Storage, retrieval, analysis and mining from huge amount of data is a challenging topic that has made significant impact in several domains in both industry and academia. This lecture will cover the basic concepts involved in the analysis of the so called "Big Data" as well as examples of typical tasks that can profit from it.

The course will cover the following topic areas:

• Large Scale distributed file systems and data storage frameworks
• Computational models for large scale data (e.g. MapReduce and GraphLab)
• Data Stream analysis
• Statistical learning techniques for large scale data
• Large Scale Recommender systems
• Link analysis

Literature:

1. Anand Rajaraman, Jure Leskovec, and Jeffrey Ullman, Mining of massive datasets
2. Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos Guestrinand Joseph M. Hellerstein (2012).
3. "Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud." PVLDB.