開課年級: 大四 研究所 (英語授課 中文輔助)
<br>學分數: 3
<br>課程目標: be familiar with programming platform, tool-kits and algorithms for cloud
<br>computing and data processing
<br>先修科目: None
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<br>課程大綱:
<br>1. Introduction of Cloud Computing
<br>2. File System: HDFS, GFS (Google File System)
<br>3. MapReduce: Hadoop
<br>4. Text Retrieval Algorithms
<br>5. Graph Algorithms
<br>6. MapReduce & Database: BigTable, Hive, Pig
<br>7. Google App Engine & Microsoft Azure
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<br>指定用書: Data-Intensive Text Processing with MapReduce, Jimmy Lin and Chris Dyer. Morgan & Claypool Publishers, 2010.
<br>參考書籍: 
<br>A. 雲端程式設計 入門與應用實務
<br>B. Hadoop: The Definitive Guide, O'Reilly, 2009
<br>C. Paper reading:
<br> 1. Jeffrey Dean and Sanjay Ghemawat. (2004) MapReduce: Simplified Data Processing on Large Clusters. Proceedings of the 6th Symposium on Operating System Design and Implementation (OSDI 2004), pages 137-150.
<br> 2. Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung. (2003) The Google File System. SOSP-03, pages 29-43.
<br> 3. Michael Stonebraker, Daniel Abadi, David J. DeWitt, Sam Madden, Erik Paulson, Andrew Pavlo, and Alexander Rasin. (2010) MapReduce and Parallel DBMSs: Friends or Foes? Communications of the ACM, 53(1):64-71.
<br> 4. Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Michael Burrows, Tushar Chandra, Andrew Fikes, and Robert Gruber. Bigtable: A Distributed Storage System for Structured Data. OSDI 2006, pages 205-218.
<br> 5. Christopher Olston, Benjamin Reed, Utkarsh Srivastava, Ravi Kumar, and Andrew Tomkins. Pig Latin: A Not-So-Foreign Language for Data Processing. Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pages 1099-1110.
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<br>教學方式:
<br>   講義授課上機實驗
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<br>教學進度:
<br>   一周一個講義或實驗
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<br>成績考核
<br>   Lab & Program Homework: 70%
<br>	LAB1: Hadoop: HDFS & HBase
<br>	LAB2: MapReduce: Text Processing
<br>	LAB3: Google App Engine: Database & Web API
<br>	LAB4: Azure: SQL Integration	
<br>   Final Projects: 20%
<br>   Course Participation & Quiz: 10%