據用戶資料間所有的相異項目，以一個二元樹來分割用戶資料為數個子資料群。基本上，我們所提出的鑑識樹方法，亦是「簽章樹」(signature tree)結構的一種。在一個鑑識樹中，每個用戶簽章是由根節點至葉節點之路徑所構成。由於每個用戶簽章都只被一個葉節點所指向，所有的用戶簽章都不會發生衝突(collision)。也就是說，不會有兩個相異的使用者資訊被擷取出相同的用戶簽章。更進一步地，鑑識樹僅需要同時檢查各資料子群集間相異的項目即可有效過濾不相關的資訊。因此，我們所提出的索引方法，可以比先前所有此領域的方法需要更少的用戶資料存取量來回答非精確過濾與近似度搜尋。此外，在建造簽章索引的過程中，我們可以更短的時間來批次地處理大量的用戶資料。根據模擬實驗的結果，在非精確過濾搜尋中，相較於Chen的簽章樹；以及在近似度搜尋中，相較於Aggarwal等人的「簽章表」(SG-table)方法，我們的方法確實可以有效減少搜尋所需存取的用戶數量。

With the booming development of WWW, many search engines have been developed to help users to find useful information from a great quantity of data. However, users may have different needs in different situations. Opposite to the Information Retrieval where users retrieve data actively, Information Filtering (IF) sends information from servers to passive users through broadcast mediums, rather than being searched by them. Therefore, each user has his (or her) profile stored in the database, where a profile records a set of interest items that can present his (or her) interests or habits. To efficiently store many user profiles in servers and filter irrelevant users, many signature-based index techniques are applied in IF systems. By using signatures, IF does not need to compare each item of profiles to filter out irrelevant ones. However, because signatures are incomplete information of profiles, it is very hard to answer the complex queries by using only the signatures. Therefore, a critical issue of the signature-based IF service is how to index the signatures of user profiles for an efficient filtering process. There are often two types of queries in the signature-based IF systems, the inexact filtering and the similarity search queries. In the inexact filtering, a query is an incoming document and it needs to find the profiles whose interest items are all included in the query. On the other hand, in the similarity search, a query is a user profile and it needs to find the users whose interest items are similar to the query user. In this thesis, we propose an ID-tree index strategy, which indexes signatures of user profiles by partitioning them into subgroups using a binary tree structure according to all of the different items among them. Basically, our ID-tree index strategy is a kind of the signature tree. In an ID-tree, each path from the root to a leaf node is the signature of the profile pointed by the leaf node. Because each profile is pointed only by one leaf node of the ID-tree, there will be no collision in the structure. In other words, there will be no two profiles assigned to the same signature. Moreover, only the different items among subgroups of profiles will be checked at one time to filter out irrelevant profiles for queries. Therefore, our strategy can answer the inexact filtering and the similarity search queries with less number of accessed profiles as compared to the previous strategies. Moreover, to build the index of signatures, it needs less time to batch a great deal of database profiles. From our simulation results, we show that our strategy can access less number of profiles to answer the queries than Chen's signature tree strategy for the inexact filtering and Aggarwal et al.'s SG-table strategy for the similarity search.

ABSTRACT . . . . . i
LIST OF FIGURES . . . . . . iv
LIST OF TABLES . . . . . viii
1. Introduction . . . . . 1
1.1 Information Filtering . . . . . 1
1.1.1 Content-Based Information Filtering . . . . . 2
1.1.2 Collaborative Information Filtering . . . . . 3
1.2 Signatures . . . . . 5
1.3 Strategies of Information Filtering Based on Signatures . . . . . 5
1.3.1 Inexact Filtering Strategies . . . . . 6
1.3.2 Similarity Search Strategies . . . . . 8
1.4 Motivation . . . . . 11
1.5 Organization of Thesis . . . . . 16
2. A Survey . . . . . 18
2.1 Inexact Filtering Index Strategies . . . . . 18
2.1.1 The Signature Files . . . . . 19
2.1.2 The Bit-Slice Files . . . . . 20
2.1.3 The S-Tree . . . . . 21
2.1.4 Signature Trees . . . . . 23
2.2 Similarity Search Index Strategies . . . . . 24
2.2.1 The Signature Table . . . . . 25
2.2.2 The S 3 B: Signature-based Similarity Search for Basket-data . . . . . 27
3. The ID Tree Index Strategy . . . . . 29
3.1 The ID Tree Structure . . . . . 29
3.2 Construction of the ID Tree . . . . . 35
3.2.1 The Preprocessing Step . . . . . 35
3.2.2 The Extension Step . . . . . 41
3.3 Searching in the ID Tree . . . . . 44
3.3.1 Inexact Filtering in the ID Tree . . . . . 46
3.3.2 The Minimum Optimistic Bound . . . . . 51
3.3.3 Similarity Search in the ID Tree . . . . . . 58
4. Performance . . . . . 66
4.1 The Simulation Model . . . . . 66
4.2 Simulation Results of Inexact Filtering Strategies . . . . . 69
4.3 Simulation Results of Similarity Search Strategies . . . . . 79
5. Conclusion . . . . . 90
5.1 Summary . . . . . 90
5.2 Future Research Directions . . . . . 92
BIBLIOGRAPHY . . . . . 93

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