AGRICULTURAL
UNIVERSITY OF ATHENS
Department of Rural
Agricultural Economy

Business Intelligence Systems

Content

Theory
1. A survey of relational Databases and of Data Warehouses. Design and implementation of Data Warehouses.
2. Examples of data integration and data interchange scenarios. Extraction process, transformation process and ETL data insertion process. Dealing with transformation problems and missing information in Databases.
3. Star schemata and snowflake schemata. Dimension Tables and Fact Tables. Multidimensional data models – hypercubes. On-Line Analytical Processing (OLAP) and relevant operations.
4. Data visualization
5. Methods and techniques of data mining.
6. Classification (classification models, types and evaluation of classifiers).
7. Clustering (clustering concept, set of basic clustering algorithms).
8. Basic Association Rules.
Laboratory
1. Open-source software for creating and managing Data Warehouses.
2. Open source software for OLAP.
3. Use of tools for data mining (WEKA, Analysis Services).

Learning results

Upon successful completion of the course the student will be able to:

  • Understand the meaning and characteristics of an intelligent system,
  • Understand the concept of an intelligent training system,
  • Understand and recognize the differences between OLTP Databases and Data Warehouses and create Data Warehouses according to the steps of the ETL (Extract, Transform and Load) process.
  • Diagnose problems that have occurred as a result of the data integration and transformation.
  • Create well structured dimension tables and fact tables and use them to create star and snowflake schemata.
  • Distinguish and choose the most appropriate method for knowledge extraction through a large number of data,
  • Acquire the necessary skills to exploit ready-made tools for data mining , in order to develop an intelligent system,

Combine results of classification, clustering and association rules and will be able to end up with the production of new knowledge

Bibliography

- Proposed literature:
1. Tan Pang - Ning, Steinbach Michael, Kumar Vipin, Introduction to Data Mining, 2nd Edition, 2018, A. TZIOLA & SONS PUBLICATIONS SA, Athens (in Greek).
2. ΑL. NANOPOULOS, G. MANOLOPOULOS, INTRODUCTION TO DATA MINING AND DATA WAREHOUSES, 2008, PUBLICATIONS OF NEW TECHNOLOGIES Ltd (in Greek).
3. Kyrkos E. Business Intelligence and Data Mining (Book Code in Eudoxus: 320088) Version: 1/2016 ISBN: 978-960-603-109-0. E-book. Publisher: Greek Academic Electronic Textbooks - "Kallipos" Repository (in Greek).
4. Stalidis George, Kardaras Dimitris. Data management and business intelligence. (Book Code in Eudoxus: 320080). Edition: 1/2016. ISBN: 978-960-603-398-8. Electronic Book, Publisher): Greek Academic Electronic Books - "Kallipos" Repository (in Greek).
-Related scientific journals:
1. DATAMINE - Data Mining and Knowledge Discovery
2. IDA - Intelligent Data Analysis
3. IJDWM - International Journal of Data Warehousing and Mining
4. MLDM - Transactions on Machine Learning and Data Mining

Faculty

2105294888
stefanos@aua.gr
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NEWSLETTER

It is the only Greek University Department with the objective of training agroeconomists able to meet the demands of this new period in Greek agriculture which was inaugurated with the entry of Greece into the E.U.
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