本研究所關切的議題為，當服務已被良好的設計出來後，為了不辜負這個被良好設計的服務，該怎麼使用它，進而能為所有重要關係人帶來最大的利益，特別是在資源有限的動態情境中。因此，我們將研究問題著眼於服務的傳遞過程中。現有的研究已回答了服務該如何被傳遞，但卻缺少了該在什麼時候，傳遞怎樣的服務才能發揮該服務的功效，帶來預期的好處(例如，贏得顧客滿意度及延續公司競爭力)。於是，本研究試圖提出一以模糊認知圖為基礎架構並考量顧客的心理狀態的服務分派機制，以期在如此動態且資源有限的情境下，能觀測當時的情境變化，透過管理顧客的期望與情緒，做出即時且適當服務分派決策，進而在對的時間針對對的人做對的服務。也就是冀望這樣一個顧客心理狀態的管理過程，能夠使得顧客感到滿意的可能性提高，並有助於所有重要關係人達成其目的，創造整個服務生態體系的平衡。考慮到會展服務即為一動態且資源有限的服務應用情境，本研究將以會展服務做為例子，加以描述整個研究的內容。 / There are already some researches providing the answer to how to deliver services but the issue “when to deliver which service” is still not so clear. Especially under the dynamic and resource-limited situation, bringing the effectiveness of each service into full play and allocating them appropriately to earn the most benefits are imperatives for service providers to keep both service quality and competitiveness. Therefore, the FCM-based mental-driven service dispatcher proposed here tries to pull service receivers’ mental information in to make real-time service deployment decisions which are capable of achieving each stakeholder’s purposes and satisfying service receivers. With the mental information – expectation and emotion, we are given a hand to do the right things at the right time to the right person by building up such a customer- mentality-centric service dispatching system.

空間認知(Spatial Cognition)指出人所理解的空間複雜度，也就是人與環境互動的過程中，經由記憶與感官經驗，透過內化與重建產生物體在空間的關係認知。認知圖(Cognitive Map)是最常被使用在評估空間認知。分析學生所畫的認知圖有助於老師們瞭解學生的空間認知能力，進而擬定適當的地理教學設計。我們視空間認知發展的學習歷程檔案是由這些認知圖所構成。隨著數位學習科技的進步，我們可以透過探勘認知圖的方式，探討空間認知發展的學習歷程檔案。因此，我們藉由透過圖像的空間資料探勘，分析學生空間認知發展的學習歷程。 空間資料探勘(Spatial Data Mining)主要是從空間資料庫或圖像資料庫中找出有趣且有意義的樣式。在論文中，我們介紹一種空間樣式(Spatial Co-orientation Pattern)探勘以提供空間認知發展學習歷程的分析。Spatial Co-orientation Pattern是指圖像資料庫中，具有共同相對方向關係的物體(Object)常一起出現。例如，我們可以從圖像資料庫中發現物體P常出現在物體Q的左邊，我們利用二維字串(2D String)來表示物體分佈在圖像中的空間方向關係。我們透過Pattern-growth的方法探勘此種空間樣式，藉由實驗結果呈現Pattern-growth的方法與過去Apriori-based的方法[14]之優缺點。 我們延伸Spatial Co-orientation Pattern的概念至時空資料庫(Spatio-temporal Database)，提出從時空資料庫中，探勘Temporal Co-orientation Pattern。Temporal Co-orientation Pattern是指Spatial Co-orientation Pattern隨著時間的變化。論文中，我們提出兩種此類樣式，即是Coarse Temporal Co-orientation Pattern與Fine Temporal Co-orientation Pattern。針對此兩種樣式，我們提出三階段(three-stage)演算法，透過實驗分析演算法的效率。 / Spatial cognition means how human interpret spatial complexity. Cognitive maps are mostly used to test the spatial cognition. Analyzing cognitive maps drawn by students is helpful for teachers to understand students’ spatial cognitive ability and to draft geography teaching plans. Cognitive maps constitute the portfolios of spatial cognitive development. With the advance of e-learning technology, we can analyze portfolios of spatial cognitive development by spatial data mining of cognitive images. Therefore, we can analyze portfolios of spatial cognitive development by spatial data mining of images. Spatial data mining is an important task to discover interesting and meaningful patterns from spatial or image databases. In this thesis, we investigate the spatial co-orientation patterns for analyzing portfolios of spatial cognitive development. Spatial co-orientation patterns refer to objects that frequently occur with the same spatial orientation, e.g. left, right, below, etc., among images. For example, an object P is frequently left to an object Q among images. We utilize the data structure, 2D string, to represent the spatial orientation of objects. We propose the pattern-growth approach for mining co-orientation patterns. An experimental evaluation with synthetic datasets shows the advantages and disadvantages between pattern-growth approach and Apriori-based approach proposed by Huang [14]. Moreover, we extend the concept of spatial co-orientation pattern to that of temporal patterns. Temporal co-orientation patterns refer to the change of spatial co-orientation patterns over time. Two temporal patterns, the coarse temporal co-orientation patterns and fine temporal co-orientation patterns are introduced to be extracted from spatio-temporal databases. We propose the three-stage algorithms, CTPMiner and FTPMiner, for mining coarse and fine temporal co-orientation patterns, respectively. An experimental evaluation with synthetic datasets shows the performance of these algorithms.