University of Macau / Faculty of Law

近年來，隨著大數據時代的來臨，即時資料運算面臨許多挑戰。例如在期貨交易預測方面，為了精準的預測市場狀態，我們需要在海量資料中建立預測模型，且耗時在數十毫秒之內。 在本研究中，我們將介紹一套即時巨量資料運算架構，這套架構將解決在實務上需要解決的三大需求：高速處理需求、巨量資料處理以及儲存需求。同時，在整個平行運算系統之下，我們也實作了數種人工智慧演算法，例如SVM (Support Vector Machine)和LR (Logistic Regression)等，做為策略模擬的子系統。本架構包含下列三種主要的雲端運算技術： 1. 使用Apache YARN以整合整體系統資源，使叢集資源運用更具效率。 2. 為滿足高速處理需求，本架構使用Apache Storm以便處理海量且即時之資料流。同時，借助該框架，可在數十毫秒之內，運算上千種市場狀態數值供模型建模之用。 3. 運用Apache Spark，本研究建立了一套分散式運算架構用於模型建模。藉由使用Spark RDD(Resilient Distributed Datasets)，本架構可將SVM和LR之模型建模時間縮短至數百毫秒之內。 為解決上述需求，本研究設計了一套n層分散式架構且整合上列數種技術。另外，在該架構中，我們使用Apache Kafka作為整體系統之訊息中介層，並支持系統內各子系統間之非同步訊息溝通。 / With the coming of the era of big data, the immediacy and the amount of data computation are facing with many challenges. For example, for Futures market forecasting, we need to accurately forecast the market state with the model built from large data (hundreds of GB to tens of TB) within tens of milliseconds. In this research, we will introduce a real-time big data computing architecture to resolve requests of high speed processing, the immense volume of data and the request of large data processing. In the meantime, several algorithms, such as SVM (Support Vector Machine, SVM) and LR (Logistic Regression, LR), are implemented as a subproject under the parallel distributed computing system. This architecture involves three main cloud computing techniques: 1. Use Apache YARN as a system of integrated resource management in order to apply cluster resources more efficiently. 2. To satisfy the requests of high speed processing, we apply Apache Storm in order to process large real-time data stream and compute thousands of numerical value within tens of milliseconds for following model building. 3. With Apache Spark, we establish a distributed computing architecture for model building. By using Spark RDD (Resilient Distributed Datasets, RDD), this architecture can shorten the execution time to within hundreds of milliseconds for SVM and LR model building. To resolve the requirements of the distributed system, we design an n-tier distributed architecture to integrate the foregoing several techniques. In this architecture, we use the Apache Kafka as the messaging middleware to support asynchronous message-based communication.

