中文摘要
在無線網路中，由於資料的上傳頻寬有限，以致於資料廣播快速地成為大量資料傳輸的機制。目前資料的傳輸可分為兩種方式，一種稱為push-based，在這種方式下，資料是有週期性地在頻道中被廣播出來。另一種則是pull-based，在這種方式下，客戶端會將需求利用上傳頻道來通知伺服器，伺服器再將客戶端所需的資料播出，最後再由客戶端來接收。在push-based的方法中，以往的研究大多假設客戶端的需求每次都只需一個資料。然而在許多應用中，客戶端通常一次會要求多個資料。而在pull-based的方法中，伺服器會動態地決定目前所要被播出的資料，在以往的研究中大多假設資料長度都一樣，然而實際上資料的長度大多不一樣。在本論文中，我們分別提出Improved QDS Expansion Method (Improved-QEM)以及Heuristic On-line Algorithm來克服先前方法的缺點。當客戶端一次要求多個資料的情況下，並不能簡單地視為多個單一資料的要求的狀況。先前已經被提出的兩個方法Query Expansion Method (QEM)以及Modified Query Expansion Method (Modified-QEM)並不是提供最佳解的方法，我們提出一個方法(Improved-QEM)，乃整合了QEM以及Mining Association Rule。根據效能的分析結果，我們的Improved-QEM能夠產生更小TQD的排程。所謂的TQD指的是Total Query Distance，TQD可以正比於平均存取時間。線上演算法利用一些決策機制決定那個資料被播出。資料會根據時間的不同，而產生屬性上的變化，決策機制便會根據這些屬性來決定那個資料被播出。因此，如果資料的個數相當大時，如何有效率地決定出那個資料被播出，並且讓平均的存取時間維持很小，就顯得相當重要。Vaidya和Hameed提出兩個線上排程方法，一個是On-line Algorithm，另一個是On-line with Bucketing Algorithm。On-line Algorithm最主要的缺點則是執行的時間太長了，而On-line with Bucketing Algorithm最主要的缺點則是使得平均的存取時間加長了。因此，我們提出一個演算法(Heuristic On-line Algorithm)來克服之前兩個方法的缺點。根據效能的分析結果，我們的方法可以提供接近最佳平均存取時間的品質，並且不需很多計算時間。

ABSTRACT
Broadcasting data delivery is rapidly becoming the good choice for disseminating information to a massive user population in many new application areas where the client-to-server communication is limited. There are two different ways of data dissemination. One is called push-based that the data items are broadcasted periodically in the channels, another one is called pull-based that the client requests a piece of data on the uplink channel and the server responds by sending this piece of data to the client. In push-based, most of the previous researches assume that each mobile client needs only one data item. However, in many situations, a mobile client might need more than one data item. In pull-based, the data items were broadcasted dynamically. Most of the previous researches assume that the data items which requested by the clients are of the same size. However, the data items may of different sizes in reality. In this thesis, we propose Improved QDS Expansion Method (Improved-QEM) and Heuristic On-line Algorithm to overcome the above two weaknesses, respectively. The issue of scheduling the broadcast data for the situation that each client may access multiple data items can not be simply considered as multiple subissues. There have been two methods was proposed, Query Expansion Method (QEM) and Modified Query Expansion Methods (Modified-QEM). These two methods are heuristic-based algorithm and do not provide the optimal solution. To improve the performance, our Improved-QEM is an efficient scheduling for query-set-based broadcasting, which is integrated with Query Expansion Method (QEM) and mining association rules technique. The mining association rules can globally find the data item sets (large itemsets) which are requested by clients, frequently. From our simulation results, we show that, as compared to the local optimal approach in the previous methods, our Improved-QEM can construct the schedule with the smaller TQD than that constructed by QEM and Modified-QEM, where TQD is denotes Total Query Distance and is proportional to the average access time. The on-line (push-based) algorithms are easy to adapt to time varying demands for the data items, which uses some decision-making mechanism to determine which data item is to be broadcasted next. Hence, when the number of data items is huge, it is important to schedule broadcasting program such that, it can provide the small overall mean access time. Therefore, Vaidya and Hameed have proposed two on-line algorithms, On-line Algorithm and On-line with Bucketing Algorithm. The main disadvantage of On-line Algorithm is the heavy run-time overhead and the main disadvantage of On-line Algorithm with Bucketing is the poor performance of the overall mean access time. Therefore, we propose the heuristic on-line algorithm to solve these two problems. From our simulation results, we show that our heuristic algorithm provides the performance that closes to the overall mean access time and has with low run-time overhead.

TABLE OF CONTENTS
ABSTRACT
LIST OF FIGURES
LIST OF TABLES
1. Introduction
1.1 Mobile Environment
1.2 Data Broadcasting
1.3 Query-Set-Based Broadcast Scheduling
1.4 On-Line Scheduling Algorithm
1.5 Motivations
1.6 Organization of the Thesis
2. A Survey
2.1 Chung et al.'s QDS Expanding Method (QEM)
2.2 Lee et al.'s Modied Query Expansion Methods
2.2.1 Method 1
2.2.2 Method 2
2.3 Mining Association Rules | The Apriori Algorithm
2.4 Vaidya and Hameed's On-Line Algorithms of Data Broadcast Scheduling
2.4.1 On-line Scheduling Algorithm
2.4.2 On-line Scheduling Algorithm with Bucking
3. The Improved QDS Expanding Method
3.1 Assumptions
3.2 The Method
4. A Heuristic Approach to On-Line Data Broadcast Scheduling
4.1 Assumptions
4.2 The Heuristic On-line Algorithm
5. Performance Study
5.1 The Performance Model of the Improved QDS Expanding Method
5.2 Simulation Results of the Improved QDS Expanding Method
5.3 The Performance Model of the Heuristic On-line Algorithm
5.4 Simulation Results of the Heuristic On-line Algorithm
6. Conclusion
6.1 Summary
6.2 Future Research Directions

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