School of Science and Technology Faculty

Dr Christos Berberidis

Course(s):Decision Support Systems
Advanced Database Systems

 

Dr Christos Berberidis

Christos Berberidis received his PhD from the Department of Informatics of Aristotle University of Thessaloniki. He holds a BSc. from the Department of Informatics of Aristotle University (1998) and an MSc. in Information Systems Engineering from University of Manchester, UK (1999) (UMIST).

His research has been focused on Data Mining and Machine Learning, mostly on temporal and sequential data. He is also working on data mining in biological data, for the prediction of functional sites in genomic sequences. He is the author of several papers in scientific journals and conference proceedings.

Christos has worked as a visiting researcher at the Department of Computer Sciences of Purdue University (IN, USA). For the past 2 years he has been working as an Adjunct Lecturer at the Department of Informatics, of Aristotle University, teaching basic and advanced programming techniques. He has participated in many R&D projects and has also significant experience as a research consultant and project manager in the private sector.

 

List of Publications

Journals

  1. E. Kontopoulos, C. Berberidis, T. Dergiades, N. Bassiliades, “Ontology-based Sentiment Analysis of Twitter Posts”, Expert Systems with Applications, (in press, available online January 2013)
  2. C. Berberidis, I. Vlahavas, "Mining for weak periodic signals in time series databases", Journal of Intelligent Data Analysis, Volume 9(1), IOS Press, 2005
  3. C. Berberidis and I. Vlahavas, “Detection And Prediction Of Rare Events In Transaction Databases”, International Journal of Artificial Intelligence Tools (IJAIT), World Scientific, 16(5), pp. 829 – 848, 2007
  4. G. Tzanis and C. Berberidis, “Mining for Mutually Exclusive Items in Transaction Databases”, International Journal of Data Warehousing and Mining, David Taniar (Ed.), Idea Group Publishing, 3(3), 2007
  5. Tzanis G., Berberidis C. and Vlahavas P. I., "StackTIS: A Stacked Generalization Approach for Effective Prediction of Translation Initiation Sites", Computers in Biology and Medicine, Elsevier, November 2011

 

Book chapters

  1. G. Tzanis, C. Berberidis, I. Vlahavas, "Machine Learning and Data Mining in Bioinformatics", Handbook of Research on Innovations in Database Technologies and Applications: Current and Future Trends, Viviana E. Ferraggine, Jorge H. Doorn, and Laura C. Rivero (Eds.), IDEA Group Publishing, 978-1-60566-242-8, February 2009.


Conferences

  1. C. Berberidis, A. G. Walid, M. Atallah, I. Vlahavas and A. K. Elmagarmid, "Multiple and Partial Periodicity Mining in Time Series Databases", Proc. 15th European Conference on Artificial Intelligence (ECAI 2002), pp. 370-374, Lyon, France, 2002, IOS Press, pp.370-374
  2. C. Berberidis, I. Vlahavas, W. G. Aref, M. Atallah and A. K. Elmagarmid, "On the Discovery of Weak Periodicities in Large Time Series", Proc. 6th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'02), Helsinki, Finland, August 2002, Springer-Verlag, LNAI, vol. 2431, pp.51-61
  3. C. Berberidis, I. Vlahavas, "Periodicity Mining in Industrial Data: A Real World Example on Power Data", Proc. First International Conference for Mathematics and Informatics for Industry (MII 2003), Thessaloniki, Greece, April 14-16 2003
  4. C. Berberidis, L, Angelis, I. Vlahavas, "Inter-transaction Association Rules Mining for Rare Events Prediction", In Proc. (companion
    volume) 3rd Hellenic Conference on Artificial Intelligence (SETN '04), Samos, Greece, 2004
  5. C. Berberidis, L, Angelis, I. Vlahavas, "PREVENT: An algorithm for mining inter-transactional patterns for the prediction of rare events", Proc. 2nd European Starting AI Researcher Symposium (STAIRS' 04), IOS Press, Valencia, Spain, 23-24 August 2004
  6. C. Berberidis, G. Tzanis, “Mining for Contiguous Frequent Itemsets in Transaction Databases”, IEEE Third International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS'2005), Sofia, Bulgaria, September 5-7, 2005
  7. G. Tzanis, C. Berberidis, A. Alexandridou, I. Vlahavas, "Improving the Accuracy of Classifiers for the Prediction of Translation Initiation Sites in Genomic Sequences", In Proc. 10th Panhellenic Conference on Informatics (PCI'2005), P. Bozanis and E.N. Houstis (Eds.), Springer-Verlag, LNCS 3746, pp. 426-436, Volos, Greece, 11-13 November 2005
  8. G. Tzanis, C. Berberidis, and I. Vlahavas, "On the Discovery of Mutually Exclusive Items in a Market Basket Database", In Proc. 2nd ADBIS Workshop on Data Mining and Knowledge Discovery (ADMKD 2006), Thessaloniki, Greece, September 6, 2006
  9. G. Tzanis, C. Berberidis and I. Vlahavas, “A Novel Data Mining Approach for the Accurate Prediction of Translation Initiation Sites”, In Proc. 7th International Symposium on Biological and Medical Data Analysis (ISBMDA 2006), Springer LNCS, Thessaloniki, Greece, December 7-8, 2006
  10. G. Tzanis, C. Berberidis, I. Vlahavas, “MANTIS: A Data Mining Methodology for Effective Translation Initiation Site Prediction”, Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Lyon, France, 2007.


