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系統識別號 U0026-3008201109334000
論文名稱(中文) 利用語意軌跡探勘來預測未來行動位置
論文名稱(英文) Semantic Trajectories Mining for Location Prediction
校院名稱 成功大學
系所名稱(中) 資訊工程學系碩博士班
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 99
學期 2
出版年 100
研究生(中文) 翁梓喬
研究生(英文) Tz-Chiao Weng
學號 p76981104
學位類別 碩士
語文別 英文
論文頁數 80頁
口試委員 指導教授-曾新穆
召集委員-魏志平
口試委員-謝孫源
口試委員-高宏宇
中文關鍵字 位置預測  語意軌跡  適地性服務  資料探勘 
英文關鍵字 Location Prediction  Semantic Trajectory  Location Based Service  Data Mining 
學科別分類
中文摘要 最近幾年,預測行動裝置使用者的未來移動行為已經得到許多研究的注意。許多預測技術利用行動裝置使用者的移動軌跡,並萃取出其地理特徵來發展建構預測模型。在本研究中,我們發展出一個嶄新的方法來預測使用者的未來移動行為,不但運用了傳統中之地理特徵,而且我們另外考慮被隱藏在地理特徵中的資訊地理資訊以及語意資訊,該特徵我們稱之為語意軌跡。在我們的方法模型中主要的核心思想建立在一個嶄新的預測策略,其利用語意軌跡來分析出語意軌跡相似的使用者,並且利用其語意軌跡的相似度加以分群,對於不同語意軌跡的使用者群集我們各自建立了一個屬於該群集之預測模型以評估出使用者下一個地點位置的可能性。在實驗評估方面,我們採用了兩種資料集,一種是來自麻省理工大學( Massachusetts Institute of Technology )的真實資料;此外,我們另外設計了一個模擬資料模型,並且模擬出多種不同情況下可能的資料分布。 透過我們的實驗, 我們提出的位置預測方法展現出來比其他行動位置預測方法更好的實驗結果。
英文摘要 Researches on predicting movements of mobile users have attracted a lot of attentions in recent years. Many of those prediction techniques were developed based on geographic features of mobile users’ trajectories. In this study, we take into account the activities hinted out in mobile users’ trajectories to propose a novel approach for predicting the next location of a user’s movement based on both the geographic and semantic features of users’ trajectories. The core idea of our prediction model is based on a novel prediction strategy which evaluates the possibility of next location for a given user. For the experiments, we used two datasets, namely the MIT reality dataset and another simulation dataset we designed. Through the experimental evaluation, the proposed location prediction approach is shown to deliver excellent performance.
論文目次 中文摘要 i
Abstract ii
誌謝 iii
CONTENTS iv
LIST OF TABLES vi
LIST OF FIGURES vii
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Overview of our approach 4
1.4 Contribution 7
1.5 Thesis Organization 7
Chapter 2 Related Works 8
2.1 Person-pattern-based prediction 9
2.2 General-pattern-based prediction 9
2.3 WhereNext approach discussion 10
2.4 The semantic trajectory 10
2.5 Stay Location Extraction 12
Chapter 3 Proposed Methods 13
3.1 Framework 13
3.2 Data preprocessing 15
3.3 Geo-Pred 21
3.3.1 Geographic mining 21
3.3.2 Geographic Score Calculation 23
3.4 Semn-Pred 27
3.4.1 Semantic Mining 27
3.4.2 Geographic mining 33
3.4.3 Prediction Module 35
Chapter 4 Experimental Evaluation 41
4.1 MIT Reality Mining Dataset 41
4.2 Evaluation Methodology 43
4.3 Comparison of various prediction strategies 44
4.4 Simulation dataset 59
4.5 Discussions on Experimental Results 74
Chapter 5 Conclusions & Future Work 75
5.1 Conclusions 75
5.2 Future Work 76
REFERENCES 77
VITA 80
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