政府機關檔案的開放與應用是當代民主政治運作的磐石，良善且完備的應用制度與開放管道，將能深刻機關檔案的價值及資料開放的深度。由於政治環境及歷史遺緒使然，我國國防檔案開放應用的過程是在相對保守及封閉的情境中所進行。隨著我國民主政治的穩定發展，國防檔案在管理及實務層面歷經了多次的轉變與調整，循序漸進的推展檔案開放應用的進程及範圍。本研究以政策評估理論中的評估指標，來觀察及解析國防檔案開放應用過程的影響與箝制因素，並聚焦於國防部內部使用者在實務過程及現實層面所提出的觀點與問題，嘗試進一步分析及詮釋國防檔案在開放應用時所面臨的困境及挑戰，並提出研究建議。 研究發現在效能性及效率性指標層面，因組織降編與整併、業務移轉與承接、人事精簡與離退及檔案審查行政流程等現實，肇生了國防檔案開放應用在政策效能、資源配置及行政效率等問題。在充分性及回應性指標層面，檔案開放應用的現行內容、範圍及數量未能充分滿足使用者的需求，而檔案管理人員的知能未能有效傳承，形成了執行面向與政策價值之間的落差。在公平性及適當性指標層面，檔案開放應用的管道、主題徵集作業停止及國防組織本質與法規層面的特性限制，使得政策目標的價值受限，並出現了目標偏失的困境。 本研究提出的改善建議可分為政策及實務執行二個層面。在政策層面的改善建議有三點：國防部應重視檔案管理及檔案應用的人力配置與預算資源，避免落入員額精簡或單位裁撤的謬誤循環之中；國防部可積極研擬將非機敏性業務，進行業務委外辦理的推動工作；另外可就現行法制規範層面進行適度的修正及開放等。而實務執行層面的改善建議則有四點：建議國防部在業務流程應積極推動簡化，並以作業管理的資訊化作為輔助；再來應在檔案審查准駁的作業流程進行效率化工作，以提升行政服務的效率；同時應關注檔案管理人員在專業知能上的培訓，以及機關內部和外部的進修訓練；另外建議國防部可以嘗試建立並整合檔案應用的網絡機制，透過多元行動者的互動參與及資訊的垂直和水平整合，來提升檔案開放與應用的實質成效。 / The access of government archives is the cornerstone of modern democracy. Due to the political environment past decades, the access of the national defense archives in Taiwan has been conducted in a conservative and confined manner. With the steadily democratic development in Taiwan, the management of the national defense archives has experienced much improvement and has gradually been stimulating processes and scope of public access. This study, using evaluation indicators adopted from policy evaluation theories; try to analyze the impact and limitations of accessing national defense archives, with a focus on opinions and suggestions made by internal users from the Ministry of National Defense (MND) in Taiwan. Further, this study aims to further analyze the issues and challenges faced and propose some recommendations. The study found that the access of national defense archive has difficulties in policy effectiveness, resource allocation, and administrative efficiency that caused by organizational downgrades and downsize, job assignment and adjustment, personnel reductions, and the archives reviews process. In terms of adequacy and responsiveness indicators, the content, scope and quantity of the archives accessed could insufficiently meet the demands of users, and the knowledge of the archivists not been transferred causes a gap between the practical implementation and the value of the policy. In terms of equity and appropriateness indicators, the access route, stop of theme acquisition and the nature of MND had limited the value of the policy. The suggestions proposed in this study are for policymaking and practical implementation individually. Three suggestions were for improving the policy making process. The MND should pay more attention to archive management, human resource and budgets reallocation in archives division. The MND could outsource some non-classified archive management work. In addition, current public access regulations should make some appropriate adjustments in accordance with reality. There are four suggestions for further improvements of practical implementation. First, the MND should actively promote process reengineering and use information technology as an aid in operation. Second, simplifying the workflow of access of national defense archive should be implemented. At the same time, archivist professional training should be enhanced. Last, the MND should establish an archive-access network; through it the effectiveness of access can be improved.

