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DIP3 Project

Computer Science Department, University of Ioannina

College of Computing, Georgia Institute of Technology

DIP3

FP7 People IOF Action
The 3Ps of Distributed Information Delivery: Preferences, Privacy and Performance

Knowledge Transfer

Besides broad dissemination of results through publications to prestigious conferences and journals, participation to local fora and workshops and organization of Departmental seminars, DIP3 has achieved to re-organize and offer undergraduate and graduate courses aimed at developing preference, privacy and performance aware curricula at both the graduate and undergraduate level.

Courses

  • Data Privacy in Social Networks: graduate offered in spring semester of academic year 2010-2011 and in fall semester of the academic year 2011-2012. Offered in cooperation with Dr Panos Vassiliadis. The course treats fundamental concepts of privacy in data management, different approaches for achieving privacy (k-anonymity, l-diversity, t-closeness, etc), global vs local recoding, privacy for multi-relational data collections and privacy in data-centric applications. For more information the reader is referred to the course homepage.
  • Database Systems: undergraduate core course offered in fall semester of academic year 2010-2011 and in fall semester of the academic year 2011-2012. The course treats preliminaries of database management systems, data models, conceptual model, entity- relationship model, relational model, relational algebra, relational calculus, database languages, SQL, QBE, query optimization, dependency theory, database design theory, Schema normalization, transaction management, concurrency control, recovery, database security. For more information the reader is referred to the course homepage.
  • Data mining: undergraduate course offered in spring semester of academic year 2010-2011. The course covers the basic principles, algorithms and applications related to mining large data sets. Topics covered include: preliminaries, classification, clustering, association rule mining, handling incomplete information, visual data mining, data mining tools, web mining, summaries, privacy issues, recommendations and preferences, performance issues. For more information the reader is referred to the course homepage.

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