2017年英國《經濟學人》雜誌曾提出，「世界上最寶貴的資源不再是石油，而是數據」。隨著物聯網時代來臨，工業應用領域也開始整合各種技術而掀起新一波工業革命。因為大量自動化及數據化，除了升級自動化設備、整合網通系統，監控設備產生的大數據，透過工業電腦進行分析，經由人工智能判斷邏輯產生條件，再由設備自主處理各種生產問題。除去大量勞動，專注於大數據自動化處理，即能生產更優質的產品，並且優化流程，降低企業成本。 自動化流程機器人(Robotic Process Automation)能自動的管理並執行企業大量耗費時間與人力的業務流程，可用於客戶服務、人力管理、供應鏈管理、採購、會計等範疇。物聯網(IoT)時代下的機器人自動化流程加入了認知運算等新興技術，更能進一步提升企業效率並降低成本。自動化流程機器人(Robotic Process Automation)儼然成下一個新的生產力革命。 市場研究機構IDC預測，2017年全球在認知和人工智慧系統支出將達到125億美元，和2016年相比成長達59.3%。Google母公司Alphabet公開測試無人駕駛汽車、阿里宣佈投資千億成立達摩院、百度機器人入駐肯德基等等。人工智慧(Artificial Intelligence)將顛覆商業思維、改寫商業模式。在2020年，人工智慧(Artificial Intelligence)將成為市場上真正的「主流」技術思維。IDC並且認為亞洲將在2020年成為全球第二大認知與人工智慧輸出區域。 本文探討自動化流程機器人與人工智慧之間的關聯，以及流程優化後對企業所產生的影響與變革．並且針對個案的自動化解決方案所達到的效益與後續發展進行評估與檢討，藉以提升自動化解決方案，協助企業在未來挑戰的競爭環境中創造最佳化優勢． / “The Economist” stated in 2017 that “the world’s most precious resource is no longer oil but data”. With the advent of the Internet of Things, industrial applications have begun to integrate various technologies and set off a new wave of industrial revolution. Because of a large amount of automation and data, in addition to upgrading automation soluitons, integrating netcom systems, and monitoring the big data generated by the solutions, analysis is performed through industrial computers, and conditions are generated through the logic judgment of artificial intelligence, and then the solutions autonomously handles various processes. It can produce better products, optimize the process and reduce business costs to focus on automation of big data and to save a lot of labor hiring. Robotic Process Automation can automate the management and execution of a large number of business processes that consume time and manpower, and can be used in areas such as customer service, manpower management, supply chain management, procurement, finance and accounting. The robotic automation process in the Internet of Things (IoT) era has added emerging technologies such as cognitive computing to further enhance the efficiency of enterprises and to reduce costs. Robotic Process Automation becomes the next new productivity revolution. In 2017, marketing research firm, IDC, predicts that global spendings on cognitive and artificial intelligence systems will reach US$12.5 billion, which represents a growth of 59.3% compared to 2016. Google, the parent company of Alphabet, publicly tests driverless cars, Ali announced that it has invested 100 billion to establish Daruma House, Baidu Robots has settled in Kentucky. Artificial Intelligence will disrupt business thinking and rewrite business models. In 2020, Artificial Intelligence will become the real "mainstream" technical thinking in the market. IDC also believes that Asia will become the world’s second largest