摘要 　　觀光旅遊是現今十分興盛的休閒活動，根據 2010 至 2012 年交通部觀光局統計資料顯示，國人國內旅遊以非旅行社承辦平均高達約 88%，因此如何有效規劃行程內容及安排各景點間的交通路線，就成為旅遊前的重要準備工作。 　　檢視目前旅遊規劃的數位服務平台並無法真正解決旅遊需求。故本研究設計與建置一套旅遊支援平台，首先藉由訪談，了解使用者需求，歸納出平台設計重點，分析系統的使用流程，再設計功能模組化與操作介面，最後建置平台並進行原型評估。本平台以雲端資料庫為基礎，無縫結合網站與行動應用兩端的資訊流。使用者在行前利用網站規劃行程內容，完成後上傳至雲端資料庫，網站端內容會自動同步於行動應用端，並以適合行動應用的界面呈現。使用者之行程安排因此更便利與彈性，在行動中亦能利隨時以手機查看行程，並依當地情況修改行程、即時安排交通路徑，使得旅遊行程計畫可以依實際的變化機動調適。而更新後的行程亦同步儲存於雲端。 　　本研究未來方向可以往更多的服務串流發展，如景點的多媒體內容、社群結合、智慧邏輯排程或外部資訊連結等等。隨著未來資訊技術的提升，將為系統平台帶來更多可能性、讓整體服務擁有更佳的整合性，提供使用者更好的體驗。 / Nowadays, touring is a popular leisure activity. According to statistics from the Taiwan Tourism Bureau, people arranged their own domestic travels as high as 88%. Because backpacking can be economical and more flexible, many young people tend to travel by themselves. Therefore travel planning has become the most important preparation work. How to plan an effective schedule and how to arrange the routes between attractive points are the tasks that backpackers often need to face. However there are few information services for the needs currently. The thesis designed and implemented a travelling support platform. First, the research identified design key elements by interviewing users and analyzing travelling behaviors. Second, the research mapped the touring process into functional models. Finally, we built and tested the platform prototype. This platform is Cloud-based and seamlessly integrates both website and mobile application to achieve the information service. Users use the website to schedule their trips and upload the schedules to the cloud database. Then the user can access the same schedule made at website on the mobile APP synchronously. They can check their schedule while they are traveling on the mobile phones. In addition, they can edit the schedule and rearrange new traffic routes real-time to adjust to the local circumstances. The further developments of the platform will include more integrated services, such as multimedia contents, social network, and intelligent logic scheduling. With the evolution of the technology in the future, the platform will provide better user experience.

近年來，智慧型手機(Smartphone)的銷量超過其他型式手機。智慧型手機具有更先進、更開放的行動作業系統，可允許使用者自行安裝應用程式軟體(Application)來擴充手機功能。目前市面上的應用程式數量非常龐大，在眾多的應用程式和有限的時間下，使用者不太可能將所有的應用程式下載試用，所以對使用者而言，找出自己所想要和需要的應用程式，是個困難的問題。推薦系統可依照使用者的喜好，或是準備推薦項目的相似程度來做推薦，讓使用者能較快得到想要的資訊，目前主要的方式有協同過濾(Collaborative Filtering, CF)、內容過濾(Content-Based Filtering, CBF)，還有結合前述兩種方式的混和式推薦(Hybrid Approach)。 本研究所使用的資料集是由政治大學資訊科學系所開發的實驗平台蒐集而來。資料以側錄的方式，將使用者實際操作手機應用程式的狀況記錄下來，其中包含了25位使用者和1125個應用程式。我們將原始資料集以三種方式整理成三個資料集：一、是否使用應用程式；二、使用應用程式的次數；三、使用應用程式的頻率，其值表示使用者在該應用程式的使用狀況。我們並將資料分成前段與後段時間兩部分，以前段時間的資料當作基準，推薦最多同群使用者使用的應用程式、同群使用者使用次數最多的應用程式，以及同群使用者最常使用的應用程式，然後以後段時間的資料做驗證，計算推薦結果的準確率與召回率加以比較。 我們使用知名的Information Theoretic Co-Clustering Algorithm和兩種基於Minimum Squared Residue Co-Clustering Algorithm的演算法將使用者與應用程式分群，利用分群結果做計算，推薦應用程式給使用者。實驗發現三種演算法在第一個資料集的準確率與召回率表現最好，此資料集以0和1的值，來紀錄使用者在各應用程式的使用狀況。實驗比較三個演算法的結果，在大部分的情況之下，一個基於Minimum Squared Residue Co-Clustering Algorithm的演算法，給出的結果較好。 此外，我們也發現應用程式開發者將應用程式上架提供下載時，以個人主觀想法對該應用程式定義其分類，與我們利用雙分群方法，以使用者實際操作的情況將應用程式分類的結果有些差異，或許在Google Play的分類上可做調整。 本研究提出推薦系統的框架具有彈性，未來可以使用不同的雙分群演算法做分群，也能套用其他的推薦方式。 / With the rapid evolution of smartphone devices, tens of thousands applications have been supplied on online stores such as App Store (operated by Apple Inc.) and Google Play (operated by Google Inc.). Since there are many applications, recommending applications to users becomes an important topic. In this thesis, we present a framework for using a co-clustering algorithm to recommend applications to users. Recommendations are a part of everyday life. People usually rely on some external knowledge to make informed decisions about a particular artifact or action. Using recommender systems is one of general approaches that help people make decisions. There are three common types of recommender systems, namely collaborative filtering, content-based filtering, and hybrid recommender systems. In this thesis, we use the dataset that was collected by a tool developed by the Department of Computer Science at the National Chengchi University. It recorded the users’ behavior when they were using their smartphones. We transform the original dataset into three types of datasets: 1) indicating whether a user used an application; 2) indicating the number of uses made by a user for an application; 3) indicating the frequency of uses made by a user for an application. Furthermore, we divide each dataset into two parts: The first part containing data for the early time period is used as the recommending base, and the second part containing data for the late time period is used for verifying the results. We utilize three famous co-clustering algorithms, which are the Information Theoretic Co-Clustering Algorithm and two algorithms based on the Minimum Squared Residue Co-Clustering Algorithm, in the proposed framework. According to the clusters given by a co-clustering algorithm, we recommend top five applications to each user by referring to the maximum number of users, the maximum number of uses, and the most frequently used applications that are in the same cluster. We calculate the precision and recall values to compare the results. From the experimental results, we find that the best result corresponds to the first type of dataset and also that one of the algorithms based on the Minimum Squared Residue Co-Clustering Algorithm is better than the other two algorithms in terms of the precision and recall values. From the clusters of applications, we obtain some interesting insights into the categories of applications. The categories of applications are set by their developers, but the users may not totally agree with the settings. There might be space for improvement for the categories of applications on the online store. In the future, we can utilize different co-clustering algorithms and other recommended methods in the proposed framework.

隨著網路應用的發達與普及，應用系統的安全防護非常重要，但是要將安全方防護方面的設計與製作做好，卻不容易。因為與安全相關的程式碼必須嵌入到應用系統的各個模組中去執行，具有橫跨（cross-cutting）的特性。在設計時，若不加以區分，仍然以一般的物件或是函式模組來將其模組化的話，往往造成系統中反覆出現類似的程式碼以及不同需求的程式碼夾雜不清的現象，當系統愈趨複雜時，這些問題就愈顯嚴重，結果導致系統不易維護且錯誤頻仍。 最近興起的剖面導向程式設計（Aspect-Oriented Programming）基於關注分離的原則（Separation of Concerns），針對像安全這類橫跨性的需求，倡議在原有的物件或函式模組外，另以剖面（aspect）作為這些橫跨性需求的模組單位，以大幅改善應用系統的模組性。近兩三年來，這方面的發展迅速，各種支援方面導向的程式語言與相關工具相繼推出，美國全錄公司柏拉圖實驗室發展的AspectJ語言就是一個具代表性的成果。本論文以剖面導向的原則，以AspectJ及JBossAOP為主要工具，針對Web應用程式在認證與存取控管方面的安全需求，設計與製作一套具重用性且可處理資料內容相關、細緻層級的存取控管框架。 / Access control is a system-wide concern that has both a generic nature and an application dependent characteristic. It is generic as many functions must be protected with restricted access, yet the rule to grant a request is highly dependent on the application state. Hence it is common to see the code for implementing access control scattered over the system and tangled with the functional code, making the system difficult to maintain. This thesis addresses this issue for Web applications by presenting a practical access control framework based on aspect-oriented programming (AOP). Our approach accommodates a wide range of access control requirements of different granularity. AOP supports the modular implementation of access control while still enables the code to get a hold of the application state. Moreover, framework technology offers a balanced view between reuse and customization. As a result, our framework is able to enforce fine-grained access control for Web applications in a highly adaptable manner.

