DESCRIPTION

TOPICS TO BE COVERED

1. Database topics:
   • Traditional databases: Advanced hashing and multi-key access methods, for main-memory and for disk-based data.
   • Text databases:  indexing text and DNA strings, clustering, information filtering, LSI (singular value decomposition).
   • Multimedia databases:  Searching by content in signals: Time  sequences, photographs and medical images, video clips, feature extraction,  continuous media storage and delivery.
2. Tools:
   • Fundamental signal processing methods: Discrete Fourier Transform, wavelets, JPEG and MPEG compression.
   • Singular Value Decomposition: revisited
   • Fractals in databases: Self-similarity/non-uniformity of real datasets, fractal dimensions, selectivity using fractals and multifractals, fractal image compression, self-similarity in web-traffic patterns.
3. Data Mining:
   • Graph mining: ``Laws'' in large graphs (power laws; 'small world' phenomena); graph generators; social networks.
   • Sensor and time series mining: linear and non-linear forecasting
   • Review of Statistical methods,
   • Review of AI-methods,
   • Database methods - Massive datasets: Association rules; Frequent sets;  Single-pass learning algorithms;   Information compression and reconstruction; Sampling; Condensed data representations; Datacubes; Cube-trees; Function finding.
   • Security and Privacy Protection.
   • Visualization of large data sets
   • More tools: approximate counting algorithms; Independent Component Analysis.
4. OVERVIEW OF RECENT TOPICS: trust and influence propagation; Future directions.

UNIVERSITY UNITS: 12

CORE UNITS: 1

TEXT

• Christos Faloutsos, Searching Multimedia Databases by Content, Kluwer Academic Press, 1996.
• Deepayan Chakrabarti and Christos Faloutsos Graph Mining: Laws, Tools and Case Studies, Morgan Claypool, 2012.

• William H. Press, Saul A. Teukolsky, William T. Vetterling and Brian P. Flannery, Numerical Recipes in C, Cambridge University Press, 1992, 2nd Edition.
• Raghu Ramakrishnan, Johannes Gehrke, "Database Management Systems," McGraw-Hill 2002 (3rd ed).
• Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, 3rd edition, 2011

METHOD OF EVALUATION

• A midterm (20%)
• Homeworks (10%)
• A Project (40%)
• A Final exam (30%)

• Some homeworks will be indicated as mandatory, that is, a passing grade is necessary.
• Projects will be carried out in teams of 3. A detailed handout about the project will be distributed at the beginning of the course, along with a list of suggested projects. The goal of the project is to give the participants the opportunity to tackle a large, interesting problem, which may lead to a publication.