cognitive and artificial intelligence output region in 2020. The article discusses the relationships between robotic process automation and artificial intelligence, and also the impact and changes after implementing the solutions. It has also evaluated and reviewed the effectiveness and following development of the automated solutions, so as to enhance the values of automation solutions and to help companies create optimal advantages in the future challenging and competitive environment.

科技進步神速且日新月異，電腦大數據資訊的傳播與統計資料，可以與物聯網結合，方便消費者一系列之採購需求，各行各業也莫不受其恩賜，但同時可能讓個人隱私權的保密受到威脅。同理，今日預防醫學大數據比起傳統生物統計學，可以處理更多複雜的生物統計項目，包括昔日公共衛生之生物統計學所難以處理的複雜DNA序列，而加以收集、歸納、分析與應用於基因流行病學、癌症基因之篩選、個人化醫療的用藥、老人之長期照顧、孕婦產檢、新生兒疾病篩檢等，並且越來越蓬勃發展。 預防醫學大數據主要是由三種類型之電腦資訊所建構而成：(一)病歷，必須將紙本病歷之數據轉為電子檔案，才可能對於所收集之資料加以歸納分析，形成日後具備預知能力的大數據。(二)病患提供之DNA，收集病患提供之DNA亦可作成具有預測能力的大數據，應用於未來人類基因缺陷之篩檢或治療，以及提供個人化醫學更精準的治療。(三)傳染病之通報案件，作成預防醫學大數據以利於調查疫情，亦有釐清何種因素促成疫情擴散之能力，進而實施衛教宣導，讓民眾知道當地疫情狀況，並貢獻預防方法及加強自我保護。 因此，預防醫學大數據的DNA序列也涉及隱私權之保障，雖指紋、虹膜與DNA序列皆可用來辨識個人身份，對尋人偵辦法律案件皆有幫助，但唯獨DNA序列可用於大眾疾病之預測以及個人化醫學之預防與治療，是人類生物辯識系統中可謂重中之重，不僅可以依此DNA序列尋人辦案，更可以評估個人健康狀況與未來壽命，具備有「預測能力」。因此，不論病患日後之求職履歷或投保，皆可能因DNA序列之外洩，而遭遇到主管的監督或審核者的排斥。將來病患對DNA序列所要求的保密程度會因此更加嚴謹，使得原先醫病之間的隱私權關係，提昇到另一更高的層次。 整個預防醫學大數據基因庫之建立如同水壩，在研發基因庫的單位當然希望「上游」的自願者欲提供自身之DNA人數，可以源源不絕，以增大基因庫的量，期待有更多新的發現。因此，基因庫之「蒐集端」應該以其他國家建立基因庫建置前之規劃或與民眾有公開且相互瞭解之溝通，來進行研討。在「中游」之「管理端」，著重資料之保密與更新，遇到「選擇退出」的民眾，則必須將選擇退出之民眾資料徹底銷毀。如果保密工作未做好，不但自願者會減少，甚至會影響已經參與者繼續參加之意願，正如同水壩有管理上之缺口，容易潰堤。至於「下游」之「應用端」須考慮DNA用於病患篩檢結果，是否影響其日後生活與人際關係。 不論「蒐集端」之提供者對收集者之無私供出自身病歷與DNA資料；「管理端」之對已經提供巨量自願者的DNA的資料，於固定時間與自願者的日常習慣、作息或歷年來的病歷記錄作交叉比對，經常年累月之採取自願者的DNA與更新的日常習慣或最新之病歷記錄作交叉比對，如此不斷更新(up-date)來取得統計學上有意義的DNA序列與某疾病多因子的關聯性，對自願者之病歷與DNA資料有保密義務；或是「應用端」測得DNA後之結果，揭發於受測者知曉；此三階段之流程，無不涉及到個人之隱私權。 世界各國對基因數據的保障有不同立法之思維：德國對基因數據的蒐集及利用，從「個人資訊自決權」著眼，看重於外顯的自由行為是否同意來決定，必須與「告知後同意」始能蒐集、管理與利用的程序保護連結在一起，之後才有權利對抗的問題。然而美國是從個人的「隱私權」出發，強調個人內心私密空間不容任何人干擾，保障個人人格的最後一道城牆，凡侵入或侵占城牆內的任何行為，皆構成侵權行為。 本文解說出「國家防疫」、「個人疾病基因隱私權」與「臨床醫學研究」，此三者間的「衡平原則」：以預防醫學大數據運用而言，所涉及社會秩序公共利益，流行性傳染病之通報，個人「隱私權」之保障，臨床醫學的研究，聯合國宣言等，亦合併本文對國內外案例判決之評析以探究之。 最新之歐洲聯盟執行委員會（European Commission）就「歐盟資料保護規範」（General Data Protection Regulation ; GDPR）之條文內容，使歐盟新個人資料保護法擴及至非歐盟企業也一體適用的法律，已經於2016年年初獲得確定後，並且於2018年正式生效，尤其是法規要求於資料洩漏時必須在72小時內發出通知，知會其所屬企業公司個體、行政主管機關及個資當事人，以及必須遵守資料傳輸的重要相關規定，於本文亦有詳細介紹。 我國最新的醫療法第82條已經於民國107年1月24日公布施行，內容對醫師的損害賠償責任及刑事責任規定為：「醫療業務之施行，應善盡醫療上必要之注意。醫事人員因執行醫療業務致生損害於病人，以故意或違反醫療上必要之注意義務且逾越合理臨床專業裁量所致者為限，負損害賠償責任。醫事人員執行醫療業務因過失致病人死傷，以違反醫療上必要之注意義務且逾越合理臨床專業裁量所致者為限，負刑事責任」。