「餐桌變化多端，輕鬆管理菜籃。」是Chef Mommy帶給使用者的主要價值。 Chef Mommy為了解決有烹飪需求者在菜色變化與食材選購上的問題，透過網站與手機應用程式，應用雲端科技提供食譜搜尋引擎、食材庫存管理、食譜比對推薦、一週菜單規劃與採購清單管理等服務，為使用者提供方便省時，又可兼顧菜色變化與食材管理的料理解決方案。使用者藉由Chef Mommy的服務，能夠以家中庫存食材為基礎，得到原本沒想到的菜色建議，進而在備餐時搭配出更多菜色變化，為家人、朋友準備出豐盛而美好的一餐。 以台灣為例，行政院主計處2010年的家庭收支調查顯示，國內家庭花費在飲食相關採購上的支出，高達8,400億元，在這當中，國內整體食材供應市場規模粗估超過2,000億元。Chef Mommy除了在第一、二階段，針對網站與手機應用程式開發服務功能外，第三、第四階段更將規劃與食材供應業者或連鎖超市業者進行合作，將食譜推薦服務與食材購買進行連結，切入使用者的購買流程，並據此獲利。 藉著滿足使用者輕鬆管理食材與希望菜色天天有變化的需求，Chef Mommy將投入資源，培養使用者常用的習慣，以深入使用者的飲食體驗。透過使用者長期使用的歷程記錄，可了解使用者習慣購買的食材與偏好的食譜、料理方式等資訊，據此拓展出更大的飲食市場商機。 / In order to solve users’ problems of dish variety and ingredients purchase, Chef Mommy will provide services such as recipe search engine, ingredient inventory management, recommended recipes, weekly menu planning and shopping list management via its website and mobile app. As for these convenient services, Chef Mommy wants to provide a total cooking solution that can help users saving their time and giving consideration to dish variety and ingredients purchase at the same time. Based on ingredient inventory in home, Chef Mommy will recommend recipes that users may not expect or remember originally, and then with more dish variety, they can prepare a bountiful meal for family and friends. In Taiwan, 2010 Family Income and Expenditure Survey by the DGBAS shows that domestic household expenditures spent on diet-related purchase, up to NTD$ 840 billion, in which the overall food supply market size were roughly over NTD$ 200 billion. Chef Mommy will focus on developing services functions in the 1st and 2nd stages, and cut into the users’ buying process by recipes recommend service in the 3rd and 4th stages. By linking users’ recipe choice and ingredient purchase, Chef Mommy will cooperate with food supply companies or supermarket chains and gain profit from it. Chef Mommy will invest resources to training users’ habits of using Chef Mommy’s services. Via users’ long-term usage history, Chef Mommy will know users’ accustomed to buy ingredients, preferred recipes, cooking methods and other cooking information. According to this, Chef Mommy will be able to expand to a greater diet market.

自台灣地區演藝行業隨著經濟發展起飛，工商業繁榮與人民生活品質的提升帶起了電視廣告、唱片、電影、偶像劇的盛行，而媒體的推波助瀾更吸引大量的年輕人投入演藝行業的意願和行動，促進了演藝人才媒合產業的發展。然而演藝工作是一項傳統行業，其運作模式自第二次世界大戰結束迄今五十年未變，潛在演出人和需求單位間媒合的不效率，使得尋找演出人的時間成本增加，加上因此所衍伸而出層出不窮的社會問題，讓演藝娛樂產業業者與相關單位都亟需擁有更加安全、效率的方式來滿足各自的需求。 台灣地區以模特兒經紀服務為名目，卻並不是真的有提供演員試鏡機會資訊，卻要求有心進入演藝行業的年輕人進行自費拍照或自費接受模特兒教育課程，此類俗稱詐騙型的藝人經紀公司林立，根據經濟部資料顯示超過三千兩百餘家，估計每年以此類似掛羊頭賣狗肉行為進行營利行為的產值，超過新台幣百億元，加上正派經營型態的模特兒（藝人）經紀公司在廣告拍攝、戲劇、唱片發行、版權授權與活動舉辦等文化外銷產業中獲得的產值，每年更超過新台幣千億元。近年來隨著政府致力推廣「台灣軟實力」，其發展後勢亦被看好。 在經紀公司當中大量比例以演藝經紀為名，本身卻並非從事以媒介通告工作，僅以演藝名目從中賺取不當收益者，並從事騙財騙色的詐騙種種行為，又使得這個行業更加蒙罩上一層負面陰影。 隨著網路的普及，演出人尋求通告機會的管道趨於多元化，Terpstra(1996)提出網際網路線上服務是最新的管道，也是最有發展潛力的招募方式，透過網路的「連結性」與「可達性」(謝清佳，民89)，將「媒體」以及「仲介」的角色結合，使網路成為一個服務平台，串聯多個虛擬社群與市場，並支援各種不同型態的傳播溝通服務(Huston,2000)為傳統演藝界人才媒介市場帶來新的契機。 網際網路平台向以訊息自由為特質，本研究以網際網路為之於演藝人才產業媒介之應用為題，嘗試找出一條創新模式，使得演藝行業也能最大程度地實現公平效率和安全，讓有明星夢的年輕人能放心地嘗試夢想，也讓演藝行業中的演出人才配置更有效率。

行動應用程式是當今最受歡迎和最主要的軟體應用程式，因此應用程式的實際行為以及相關的安全和隱私問題變得越來越重要。另一方面，隨著時間的推移，AppStore上有越來越多的應用程式已經停止更新或停止服務，但沒有從AppStore中刪除。然而，用戶對於缺少維護問題一無所知，仍然下載並使用它。在本研究中，我們將解決在應用程式中檢查特定屬性方法序列的問題。通過使用IDApro生成Function call dependency graph和Subroutine control flow graph，我們使用語法分析方式來進行跨子程式的序列檢查方案。我們將通過預先定義屬性的方法序列作為模型來檢查應用程式行為。這個分析方法可以說明在App Store中可用的應用程式中是否存在屬性方法序列。有助於我們在應用程式中檢查一些惡意行為屬性方法序列或特定行為方法序列（例如使用不推薦的api方法）。 我們的網絡爬蟲從官方文件中摘取了的所有可用的iOS SDK方法，並從中提取做為我們的模型序列。我們將檢查應用程式是否包含所準備的模型序列。如果應用程式中存在該序列，我們將在應用程式中記錄子程式中包含的方法序列調用。然後將結果數據匯總到我們的數據庫中，並將結果視覺化、數據化，並建立系統的的API服務。