隨著資訊科技的發展，網際網路成為個人獲得資訊的主要來源之一。但是過多的資訊產生資訊爆炸(information overload)的現象，人們除了要在眾多資訊中找尋想要的資訊外，還需要擔心所尋找到的資訊的品質是否良好。因此，推薦系統提供了一個良好的解決方法。推薦系統透過分群與推薦的技術來達到減少資訊量與推估使用者潛在興趣的目的。目前推薦系統多應用在單一維度的推薦，本論文希望藉由某一情境來探討多維度推薦的應用，所以選擇助理軟體來實現多維度推薦的應用。選擇助理軟體是由於其已經成為個人日常生活中時常使用的工具，且由於助理軟體管理個人日常生活中的大小事務，成為最貼近個人的工具。若專注在個人行事曆的安排上，我們可以發現個人行事曆安排牽涉到有人、事、時、地、物五個維度。因此我們以五維度做分群，透過合作推薦(Collaborative Recommender)的方式將可以達到個人潛在興趣的多維度(Multi-Dimensions)推薦。本研究將以行事歷排定為情境，來說明如何將五個維度的各種可能組合依照其契合個人興趣的程度來進行推薦，這將使得助理軟體的內容更加豐富，且能貼近使用者的需求，提供意想不到的資訊組合。 / With the development of information science and technology, assistant software becomes a tool which often uses in personal daily life, and because all kinds of affairs in personal daily life that assistant software is managed, so assistant software becomes a tool which personally close to people. Intelligent assistant software hopes to make assistant software have intelligence which is similar to the mankind. Just like a personal general secretary, arrange the most proper individualized journey. Further, it can combine the idea of Recommender system to recommend the journey of the potential interest while arranging in the personal journey. This research proposes an intelligent assistant software with five- dimensions include of people, thing, when, location and things, uses cooperative Recommender approach to reach multi-dimension recommendation of personal potential interest. This research will give example of meeting as the situation to explain how to make five-dimensions recommendation according to personal interest. This will make the content of assistant software more abundant, and can press close to the user's demand.

癌症至今已連續蟬聯並高居國人十大死因之首，由於癌症初期病患接受適時治療的存活率較高，因此若能「早期發現，早期診斷，早期治療」則可降低死亡率。本文所引用的資料庫，是經由「表面強化雷射解吸電離飛行質譜技術」（SELDI-TOF-MS）所擷取建置的蛋白質質譜儀資料，包括兩筆高維度資料：一筆為攝護腺癌症，另一筆則為頭頸癌症。然而蛋白質質譜儀資料常因維度變數繁雜眾多，對於資料的存取容量及運算時間而言，往往造成相當沉重的負擔與不便；有鑑於此，本文之目的即在探討將高維度資料經由維度縮減後，找出分錯率最小化之分析方法，希冀提高癌症病例資料分類的準確性。 本研究分為實驗組及對照組兩部分，實驗組是以主成份分析（Principal Component Analysis，PCA）進行維度縮減，再利用支持向量機（Support Vector Machine，SVM）予以分類，最後藉由重疊法（Overlap）以期改善分類效果；對照組則是以支持向量機直接進行分類。分析結果顯示，重疊法對於攝護腺癌症具有顯著的改善效果，但對於頭頸癌症的改善效果卻不明顯。此外，本研究也探討關於蛋白質質譜儀資料之質量範圍，藉以確認專家學者所建議的質量範圍是否與分析結果相互一致。在攝護腺癌症中的原始資料，專家學者所建議的質量範圍以外，似乎仍隱藏著重要的相關資訊；在頭頸癌症中的原始資料，專家學者所建議的質量範圍以外，對於研究分析而言則並沒有實質上的幫助。 / Cancer has been the number one leading cause of death in Taiwan for the past 24 years. Early detection of this disease would significantly reduce the mortality rate. The database adopted in this study is from the Protein Mass Spectrometry Data Sets acquired and established by “Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry” (SELDI-TOF-MS) technique, including the Prostate Cancer and Head/Neck Cancer Data Sets. However, because of its high dimensionality, dealing the analysis of the raw data is not easy. Therefore, the purpose of this thesis is to search a feasible method, putting the dimension reduction and minimizing classification errors in the same time. The data sets are separated into the experimental and controlled groups. The first step of the experimental group is to use dimension reduction by Principal Component Analysis (PCA), following by Support Vector Machine (SVM) for classification, and finally Overlap Method is used to reduce classification errors. For comparison, the controlled group uses SVM for classification. The empirical results indicate that the improvement of Overlap Method is significant in the Prostate Cancer case, but not in that of the Head/Neck case. We also study data range suggested according to the expert opinions. We find that there is information hidden outside the data range suggested by the experts in the Prostate Cancer case, but not in the Head/Neck case.