此次修法之目的在於：近年醫療爭議事件動輒以「刑事方式」提起爭訟，不僅無助於民眾釐清真相獲得損害之填補，反而導致醫師採取防禦性醫療措施，修正醫療刑法「過失」之要件，即以「違反醫療上必要之注意義務且逾越合理臨床專業裁量」定義現行條文所稱之「過失」。但是，本文所引用國內外之法院判決，皆為民法與行政法的範圍與案例，即使在最新之醫療法第82條公布之後，亦不影響本文的主張。 本文結論分兩大節提出見解與建議：第一節內容，著重於綜合國內外之民法與行政法的案例判決，以提出評析與見解。第二節內容，從「上游」源頭增加預防醫學大數據「蒐集端」基因庫之泉源，提出建議，以增加我國大數據基因庫的量。透過基因(DNA)之捐贈，可以使「上游」之預防醫學大數據「蒐集端」的源頭能夠源源不絕。「前人種樹，後人與前人皆可以受惠乘涼、利益共享」，況且「預防又勝於治療」；不論國家社會或個人，對於如何促進預防醫學大數據之茁壯與永續經營發展，並且兼顧病患隱私權之保障，本文也提供了最佳的方法與展望。

互聯網的發展與近年來數據平台受到公私部門重視，資訊的取得與流通變得便捷，中國房地產文化目前有別於台灣，尚無實價登錄機制且地域面積廣大，傳統估價模型可能無法直接應用，面對房地產背後眾多的影響因素，本研究將預測建模目標放在泡沫化尚不嚴重且較具有潛力的中國新一線城市杭州市，自新浪二手房網爬取杭州市房地產數據，並自國家統計局取得各地區行政支出數據，作為實證分析資料。結合自動程序爬蟲抓取數據、統計分析與機器學習方法，期望對中國房地產建立一混合非監督式與監督式學習之模型。 在分群結果之後建構模型採用之技術為C5.0、三層CHAID、五層CHAID與Neural Network，挑選出最適合的模型為使用混合模型後的C5.0決策樹方法，達到降低變數維度亦提升或達到相當的預測準確率的雙贏目標，模型中行政地區、面積、總樓層為最頻出現的重要變數。 另外透過集群分析於行政支出的應用，發現2016年度杭州市投入的行政支出集中於余杭區、蕭山區、濱江區，成為賣屋及購屋者的第二項決策標準。 / In recent years, with the growth of the Internet and the importance of data platform on public sector and private sector. Getting and sharing information are made easily. The culture of real estate in China is different from Taiwan. For instance, there is no actual house price registration system. Furthermore, traditional estimate model may not be directly applicable to China which has the vast geographical area of the mainland. There are many factors to influence house price model. This study focus on Hangzhou city. Because the burst of real estate bubbles is not serious as first-tier cities and it is one of new first-tier cities in China. The research data were crawler from Sina second-hand housing website and National Bureau of Statistics. By using auto web crawler skill, statistical analysis, and machine learning method to build a real estate model in China, which was combining unsupervised learning method with supervised learning method. After clustering Hangzhou second-hand housing data, this study used C5.0, three layers Chi-Square Automatic Interaction Detector(CHAID), five layers CHAID, and Neural Network(NN). The study goal are both reducing dimension and getting better forecast accuracy. Choosing clustering- C5.0 model as appropriate house price model to achieve win-win situation after comparing final result. Administrative region, area, and total floor are the top three high frequency influential factors. Applying Clustering Analysis to administrative expenses data in Hangzhou, the study found that the government resource focus on Yuhang, Xiaoshan, and Binjiang. It can be the second decision-making criterion for house sellers and house buyers.

