Learning Outcomes
1. Identify data structures and models World Wide Web
2. Construct and analyze Web data models and understand data relevance metrics i
3. Learning important Web properties, attributes and algorithms to find and recognize common patterns, behaviors, and Web clusters
Course Content (Syllabus)
• Introduction to the basic concepts of information and data management within World Wide Web.
• Data types on the web and representation.
• World Wide Web Structure and Graph Model.
• World Wide Web Performance Metrics and Information Sharing Techniques.
• Information aggregation techniques in Social Networks.
• Sentiment analysis and behavioral analytics on the Web.
• Recommendations in Social Media and New Collaborative Web Environments.
Keywords
Web mining, Web data models, data mining, data analytics algorithms
Course Bibliography (Eudoxus)
● B. Liu: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications), Springer, 2011.
● R. Zafarani et al : Social Media Mining, Cambridge University Press, 2015
● Isoni, Andrea. Machine Learning for the Web. Packt Publishing Ltd, 2016.
● Easley, David, and Jon Kleinberg. Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge University Press, 2010.
● Α. Βακάλη, Ζ. Παπαμήτσιου: Πληροφοριακά Συστήματα Παγκόσμιου Ιστού, Εκδόσεις Νέες Τεχνολογίες, 2012.
● A. Vakali and G. Pallis: Web Data Management Practices, Idea Group Publishing, 2007.
● Published papers in scientific journals and conferences.