最後，我們構建了一個使用上述檢查功能所識做的的分析系統，並以Web服務形式顯示結果。 / Mobile application is the most popular and dominant software applications nowadays, so the actual behaviors of the application and the related security and privacy issues become more and more important. On the other hand, as time goes by, there are more and more applications on the AppStore stop to update or being abandoned but not removed from AppStore. However, the users know nothing about the lack of maintenance problems and still download and use it. In this research, we will resolve the issue for checking specific property method sequence within an application. By using IDApro to generate function call graph and the subroutine control flow graphs, we use syntax checking strategy to perform a across subroutines sequential checking solution. We will check the application behavior by predefining a property method sequence as pattern and then check with applications’. The analysis method can illustrate whether a property method sequence exists in the application which is available on App Store. This may help us to check some malicious behavior property method sequence or specific behavior method sequence (ex. using deprecated api methods) in the applications. We have prepared some property method sequence as our system input pattern extracted from all the available iOS SDK methods fetching by our web crawler. We will check whether an application contains the prepared method sequence or not. If the sequence exists in the application, we would record the method sequence call included in the subroutine within the application. Then the results data will be aggregated in our database, and export as api service for visualizing and statistic uses. Finally, we construct a call sequence analysis system for the above checking functions and show the result in a web service form

當今已有數以百萬計的行動應用程序在 Apple 的 App Store 中發布，並在iOS設備下載量超過150億次。為了保護iOS用戶免於惡意應用程式的傷害，Apple 對於上架之App 有相對嚴格的審查政策。通過審查的App才能在App Store中發布。在本文中，我們提出基於 iOS可執行檔的靜態字串分析技術用於檢驗App可能動態載入之類別 。為了檢查動態載入之類別是否符合Apple之規範，必須要能確定動態加載函數之可能字串參數值 。我們方法的第一步是使用現有工具擷取 iOS可執行檔的組合語言。然後自組合語言中建立整個程式的控制流程圖（CFGs） 。接著，在控制流程圖上識別動態加載類別的函數，並且對於該函數的每個參數，我們構造一個字串相依圖，用以顯示流向字串參數值的所有構成成分以及構成方式 。最後，我們對這些可能流向參數的字串進行字串分析，以確定這些參數值所有的可能值集合。透過把這些可能值與特徵值（從Apple 審查政策建構而來，例如私有/敏感性API），我們能夠檢測到App 潛在違背Apple政策之情形。我們分析了1300多種目前上架於App Store的App，並檢查他們是否違反蘋果關於使用私有API的政策以及 廣告識別碼（IDFA）政策。我們的工具提取了超過37000 這些App的字符相依圖，分析結果顯示208個App透過字串操作構組合出對應的API名稱並且有潛在的IDFA違規濫用之可能。我們的分析還發現了372個可以使用字串構建私有類名稱的應用程序和236個可以使用路徑字符串加載私有框架的App，這些App可能違反Apple 禁止使用私有API使用政策。 / Millions of mobile apps have been published in Apple's AppStore with more than 15 billion downloads by iOS devices. In order to protect iOS users from malicious apps, Apple has strict policies which are used to eliminate apps before they can be published in the AppStore. In this paper we present a string analysis technique for iOS executables for statically checking policies that are related to dynamically loaded classes. In order to check that an app conforms to such a policy, it is necessary to determine the possible string values for the class name parameters of the functions that dynamically load classes. The first step of our approach is to construct the assembly for iOS executables using existing tools. We then extract flow information from the assembly code and construct control flow graphs (CFGs) of functions. We identify functions that dynamically load classes, and for each parameter that corresponds to a dynamically loaded class, we construct a dependency graph that shows the set of values that flow