本研究主旨是希望提供一個智能金融演算法交易平台，以Django CMS作為網頁框架，區分成研發環境與交易環境，完整的功能包含用戶研發、用戶測試以及使用演算法服務。用戶研發與測試上採用IPython的互動式開發介面，利用JupyterHub進行管理與配置，能夠同時提供多個用戶存取平台，使得平台足以負載大規模用戶的使用；而演算法服務經由Celery包裝成任務，以利交付給後台進行分散式運算。搭上近年來深度學習的熱潮，平台額外擴充Tensorflow套件與GPU建置，支援多核及高速演算法運算。 面對存取大量、複雜且結構化的金融資料，本研究的資料庫採用HAWQ做為解決方案，利用其極大量平行化的架構，改善過往存取大數據所造成的系統複雜性與效能瓶頸，並搭配Ambari達到創建、監視及管理Hadoop分散式集群的功用，讓開發者在部署與維運上都將事半功倍。 由於採用新的資料庫HAWQ，傳統的資料表設計將不利反傷，因此本研究會針對程式端存取資料庫裡的金融資料，量身打造適合的資料表設計，並對其做效能評測，以確保資料能有效且迅速地被程式所取用。 / The purpose of this research is to provide a smartly algorithmic trading platform with financial data. I use Django CMS as a web framework and consisting of Develop environment and Trade environment. The entire functions of the platform include “User Research and Development”,” User Testing” and “Algorithmic Services”. “User Research and Development” and “User Testing” using IPython interactive development interface, with JupyterHub management and configuration, can simultaneously provide multiple user accessing and make the platform enough to support more and more users; “Algorithmic Services” using Celery to package algorithms into tasks can facilitate the delivery to the Server for distributed computing. By means of the growth of Deep Learning in recent years, the platform adds extra Tensorflow and GPU deployment to support multi-core and high-speed algorithm computing. In face of accessing large number of complex and structured financial data, I choose HAWQ as the database in this research. Its extremely massively parallel processing can alleviate the complexity of system and the bottlenecks of efficiency caused by accessing massive number of data. Combing HAWQ with Ambari can achieve the functions of creation, monitoring and management of Hadoop distributed cluster. The developers will do much more easily in deployment and maintenance. The traditional table design may not fit in with the new database HAWQ, so this research will design appropriate table, and evaluate its performance to ensure that data can be accessed effectively and quickly from programs.

作業基礎成本制度（Activity-Based Costing, ABC）為現行管理會計制度中，為較多企業所採用之制度，吳安妮教授在經過多年理論與實務之研究後，將ABC制度IT系統商品化，並與許多不同的制度整合為一體，命名為「作業價值管理系統（Activitiy Value Management System,AVMS）」，藉由作業價值管理能提供管理階層正確、即時、攸關之資訊，並協助其做出較適當之管理決策。 　　大數據（Big Data）被許多產業所使用，藉由歷史資料與未來預測，開創了新市場與新商業模式，而大數據也結合了許多制度，例如工業4.0、物聯網等，但卻沒有與管理會計相結合之研究，本研究藉由作業價值管理與產能管理之結合進行大數據之分析，初步的將管理會計與大數據結合，並同時協助個案公司發現有關產能管理之問題，並改善之。 　　本研究使用個案研究法，個案公司為一民防工程與地下空間設計之公司，藉由該個案公司所處產業之產業資料、未來趨勢、競爭者資料、作業價值管理資料等，分析找出個案公司之產能管理問題，並協助個案公司解決所發現之問題以提升管理之效率。 / Activity-Based Costing (ABC) is a well-known management accounting method and used by many companies. After professor An Wu’s 30-years research, she put Activity-Based Costing into IT system and named it Activity Value Management System (AVMS). This system provides correct and immediate information for company’s manager which can help them making a good decision. 　　Big data Analysis is used by many industry for creating new markets and new business models. Big Data Analysis combined with many systems such as Industry 4.0 and Internet of Things (IOT), but there aren’t any integration with management accounting. In this thesis we will Integrate Activity Value Management and Capacity Management with Big Data Analysis. By doing so, this can help the company reviving and solving the problem of Capacity Management. 　　The thesis is a case study with a China Basement designing company. Using the industry information, future trends, Competitor information and AVM data we can not only figure out the problem of Capacity Management that the company is facing but also help the company solving them.

