隨著無線通訊技術、智慧型手持式裝置、定位系統以及網路技術的快速發展，基於位置的服務與應用像是移動物體追蹤、行為探索與預測以及導航路徑規劃，已經成為熱門的研究議題並且吸引許多研究學者的關注。因此，如何萃取、了解、分析以及利用從大量行動資料中所獲得的移動行為知識，已經成為一個具有吸引力與挑戰性的議題。本研究發展多個實用且有效率的資料探勘技術，從大量的行動資料中，挖掘有價值的知識，並且利用這些知識實現高品質的移動行為追蹤、探索、預測以及推薦。
近年來，在感測網路中，許多與節能相關的移動物體追蹤研究被提出。多數學者嘗試從通訊耗能的最佳化和偵測模式的排程上著手以節省感測網路的電力消耗。這些研究認為物體的移動軌跡是毫無規律，然而在一些研究中發現，物體的移動通常會遵循其長久的習慣，而不是完全的毫無規律。因此，挖掘物體的移動習慣將會有助於移動物體的追蹤。為了節省電力消耗，在感測範圍無物體時，感測器將會進入休眠模式，可能會因此遺失物體的正確位置，因此，有效率地將遺失物體重新定位是一個重要的議題。基於以上的問題，本研究提出一個創新的移動物體追蹤技術，透過分析物體的移動紀錄，建構一個節能且即時的移動物體追蹤感測網路模型。經由各種不同的參數實驗評估，此物體追蹤技術在網路節能與即時追蹤上展現優異的效果。
在移動行為探索的領域中，其中一個備受重視的議題為使用者行動商務之行為探索與預測。大多數的研究基於所有的行動商務紀錄，從中挖掘出行動商務樣式，並且利用這些樣式進行預測。然而這種模型的預測效果可能不夠準確，原因在於不同的使用族群在不同的時間區段可能會有不同的行動商務樣式，因此使用者與時間之間的關係是需要被考慮。有鑑於此，本研究提出一種創新的技術應用於行動商務之探索與預測，透過行動商務資料分析使用族群與時間區段，有效地挖掘使用者的行動樣式與商務行為並且準確地預測使用者接下來所需要的行動商務。然而當沒有足夠的知識時，此機制將無從預測，在實際應用上，產生嚴重的缺陷。為了解決這個問題，本技術持續延伸且改進，透過行動商務的相似度推論，使本技術能夠無時無刻執行預測的功能。經過實驗分析，新技術在預測準確性與應用性上都呈現出色的成果。
在全球衛星定位系統蓬勃發展之後，導航路徑規劃已經成為日常生活中一種不可或缺的需求。由於互動網路技術的發展，使用者越來越樂意彼此分享地圖日記。對於智慧型運輸系統應用，這樣的軌跡資料是非常有價值。現今的導航系統通常只能提供一個目的地之路徑規劃服務，然而日常生活中，多個目的地的路徑規劃安排會是一個被期待且吸引人的服務。為了實現這種需求，本研究發展一套創新的導航路徑規劃技術，提供多個目的地的路徑規劃功能，透過分析使用者的移動軌跡，挖掘與預測交通狀況的趨勢，使規劃出來的路徑能更為順暢。最後利用高雄市地圖與真實交通資料進行實驗評估，此路徑規劃技術不論在單點或多點規劃上都展現優異的規劃效果。

With the advance in wireless communication technologies, intelligent portable devices, location-acquisition availabilities and Web 2.0 technologies, Location-Based Services (LBSs) and location-aware applications have become the emerging research fields that attract a lot of attentions such as moving object tracking, mobile behavior exploration with prediction, navigation path planning, etc. Hence, how to extract, understand, analyze and utilize the mobile behavior knowledge from such massive mobility data resources has been becoming an attractive and challenging issue over the past few years. In this dissertation, we develop several efficient and effective data mining frameworks for discovering the valuable knowledge from a huge collection of mobility data to achieve the high quality of mobile behavior tracking, exploration, prediction and recommendation.
Nowadays, a number of studies have been proposed on the field of energy-efficient tracking of moving objects in Object Tracking Sensor Networks (OTSNs). Many researchers tried to save energy consumption by optimizing the communication cost and scheduling the sensing mode. Most of the existing studies consider the movement behavior of moving object is random, however, the object movement behavior is often based on certain underlying events instead of randomness completely in some applications. Hence, the movement behavior discovery may benefits for the moving objects tracing. Furthermore, to save the energy consumption, the basic idea is to inactivate a sensor node whenever there is no object located in its sensing region. However, OTSNs might lose track of objects by such strategy, it is important to recapture missing objects in the real-time manner. For the above issue, we propose a novel framework named Multi-Level Object Tracker (MLOT) for energy-efficient and real-time tracking of the moving objects in sensor networks by mining the movement log. Through experimental evaluations of various simulated conditions, the proposed framework is shown to deliver excellent performance in terms of both energy efficiency and timeliness.
For mobile behavior explorations, one of the active topics is the mining and prediction of users’ mobile commerce behaviors. Most of existing studies focus on discovering mobile patterns from the whole logs and predicting the next mobile behaviors based on the discovered patterns. However, this kind of prediction model may not be precise enough since the differentiated mobile behaviors among user groups and temporal periods are not considered. Hence, we propose a novel framework named Mobile Commerce Explorer (MCE) to mine and predict mobile users’ movements and transactions under the context of mobile commerce. Although the experimental evaluation is shown that MCE delivers excellent precision, the predictions will fail if there is no existing pattern to match. To deal with this problem, we integrate MCE and similarity inference model to mine and predict mobile users’ movements and transactions. We perform an extensive evaluation by simulation and show that MCE produces excellent results in terms of precision and recall.
With the development of the Global Positioning System (GPS), navigation path planning has become one of important research issues recently. Moreover, due to the advances of the Web 2.0 technology, many GPS device users are willing to share their trajectories with others, thus providing a very good resource for Intelligent Transportation System (ITS) applications. Recent navigation systems focus on planning the navigation path with only one destination. In our daily life, planning of the navigation path with multiple destinations is a desirable function. To achieve such requirement, we develop a new system framework called Trajectory-based Path Finder (TPF) for location based services that find the fastest navigation path with multiple destinations. Through a real-time traffic information data in Kaohsiung city and a comprehensive set of experiments, we evaluate the proposed techniques employed in the design of TPF and show that produces excellent performance under various system conditions.

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