to that parameter. Finally, we conduct string analysis on these dependency graphs to determine all potential string values that these parameters can take, which identifies the set of dynamically loaded classes. Taking the intersection of these values with patterns that characterize Apple's app policies (such as private/sensitive APIs), we are able to detect potential policy violations. We analyzed more than 1300 popular apps from Apple's AppStore and checked them against Apple's policy about the use of private APIs and the identifier for Advertising (IDFA). Our tool extracted more than 37000 string dependency graphs from these applications and our analysis reported 208 apps that compose the corresponding API with strings and have potential IDFA violations. Our analysis also found 372 apps that could have compose the private class name with string and 236 apps that could have load the private framework with path string; and could violate the private API usage policy.

近年來，電信業已由寡占走向了完全競爭市場，伴隨著消費者意識的抬頭，有限的市場大餅，更侷限了用戶數的大躍進，因此各家電信營運商無不卯足全力，專注在提供吸引消費者的產品、資費方案、以及ARPU（Average-Revenue-Per-User）的提昇…等議題上。企業要如何面對競爭慘烈的市場？如何保持內部流程的彈性去因應變化？基於上述事項，如何運用資訊科技來達到企業的目標就更加重要了，而EAI 的功能，提供了企業對應用系統的整合，維持流程的彈性應用，更進而協助企業降低整合風險、節省資訊軟體的開發與維護成本、強化供應商的合作關係，進而產生對企業的效益。 本研究使用個案研討的方式，選定幾項關鍵流程進行深度分析，針對電信業在營運系統上整合的議題作探討，並說明企業整合應用軟體導入所帶來的效益。對於電信產業而言，客戶關係管理系統、帳務系統、開通系統、加值服務應用平台系統…等是整合的開端，亦是重點之所在。經由EAI軟體的導入，提供營運應用系統之間資訊的同步，也相對的提供決策人員反應快速的資訊。 電信業者在系統建置中，如何有效運用企業整合軟體之特性來整合各異質性平台應用系統，以達到資訊的整合，並藉由資訊的及時傳遞提供各不同應用系統使用者一致的資訊，進而提高資訊的可用度及客服人員及門市人員對客戶的服務，增加企業的競爭力。 / Telco has already been moving toward perfect competitive market since last decade. Meanwhile, along with the ideology of consumers that dominates the trend and momentum of open market. It also blocked the growth rate of subscribers in domestic market. Under such circumstance, all Operators are fully concentrating on value-added product, tariff, rate plan…and so on, to attract consumers so that Operators are able to increase the ARPU (Average-Revenue-per-User). How to face the severe competitive market? How to maintain the flexibility of internal process to adopt the drastic changing market？ Based upon above mentioned issues, it is very important to use information technology to approach this target. Nevertheless, the functionality of EAI (Enterprise Application Integration) offers an all-in-one solution and integration to application system. Still, it also maintains the flexibility of process flow. Further more, it reduces the risk of enterprise integration and saves cost in terms of implementation and maintenance. On the other hands, EAI is not only to enhance the relationship of suppliers but also generate the synergy of enterprise. The purpose of this theme is to deeply analyze the critical process inside company. Besides, it also points out that how to use EAI in Telco field for the purpose of gaining benefit to Operator. Telco’s IT system includes CRM (Customer relationship management), Billing system, Provision, and the platform of value-added service and application. After installing EAI, it is able to offer the synchronization among all application system and capable to expedite the response time of decision makers. In addition, it is very important to use EAI to integrate the different application platform. After that, EAI is able to consolidation all necessary information and deliver all users with same information just in time. Customer Service Division and Chain Store could transfer this power to secure better services to consumers then enlarge the competition of enterprise.