資料的快速增長與變化以及分析工具日新月異，增加資料分析的挑戰，本研究希望透過一個完整機器學習流程，提供學術或企業在導入大數據分析時的參考藍圖。我們以Spark作為大數據分析的計算框架，利用MLlib的Spark.ml與Spark.mllib兩個套件建構機器學習模型，解決傳統資料分析時可能會遇到的問題。在資料分析過程中會比較Spark不同分析模組的適用性情境，首先使用本地端叢集進行開發，最後提交至Amazon雲端叢集加快建模與分析的效能。大數據資料分析流程將以微博為實驗範例，並使用香港大學新聞與傳媒研究中心提供的2012年大陸微博資料集，我們採用RDD、Spark SQL與GraphX萃取微博使用者貼文資料的特增值，並以隨機森林建構預測模型，來預測使用者是否具有官方認證的二元分類。 / The rapid growth of data volume and advanced data analytics tools dramatically increase the challenge of big data analytics services adoption. This paper presents a big data analytics pipeline referenced blueprint for academic and company when they consider importing the associated services. We propose to use Apache Spark as a big data computing framework, which Spark MLlib contains two packages Spark.ml and Spark.mllib, on building a machine learning model. This resolves the traditional data analytics problem. In this big data analytics pipeline, we address a situation for adopting suitable Spark modules. We first use local cluster to develop our data analytics project following the jobs submitted to AWS EC2 clusters to accelerate analytic performance. We demonstrate the proposed big data analytics blueprint by using 2012 Weibo datasets. Finally, we use Spark SQL and GraphX to extract information features from large amount of the Weibo users’ posts. The official certification prediction model is constructed for Weibo users through Random Forest algorithm.

隨著台海兩岸經貿往來密切，發展迅速，客觀上對銀行業提出了許多服務要求，為兩岸金融業的合作提供了廣闊的空間。本文通過採用數據包絡分析法和共同邊界成本函數比較分析了兩岸商業銀行的成本結構及效率差異，實證結果表示大陸商業銀行的成本效率要高於台灣銀行。對這一結果的可能性解釋是大陸銀行的資產規模要遠高於台灣銀行。銀行總資產越高，其獲取低投入要素價格的市場能力越強，因此生產成本更低，成本效率更高。台灣和大陸商業銀行有必要發揮自身的優勢，通過各種方式和渠道，加快兩岸銀行界合作的進程。 / With cross-strait rapid economic development and trade exchanges, huge business investments have induced a great demand for financial services and provided a broad space for cross-strait cooperation. This paper adopts data envelopment analysis and meta-frontier cost function to compare and analyze the different cost structure and efficiency of mainland and Taiwan commercial banks. The empirical results reveal that cost efficiency of mainland commercial banks is higher than Taiwanese ones, which is maybe caused by the larger bank size and total assets. The larger the size of banks, the higher the market power for reaping the benefits of low input prices, thereby resulting in a lower cost of production and a higher cost efficiency. It is necessary for mainland and Taiwan commercial banks to develop their own strengths to accelerate the process of cross-strait cooperation in the banking sector through various means and channels.

