Ateneo - UPO

Methodologies and techniques to realize a modern Business Intelligence system and process management

Z. Michalewicz et al., Adaptive Business Intelligence, Springer.

Students must acquire the following knowledge, competences, and abilities: Having understood notions of artificial intelligence and machine learning, with particular focus on methods and techniques for: 1. knowledge management 2. prediction 3. optimisation 4. adaptation described in the lessons; being able to apply such notions in answering theoretic questions as well as exercises.

Database fundamentals

Direct lessons in classroom. Classroom lessons will also include example questions or exercises useful for the final test. Example tests can also be provided to students. Slides, textbooks indications and additional material will be provided also through the DIR platform. In this way, the students who do not attend will be allowed to easily follow the course progression.

Written test. The test will be composed by three questions at least, focused on different course topics chosen among knowledge management, prediction, optimization, adaptation. The final score will take into account the partial scores of the different questions (e.g., calculating the average). Possibly (e.g., on student's request), the same exam type will be executed orally.

The subject will be organised in four parts, each one referring to the realization of a specific module in a modern Business Intelligence tool architecture: 1. knowledge management: implicit and explicit knowledge; Rule-based Reasoning and Case-based Reasoning; advanced Case-based Reasoning solutions (time series data, fuzzy CBR, process-oriented CBR); 2. prediction: classification, regression, time series; mathematical models, distance-based models, logic models, heuristic models; hybrid methods; 3. optimisation: mathematical methods, evolutionary algorithms, ant systems, hybrid methods; 4. adaptation: techniques to improve the prediction module performances.

Knowledge and comprehension: students must acquire a deep knowledge about: methodologies and techniques for: knowledge representation and management, prediction, optimization, adaptation. Capacity to apply knowledge and comprehension: ability to evaluate and/or apply the techniques studied in the course to design a decision support tool in an enterprise context, where heterogeneous data and knowledge may be available; Judgement autonomy: ability to work in an autonomous way, and deal with complex enterprise situations or with incomplete information Communication capabilities: They must be able to justify their design or implementation choices and clearly communicate them also to a non expert audience. Learning capacity: students must acquire the capability of describing and properly choosing the correct techniques to realize an enterprise decision support system