在現今資訊潮流中，企業的龐大資料庫可藉由統計及人工智慧的科學技術尋找出有價值的隱藏事件。利用資料做深入分析，找出其中的知識，並根據企業的問題，建立不同的模型，進而提供企業進行決策時的參考依據。資料挖掘的工作是近年來資料庫應用領域中相當熱門的議題。它雖是個神奇又時髦的技術，卻不是一門創新的學問。美國政府在第二次世界大戰前，就於人口普查以及軍事方面使用資料挖掘的分析方法。隨著資訊科技的進展，新工具的出現，以及網路通訊技術的發展，常常能超越歸納範圍的關係來執行資料挖掘，而由資料堆中挖掘寶藏，使資料挖掘成為企業智慧的一部份。在本篇論文當中，將資料挖掘技術中的關聯法則 ( Association Rule ) 運用至房地產的價格分析，進而提供有效的關聯法則，對於複雜之房價與週邊環境因素作一整合探討。購屋者將有一適當依循的投資計畫，房產業者亦可針對適當的族群做出適當的銷售企畫。 / At this technological stream of time, it is able to extract the value of corporations’ large data sets by applying the knowledge of statistics and the scientific techniques from artificial intelligence. Through the use of these algorithms, the database will be analyzed and its knowledge will be generated. In addition to these, data models will be sorted by different corporation issues resulting in the reference for any strategic decision processes. More advantages are the predictions of future events and how much public is willing to contribute and feedback to new products or promotions. The probability of outcomes will be helpful as references since this information is referable to ensure companies providing quality services at the right time. In another words, companies will have clues in attempts to understand and familiarize their customers’ needs, wants and behaviors, as a result of delivering best services for customers’ satisfactions. Data mining is such a new knowledge that is commonly discussed in the field of database applications. Although it is a relatively new term, the technology is not exactly due to the analysis methods used. Before World War II, the analysis techniques were used in particular to the statistics in census or cases related to military affairs by the US government. Knowledge discovery has been one part of business intelligence in current corporations because these new techniques are inherently geared towards explicit information, rather than just simple analysis. By applying association rules from knowledge discovery technology, this dissertation will provide a discussion of price estimation in real estates. This discussion is involved in investigations into diverse housing prices resulting from the factors of surrounding environment. By referring to this association rule, buyers will acquire information about investment plans while housing agents will gain knowledge for their plans or projects in particular to their target markets.

文化常被認為是跨國併購失敗的重要原因，許多文化因素在研究與調查中仍屬薄弱。在本論文中，國家文化是衡量跨境併購文化差異的重點，我們使用了Hofstede 六維度來分析兩種家公司併購情況，即使聯想案例文化維度距離高於TCL案例，但聯想理解在最短時間內和解文化差異，聯想最終解決了問題並變成了利潤。研究表明，溝通是必要的，大大提高了併購的成功性，管理層在合併前，文化評估是必要決策收購的成敗的重要因素。 / The failure rate of cross-border M&As is still high and culture is often blamed for hampering performance. If substantial research has been devoted to investigating M&As performance, cultural factors remain largely unexplained. In this research, national culture is the focus to measure cultural differences in cross border M&As. we used Hofstede 5 dimensions to analyzes two cases, even though Lenovo case cultural dimension distance is higher than TCL case, but Lenovo understand reconciling cultural differences in the shortest time, Lenovo eventually solved the problems and turn into profit. The studies reveal that communication is a necessity, drastically improving the success of a merger, and a cultural assessment of both fit and potential are important factors for providing direction and guidance for necessary decision making and planning initiatives required by management throughout all stages of a merger or acquisition. The purpose of this conceptual paper is to highlight the tensions generated by national culture in cross-border M&As and Chinese enterprises want to increase the success rate of Cross-border M&As, they have to pay close attention on the cultural problems, make a good cultural assessment and manager the cultural integrating work in the cultural integrating process.

商業智慧是近年來企業邁向成功重要的工具，在競爭劇烈的市場上採用商業智慧來作業其管理效果卓越。 企業除了提高產能、準時交貨率、服務品質、客戶滿意度等競爭優勢等；另外降低不良率、減少應收帳款周轉天數、減低庫存周轉率、重工率、報廢率、退貨率、客戶抱怨、成本….等。 本文就是利用商業智慧 (Business Intelligence)的企業管理儀表板 (EMD – Enterprise Management Dashboard)的一個成功的案例，例如：單一畫面全方位管理、多維度互動式 (Multi-Dimensional)、相關性快速分析、交叉分析 (Slice & Dice)、穿透鑽取 (Drill-Through)、上捲下鑽 (Drill-Down & Drill-Up)及線上分析 (OLAP – On Line Analytical Processing)引擎，提供各級主管即時的資訊作為管理工具，即時自動轉為視覺化資訊 (Visualizer Web) – 協助企業快速透視整體營運狀況，進而採取最佳行動方案快速超過對手，創造出良好的營運績效為企業帶來可觀的收益與利潤。