Knowledge Extraction in Data WarehousesPermalink

ObjectivesPermalink

• Present the terminology and general concepts related to the domain of knowledge extraction in database systems, with particular emphasis on data warehousing systems.
• Know how to justify the implementation of a knowledge extraction system in an organization as a fundamental element for a decision support system.
• Know and apply the knowledge extraction life cycle within an organization.
• Know and apply, according to their characteristics, the different techniques and models of knowledge extraction and data mining in concrete application cases.
• Know how to justify, design and implement a knowledge extraction system using a data warehousing system as data source.

ProgramPermalink

• Introduction to knowledge extraction systems.
• Knowledge discovery life cycle.
• Introduction to machine learning and data mining.
• Data preparation and pre-processing.
• Languages and architectures for data mining in data warehouses.
• Data classification and prediction techniques and models.
• Mining association rules and sequences.
• Generation and analysis of clusters.
• Mining complex data types.

BibliographyPermalink

• Golfarelli, M., Rizzi, S., Data Warehouse Design: Modern Principles and Methodologies, McGraw-Hill Osborne Media; 1st Edition, May 26, 2009.
• Bhatia, P., Data Mining and Data Warehousing: Principles and Practical Techniques, 1st Edition Cambridge University Press; 1st edition, June 27, 2019.
• Linoff, G., Berry, M., Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, 3rd Edition, Wiley, April 12, 2011. ISBN-10 ‏ : ‎ 0470650931, ISBN-13 ‏ : ‎ 978-0470650936.
• Witten, I., Frank, E., Hall, M., Pal, C., Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) 4th Edition, Morgan Kaufmann, December 1, 2016. ISBN-10 ‏ : ‎ 0128042915, ISBN-13 ‏ : ‎ 978-0128042915.