臺灣博碩士論文加值系統

無線網路與行動計算的技術隨著時代的發展與進步，所能提供的服務也越來越多樣化多元化，而此發展的真正目的在於提供現代人能於任何時間與任何地點取得所需要的服務與資訊，因此，行動資料的管理與資源的分配便成為一項相當熱門的研究領域。為此本文提出一個新“使用者活動”概念，用以詮釋複雜多變的使用者移動與需求行為之間的關聯性。每個獨立活動所代表行為型態可能不盡相同，可只具有移動關聯性規則的模式(movement pattern)、需求服務關聯性規則的模式(service pattern)，或是同時具備位置移動與需求服務相依關聯的模式(hybrid pattern)。不同型態的活動彼此之間也允許互相組合成為一個更大的活動，所以活動可以提供更精確、豐富與完整的使用者行為模式定義。

針對活動概念我們提出一套使用者活動探勘（activity mining）的方法，並加入對活動資料的維護、偵測與管理策略，使其適用於高度動態的行動計算環境中。進一步將活動概念導入行動資料管理之預取(prefetching)與推播(pushing)技術中，分別對基地台與使用者，提供具備成本控制機制的動態行動資料管理策略。最後經過深入的模擬實驗得以驗証，以使用者活動為概念的行動資料管理策略，的確可藉由提供更豐富、準確且即時的資訊，有效提昇本地端快取利用率(local availability)、降低成本花費(cost)與減少系統回應時間(response time)。

With the popularity of mobile computing, mobile data management is becoming the one of the important research topic. We propose a new idea of user activity for characterizing complex and changing behavior patterns of mobile users. The type of each individual activity may not be the same. It can be composed of movement pattern, service pattern, or the hybrid pattern with both location movement and service requests. Distinctive types of activities can be combined into a larger activity. Hence, the activity concept provides a more precise, rich, and detail description of user behavioral patterns in mobile environments.
The activity concept calls for new methods for activity processing including activity mining, incremental maintenance, online detection and data management. With the knowledge of user activities, prefetching and pushing techniques can be employed to facilitate predictive data management in mobile environments. We propose dynamic data management strategies with cost sensitive control mechanism for base stations and mobile users.
We compute the execution cost, response time and local availability through simulation to evaluate the performance of our approach. By analyzing the results of evaluation, we get the most suitable parameter setup for each of the strategy. Simulation results demonstrate that the activity based dynamic data management strategies can significantly reduce execution cost, response time and increase local cache availability.

致謝 I
摘要 II
ABSTRACT III
目錄 IV
圖目錄 VI
表目錄 IX
第一章 緒論 1
1.1 研究動機與背景 1
1.2 研究目標與方法 3
1.3 研究成果 4
1.4 論文架構 7
第二章 相關研究 8
2.1 行動計算環境 8
2.2 行動資料管理 12
2.2.1靜態資料管理 13
2.2.2動態資料管理 14
2.2.3使用者行為探勘 15
2.3關聯式規則探勘 16
第三章 使用者行為與活動探勘 20
3.1 使用者行為與活動 20
3.1.1 行為 20
3.1.2 活動 21
3.2活動(ACTIVITY) 定義 22
3.3 活動探勘(ACTIVITY MINING) 26
3.4 活動(ACTIVITY) 的管理與維護 34
3.4.1建立活動樹(Activity Tree) 35
3.4.2活動樹(Activity Tree)的維護 37
3.4.3活動分享與索引(Activity Sharing and Index) 40
第四章 以活動為基礎之動態資料管理策略 43
4.1 設計考量與系統特色 43
4.2 系統架構與方法概述 45
4.3 使用者活動偵測(ACTIVITY DETECTION) 47
4.4預取資料權重與排比 52
4.5 活動為基礎之預取(PREFETCHING)與主動式推播(PROACTIVE PUSHING)策略 58
4.6預取與推播之成本控制策略 60
4.7系統運行與整合 62
第五章 系統模擬 65
5.1 實作環境介紹 65
5.2 模擬系統架構 66
5.2.1 行為產生器與環境佈建 67
5.2.2系統模擬流程 70
5.2.3模擬工具介紹 73
第六章 系效能評估 78
6.1 行動資料管理效能評估 79
6.1.1 以改變活動型態比例為變因 81
6.1.2 以改變基地台臺快取大小為變因 83
6.1.3 以改變服務更新頻率為變因 85
6.1.4 以改變行為遵從率為變因 87
6.1.5 以改變行為機率分布(Zipf )為變因 89
6.1.6 以改變活動探勘支持度(Support)為變因 91
6.1.7以改變行為存活時間(Life Time)為變因 93
6.2　綜合分析評估 95
6.2.1 以改變活動型態比例為變因之三維分析 96
6.2.2 以改變快取大小與服務更新率為變因之三維分析 99
6.2.3 以改變行為遵率與快取大小為變因之三維分析 101
6.2.4 以改變行為遵從率與服務更新率為變因之三維分析 103
6.2.5 以探勘支持度與行為Zipf分布為變因之三維分析 104
6.2.6 以行為機率分佈與行為遵從率為變因之三維分析 106
6.3　評估結語 108
第七章 結論與未來方向 109
7.1 結論 109
7.2 末來方向與展望 110
參考文獻 113

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