Decision Support Systems & Knowledge Management

Decision Support Systems & Knowledge Management

A. Decision Support Systems

Academic Assistant: Dr. Christos Berberidis
Research Assistants:

Dr. Fotios Kokkoras

Mr. Konstantinos Paraskevopoulos

Courses Covered: Decision Support Systems


The purpose of this virtual lab is to develop interactive training material on Decision Support Systems. The educational material will be developed in the form of training workshops/ experiments based on specialized software that will allow the learner:

  • understand in a practical manner the relevant theoretical knowledge through analytical solved problems that will highlight the current issues of theory
  • to check the degree of theory understanding, by solving problems and exercises

The above activities will be conducted using specialized software that will either be developed by the scientific team or free, open source popular software tools and platforms that will be evaluated and selected to be used.

In particular, the delivered software will provide an interactive opportunity for students to experiment and to deepen in a supervisory manner to topics that deal with. In addition, it will allow the teacher to create additional teaching material to meet possible future needs.


The thematic contents of the deliverable software to create training workshops are:


1. Simple decisions under certainty: Multicriteria Methods

Software and training workshops that will be developed will allow students to interact with the following multicriteria methods:

  • Weighted Average Sum (WAS),
  • ELECTRE methodology
  • Analytic Hierarchy Process (AHP)




2. Simple Decisions under ignorance

Software and training workshops will be delivered for the following decision rules:

  • MaxiMax / MiniMin,
  • MiniMax / MaxiMin the Wald,
  • Hurwicz rule,
  • MiniMax Regret Savage rule


3. Simple Decisions under uncertainty

Will be delivered software and training workshops on:

  • Bayesian Networks
  • Decision Trees (Trees sequential decisions)
  • Game Theory




4. Decision making through data analysis

Software and training workshops will cover:

  • Classification trees
  • Neural Networks
  • Genetic Algorithms
  • Clustering
  • Association Rules





B. Knowledge Management

Academic Assistant: Dr. Christos Berberidis
Research Assistants:

Mr. Efstratios Kontopoulos

Ms. Kalliopi Kravari

Courses Covered: Knowledge Management, Decision Support Systems (partial), Web Information Systems (partial)


This virtual laboratory involves a wide variety of task-oriented exercises grouped by escalating difficulty levels. Students will progressively proceed through the material with the help of popular free third-party software tools as well as rich Graphical User Interfaces (GUIs) that will be implemented for the purposes of the course.

The aim is to allow the students to get acquainted with modern (web) Knowledge Management (KM) technologies, practice the theoretical aspects taught in class and progressively develop a practical, real-life Web application (e.g. “Amazon-like” e-commerce web site).




The following topics will be covered:

  • XML (Extensible Markup Language), which is a set of rules for encoding documents electronically. More specifically, students will practice with: (a) The XML Syntax and the three main XML building blocks, (b) DTD and XML Schema, (c) XML namespaces, (d) XML querying via XPATH and (e) XML processing via XSLT.
  • RDF (Resource Description Framework), a standard model for data interchange on the Web. More specifically, students will focus on: (a) The RDF triple-based model of statements, (b) RDF Schema (RDF/S), (c) RDF querying via SPARQL, and (d) RDF processing/parsing via Jena, a Java library for processing RDF and RDF/S documents.
  • OWL (Web Ontology Language), a family of knowledge representation languages for authoring ontologies. Users will get familiarized with: (a) The OWL syntax, which heavily builds on RDF and XML, (b) The three increasingly expressive OWL sublanguages (OWL Lite, OWL DL, OWL Full), (c) The basic notions of ontologies and ontology engineering, the main methodologies for manually and (semi)automatically constructing ontologies as well as reusing existing ontologies and the most widely-used software tools for these tasks.





  • Logic & Inference, the study of reasoning and the process of drawing a conclusion by applying logical clues. Users will get familiarized with: (a) Deductive rules, (b) Production rules, (c) Monotonic rules, (d) Non-monotonic rules.


  • Knowledge and Ontology Engineering. Users will get familiarized with: (a) Knowledge-based systems, especially with their development cycle and architecture, (b) knowledge elicitation, (c) manually constructing ontologies, (d) re-using existing ontologies, (e) using relevant methodologies, semi-automatic methods and popular tools.
  • Knowledge search. Users will get familiarized with semantic search and knowledge portals by using popular SW Search Engines, such as Semantic Web Search Engine (SWSE), Sindise, Watson, Yahoo! Microsearch, Falcons, Swoogle, Semantic Web Search and Zitgist Search.





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