Encyclopedias

  1. G. Tzanis, C. Berberidis, I. Vlahavas, "Biological Data Mining" Encyclopedia of Database Technologies and Applications, Laura C. Rivero, Jorge H. Doorn and Viviana E. Ferraggine (Eds.), IDEA Group Publishing, April 2005.

 

 

 

Decision Support Systems

Teaching Hours and Credit Allocation: 30 Hours, 6 Credits
Course Assessment: Exam & Coursework


Aims

The course aims to provide students with concepts behind decision making and analytical and applied knowledge, for the development of decision support systems (DSSs) for various applications of current interest. The course will discuss the basic stages of the decision-making process and will expose a set of mathematical tools and techniques that may be used at the “core” of a DSS. Additionally, the course will cover Knowledge Discovery in Databases (KDD) as a set of computational tools and technologies which can provide valuable assistance to the decision maker. Students will learn how to apply various decision support technologies for solving practical decision problems and how to use or develop simple decision-support systems.


Learning Outcomes
By the end of this course you should be able to

  • Introduce the basic concepts of decision support systems.
  • Develop skills on a broad range of decision making problems.
  • Understand how different mathematical and analytical tools can contribute to the decision making process.
  • Model complex problems.
  • Organise and process efficiently any knowledge, either given a priori, or extracted, that will be assisting in the decision making process.
  • Employ basic KDD techniques in order to extract knowledge from databases
  • Identify the basic components and special characteristics of a decision problem and develop a solution.


Content

  • Introduction to DSSs
  • Making simple decisions under uncertainty
  • Decision Trees and applications
  • Introduction to Utility Theory and Dynamic Programming
  • Introduction to Games & Strategies.
  • Decision making via KDD
  • Classification
  • Clustering
  • Association rules
  • KDD tools and result evaluation

Advanced Database Systems

Teaching Hours and Credit Allocation: 30 Hours, 6 Credits
Course Assessment: Exam & Coursework

 

Aims

The course aims to familiarise students with contemporary database systems, as well as emerging database technologies. It discusses basic aspects of advanced database techniques and exposes tools and technologies that can be used along with “core” database systems. Students are expected to engage in practical database system design through a series of assignments and coursework. The emphasis in the lectures will be on general concepts and theoretical foundations. In addition to the theoretical concepts, the course will require students to use commercial database systems and develop a class project.

 

Learning Outcomes

Upon successful completion of this course students will be able to:

  • Develop the logical model of a relational database
  • Use essential SQL tools to program commercial database systems
  • Understand advanced concepts of database management and architecture
  • Organize, store and process data efficiently, using contemporary methods.
  • Understand and apply emerging technologies, including Data Mining, Information Retrieval and XML.
  • Undertake a practical database management project.

 

Content

  • ER model, relational model, mapping ER to relational model and basic SQL
  • Indexing, query processing and optimization
  • Parallel, Distributed and Spatial databases and spatial query processing
  • Hadoop ecosystem and mapreduce
  • Data Warehousing and OLAP
  • Data Mining and Business Intelligence
  • Information Retrieval, Web Search and XML

 

Reading

Elmasri R., Navathe S. B., (2010), Fundamentals of Database Systems: Global Edition, 6th Edition, Pearson.

Garcia-Molina H., Ullman J., and Widom J., (2009), Database Systems: The Complete Book, 2nd edition, Pearson.

Silberschatz A., Korth H., and Sudarshan S., (2010), Database System Concepts, 6th Edition, McGraw-Hill.

Ramakrishnan R, Gehrke J. (2002), Database Management Systems, 3rd edition, McGraw-Hill Science/Engineering/Math.