Overall objectives:

1 - With the recent developments in Information Technologies we find systems collecting and generating vast amounts of data. For example, projects related with the human genome, global organizations and the World Wide Web easily generate Gigabytes or Terabytes of data. So that such data can be useful and understood, they need to be target of mining so that useful information can be extracted. Data warehousing and data mining techniques enable the analysis of huge data sets, providing useful information for decision taking. This is a multi-disciplinary area, aggregating several research areas such as database system management, data warehousing, OLAP, statistics, artificial intelligence and algorithms. The general objectives are: studying principles, algorithms, and application of the various decision support systems; gaining practical experience in the implementation and/or configuration of these systems.

Syllabus:

1 - Data warehousing and OLAP technology for data mining, pre-processing, concept description: characterization and comparison, classification and prediction, decision trees, association rules mining, bayesian classification. Data mining with: clustering, neural networks, genetic algorithms; mining spatial data, text mining, audio mining, web mining. Using the Business Intelligence suite Pentaho (http://www.pentaho.com/) - the most renowned collection of open source (enterprise/commercial version also available) business intelligence components for data warehousing, data mining and reporting.

Literature/Sources:

Micheline Kamber, Jian Pei, Jiawei Han , 2011 , Data Mining: Concepts and Techniques, 3rd Edition , O'REILLY

Assesssment methods and criteria:

Classification Type: Quantitativa (0-20)

Evaluation Methodology:
1) 3 oral exams or written mini-tests: 30% (individual) 10% each - answer to questions of theoretical and practical nature regarding the given subjects in theory classes, being the class slides the main base for the questions/answers. 2) 2 Projects (in groups): Research Project (RP) - 20% - Writing a research paper resulting from research on the Internet regarding a topic related with the syllabus and final defense of the project in a public presentation with slides related with the paper and answering of questions posed by the teacher/audience; Business Intelligence Project (BIP) - 30% - involving various components of the Business Intelligence Pentaho suite and final defense by presentation of the features implemented and answering of questions about the implementation. 3) Continuous evaluation: 20% (individual) in TP classes, to providing answers and critical observations based on the practical work being executed, and the relation with the respective theoretical subjects.