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MINF4533: Networks and Business Intelligence

MINF4533: Networks and Business Intelligence. Prof. Dr. Daning Hu Department of Informatics University of Zurich Sep 18th, 2012. Daning Hu: A Network Perspective. ?. Daning Hu. studied at. collaborates. John. works at. collaborates. collaborates. advises. advises. Jiaqi Yan.

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MINF4533: Networks and Business Intelligence

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  1. MINF4533: Networks andBusiness Intelligence Prof. Dr. Daning Hu Department of Informatics University of Zurich Sep 18th, 2012

  2. Daning Hu: A Network Perspective ? Daning Hu studied at collaborates John works at collaborates collaborates advises advises Jiaqi Yan Dr. J. Leon Zhao Jiaqi Jackie Yan, 29, Male, Chinese 2 Social Network Analysis Financial Intelligence

  3. MINF 4533: Networks and Business Intelligence • Lecturer: Prof. Dr. Daning Hu • Teaching Assistant: Dr. Jiaqi Yan • Email: hdaning@ifi.uzh.ch (In all emails to me, please put MINF 4533 in the subject line. This can me get to you faster). • Credits: 4.5 ECTS credits • Class Meetings: Tuesday 13:30-15:00 PM • Tutorials: Thursday 10:00 – 11:30 AM • Language: English • Audience: Undergraduate and Master students • Office Hours: 15:00 -16:00, email for appointment, Room 2.A.12 • http://www.ifi.uzh.ch/bi/teaching/fall2012/lecture.html

  4. Course Plan • This lecture mainly consists of two stages. • 18.09 to 18.10: the lecturer will introduce the basics of network modeling and analysis techniques. The students will also gain hands on experiences in tutorials. 23.10 to 08.11: the students will work and finish the midterm project. • 13.11 to 06.12: the lecturer will introduce the various business intelligence applications based on network modeling and analysis. 11.12 to 08.01.13: the students will work and finish the final project.

  5. Grading and Course Goals • 1. Two course projects. One midterm and one final project (each taking 40%, total is 80%) • 2. Active participation and interaction during the lectures and tutorials (20%) • The project reports should include the following four major components: • Network/Relational Data Collection (7.5%) • Network Data Processing and Modeling (10%) • Network Visualization (7.5%) • Network Analysis (15%)

  6. Example 1: Network Data Collection • Social Networks: Online communities, Social networking websites, Personal blogs and micro-bloggings, online video sharing websites. • E-Business: Amazon Web Service, Ebay Data API, Taobao. • Others: Financial, Governmental data sources, etc.

  7. Example 2: Network Data Processing Modeling • Extract relations/links from raw data in database tables • Model such relations/links into network data. • Node data • Link data

  8. Example 3: Network Visualization

  9. Example 4: Network Analysis • A microblogging Network: Who possesses the most advantageous position in broking information and knowledge in this network?

  10. A Global Terrorist Network • How to effectively break down terrorist networks?

  11. Network-based Business Intelligence Applications • Recommendation Systems:

  12. Network-based Business Intelligence Applications • Reputation Systems:

  13. Network-based Business Intelligence Applications • Social Media based Marketing, Word-of-Mouth Effect

  14. Computing tools required for this course • Database Management Software • MySQL or other common DBMS such as MS SQL Server, Oracle, etc. • Network Visualization Tool: NetDraw, FNA (Financial Network Analyzer), etc. • Network Analysis Tool: UCINet, FNA • Microsoft Windows OS Only

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