全球面臨人口老化與人口不斷成長的壓力下，對於醫療服務的需求不斷提升。醫療服務領域中常以資料探勘「關聯規則」分析，挖掘隱藏在龐大的醫學資料庫中的知識(knowledge)，以支援臨床決策或創新醫療服務。隨著醫療服務與應用推陳出新(如，電子健康紀錄或行動醫療等)，與醫療機構因應政府政策需長期保存大量病患資料，讓醫療領域面臨如何有效的處理巨量資料。 然而傳統的關聯規則演算法，其效能上受到相當大的限制。因此，許多研究提出將關聯規則演算法，在分散式環境中，以Hadoop MapReduce框架實現平行化處理巨量資料運算。其相較於單節點 (single-node) 的運算速度確實有大幅提升。但實際上，MapReduce並不適用於需要密集迭帶運算的關聯規則演算法。 本研究藉由Spark記憶體內運算框架，在分散式叢集上實現平行化挖掘多維度多顆粒度挖掘關聯規則，實驗結果可以歸納出下列三點。第一點，當資料規模小時，由於平行化將資料流程分為Map與Reduce處理，因此在小規模資料處理上沒有太大的效益。第二點，當資料規模大時，平行化策略模式與單機版有明顯大幅度差異，整體運行時間相差100倍之多；然而當項目個數大於1萬個時，單機版因記憶體不足而無法運行，但平行化策略依舊可以運行。第三點，整體而言Spark雖然在小規模處理上略慢於單機版的速度，但其運行時間仍小於Hadoop的4倍。大規模處理速度上Spark依舊優於Hadoop版本。因此，在處理大規模資料時，就運算效能與擴充彈性而言，Spark都為最佳化解決方案。 / Under the population aging and population growth and rising demand for Healthcare. Healthcare is facing a big issue how to effectively deal with huge amounts of data. Cased by new healthcare services or applications (such as electronic health records or health care, etc), and also medical institutions in accordance with government policy for long-term preservation of a large number of patient data. But the traditional algorithms for mining association rules, subject to considerable restrictions on their effectiveness. Therefore, many studies suggest that the association rules algorithm in a distributed computing, such as Hadoop MapReduce framework implements parallel to process huge amounts of data operations. But in fact, MapReduce does not apply to require intensive iterative computation algorithm of association rules. Studied in this Spark in-memory computing framework, implemented on a distributed cluster parallel mining association rules mining multidimensional granularity, the experimental results can be summed up in the following three points. 1th, when data is small, due to the parallel data flow consists of Map and Reduce, so not much in the small-scale processing of benefits. 2nd, when the data size is large, parallel strategy models and stand-alone obviously significant differences overall running time is 100 times as much when the item number is greater than 10,000, however, stand-alone version cannot run due to insufficient memory, but parallel strategies can still run. 3rd, overall Spark though somewhat slower than the single version in small scale processing speed, but the running time is less than 4 times times the Hadoop. Massive processing speed Spark is still superior to the Hadoop version. Therefore, when working with large data, operational efficiency and expansion elasticity, Spark for optimum solutions.