日前行政院提出施政報告時指出，將以「挑戰二OO八」作為目標，研議一項為期六年的「國家總體建設計畫」，計畫中又以「兩兆雙星」的產業政策最受矚目，計畫中，數位內容產業是其中一環，希望能在2006年達到三千七百億台幣的產值，增加四萬個以上就業機會﹔經濟部甚至將成立「數位內容學院」，幫助數位產業培養傑出人才，強化國家的競爭力，台灣更訂定2006年為數位電視元年，全面轉換現有類比電視系統，進入數位化電視時代。 所謂「數位電視」時代，泛指數位電視之節目訊號採用數位影音訊號壓縮技術（Digital Video/Audio Compression），透過數位編碼（Digital Coding）與數位調變（Digital Modulation）來傳輸，並具有結合其他數位資料一起廣播之能力。 台灣的無線電視台早在2000 年舉行試播典禮，每天有五小時的數位節目播送﹔五大有線電視多系統經營者，（Multiple System Operator，簡稱MSO），包括東森、和信、台灣寬頻通訊顧問公司、太平洋聯網科技及其他，五大系統商掌控全台將近90%的有線電視收視戶，發展數位電視也最積極﹔衛星電視台如年代，也以衛星直播網路（Direct PC）方式播送數位電視﹔連電信業者如中華電信，更於2000年中成立「互動式多媒體處」，提供用戶互動式影音服務。 本文研究以「創新行銷」、「價值擴散」之理論為主軸，主要探討數位電視相關產業，未來能否成功，或被視聽大眾所接受，本研究之主要目的如下 一、 數位內容與創新擴散模式之關係：不同之數位電視業者，提供哪些不同　　　　　　之數位內容？能吸引哪些消費大眾 二、 數位電視之系統採用與創新擴散模式之關係 三、 數位電視之價格策略用與創新擴散模式之關係 四、 數位電視之使用簡便性與創新擴散模式之關係 本研究以歷史文獻分析法為研究主軸，蒐集國內外相關文獻、書籍、報告、期刊、論文等資料來源加以整理分析，並以國內現有之數位電視業者及其發展之商品，如何進行創新擴散，加以介紹、分析、比較，進而探討出其未來可能之發展與建議。 / The administrative Planning Report passed recently by the Executive Yuan indicates that taking [Challenge 2008] as the target, plan and discuss a 6-year plan, named [National General Construction Planning], and of which the policies for [The Two Trillion, Twin Stars] industry attracts more attention and the digital industry is one of sectors in the planning. We expect the production value would be up to NTD 370 billion in 2006, and increase more than forty thousand job opportunities; even the Ministry of Economy would establish [Digital Institute], for helping educate the outstanding talents in digital industry and sharpen the national competitive edge; and the year of 2006, which was regarded as the Digital Video Year by the Taiwan government, will witness the process of changing the simulating video system into digital video era completely. What is called [Digital Video] era, it is generally referred to the program signal for digital video adopted with Digital Video/Audio Compression technique, and through Digital Coding and Digital Modulation to transmit the message, and has the capability of broadcasting combined by other digital information. Early in 2000, the trying-out broadcasting cerebration of the Radiovision Station in Taiwan was held, and the digital program was broadcasted for five hours everyday; The big five CATV Multiple System Operator (MSO) includes Eastern Television, KG Telecom, Taiwan Broadband Communication Corporation (ANET), Pacific Broadband Company Limited, and others, which control nearly 90% CATV audiences, and develop the Digital Video actively too; The satellite TV station like ERA also broadcasts the digital video by the way of Direct PC; and the communication operator like Chunghwa Telecom established “Interactive Multimedia Office” to provide the interactive video service in 2000. The theory of “Innovative Marketing” and “Value Extending” is taken as the principle in this research, discussing mainly the digital video and interrelated industries, whether it is successful or not, or accepted by the audience. The main proposes as followings: I. The relations between digital and innovative extending; will different digital video operator provide different digital programs? What kind of audience would be attracted? II. The relations between digital video system and innovative extending model III. The relations between the price strategy of digital video and innovative extending model IV. The relations between the convenience of digital video and innovative extending model This research took historic literature analysis as its researching principle, based on the collection and analysis of interrelated literature, book, report, periodical and article etc. information in domestic and oversea, and introduce, analyze and compare how they innovate and extend upon the existing products developed by the digital video operator in domestic, and further discuss its future orientation and suggestion.