Michailidis Panagiotis
  • +30 2310 891.436
  • pmichailidis uom.edu.gr
  • Office: ΚΖ, 235

    Michailidis Panagiotis

    Professor
    Department of Balkan, Slavic & Oriental Studies


    Academic Area

    Computational Methods - Informatics

    Curriculum Vitae
    Academic Titles

    - BSc in Applied Informatics, University of Macedonia (1998)
    - PhD in Applied Informatics, University of Macedonia (2004)

    Research Interests
    Data Science 
    Computational Methods for Information Retrieval
    Digital Humanities
    Problem Solving Environments
    Parallel and Distributed Computing

    Teaching


    • DIGITAL SOCIAL RESEARCH METHODS
      (ΒΣΑ312-ΙΙI)

    Type
    ELECTIVE

    Department Abbreviation
    BSO

    Department
    DEPARTMENT OF BALKAN, SLAVIC AND ORIENTAL STUDIES

    Course Outlines

    COURSE OUTLINE

    (1) GENERAL

    SCHOOL

    ECONOMIC AND REGIONAL STUDIES

    ACADEMIC UNIT

    BALKAN, SLAVIC AND ORIENTAL STUDIES

    LEVEL OF STUDIES

    UNDERGRADUATE

    COURSE CODE

    ΒΣΑ312-ΙΙI

    SEMESTER

    Z

    COURSE TITLE

    DIGITAL SOCIAL RESEARCH METHODS

    INDEPENDENT TEACHING ACTIVITIES
    if credits are awarded for separate components of the course, e.g. lectures, laboratory exercises, etc. If the credits are awarded for the whole of the course, give the weekly teaching hours and the total credits

    WEEKLY TEACHING HOURS

    CREDITS

    Lectures and laboratory exercises

    4

    5

     

     

     

     

     

     

    Add rows if necessary. The organisation of teaching and the teaching methods used are described in detail at (d).

     

     

    COURSE TYPE

    general background,
    special background, specialised general knowledge, skills development

    Skills development

    PREREQUISITE COURSES:

     

    No

    LANGUAGE OF INSTRUCTION and EXAMINATIONS:

    Greek

    IS THE COURSE OFFERED TO ERASMUS STUDENTS

    No

    COURSE WEBSITE (URL)

    https://openeclass.uom.gr/courses/BSO259/

               

    (2) LEARNING OUTCOMES

    Learning outcomes

    The course learning outcomes, specific knowledge, skills and competences of an appropriate level, which the students will acquire with the successful completion of the course are described.

    Consult Appendix A

    • Description of the level of learning outcomes for each qualifications cycle, according to the Qualifications Framework of the European Higher Education Area
    • Descriptors for Levels 6, 7 & 8 of the European Qualifications Framework for Lifelong Learning and Appendix B
    • Guidelines for writing Learning Outcomes

    The aim of the course is to acquire skills for the implementation of social research using computational methods and tools.

     

    After successful completion of the course, students are expected to be able to:

    • Understand the concepts and principles of digital methods in social research.

    • Collect data from the Internet and/or social media in an automated way, clean, manipulate and visualize the data as well as analyze textual data and social networks.

    • Use appropriate software tools for data exploration and analysis.

    • Plan and implement a research study in the social and economic field of research using computational methods.

    General Competences

    Taking into consideration the general competences that the degree-holder must acquire (as these appear in the Diploma Supplement and appear below), at which of the following does the course aim?

    Search for, analysis and synthesis of data and information, with the use of the necessary technology

    Adapting to new situations

    Decision-making

    Working independently

    Team work

    Working in an international environment

    Working in an interdisciplinary environment

    Production of new research ideas

    Project planning and management

    Respect for difference and multiculturalism

    Respect for the natural environment

    Showing social, professional and ethical responsibility and sensitivity to gender issues

    Criticism and self-criticism

    Production of free, creative and inductive thinking

    ……

    Others…

    …….

    Search for, analysis and synthesis of data and information, with the use of the necessary technology

    Adapting to new situations

    Decision-making

    Working independently

    Team work

    Criticism and self-criticism

    Production of free, creative and inductive thinking

    (3) SYLLABUS

    The course "Digital Social Research Methods" covers computational methods and tools for conducting research on social, economic and cultural phenomena. Students will learn to utilize methods and tools for collection, pre-processing, visualization and exploratory analysis as well as data interpretation with special emphasis on the analysis of textual data, social networks and visual data. Through practical exercises, students will develop practical skills in applying digital methods to social research questions.

     


    (4) TEACHING and LEARNING METHODS - EVALUATION

    DELIVERY
    Face-to-face, Distance learning, etc.

    Face-to-face or distance learning

    USE OF INFORMATION AND COMMUNICATIONS TECHNOLOGY
    Use of ICT in teaching, laboratory education, communication with students

    Slides and notes to support lectures

     

    Use of R programming language for practical activities

     

    Use of the E-Learning platform Open eClass in order to:

    • Organize the course material (slides, notes, examples, etc)
    • Perform weekly online quizzes to evaluate the understanding of the related course material
    • Hand in homeworks
    • Communicate with the students and the class

     

    Open courses and open educational material

    TEACHING METHODS

    The manner and methods of teaching are described in detail.

    Lectures, seminars, laboratory practice, fieldwork, study and analysis of bibliography, tutorials, placements, clinical practice, art workshop, interactive teaching, educational visits, project, essay writing, artistic creativity, etc.

     

    The student's study hours for each learning activity are given as well as the hours of non-directed study according to the principles of the ECTS

    Activity

    Semester workload

    Lectures

    52

    Laboratory practice

    26

    Project

    72

     

     

     

     

     

     

     

     

     

     

     

     

    Course total

    150

     

    STUDENT PERFORMANCE EVALUATION

    Description of the evaluation procedure

     

    Language of evaluation, methods of evaluation, summative or conclusive, multiple choice questionnaires, short-answer questions, open-ended questions, problem solving, written work, essay/report, oral examination, public presentation, laboratory work, clinical examination of patient, art interpretation, other

     

    Specifically-defined evaluation criteria are given, and if and where they are accessible to students.

    The evaluation of the students is done conclusively through exercises and research papers. The exercises (50%) are laboratory activities that focus on the various research activities using R as well as designing a research database. The research work (50%) takes place in the middle of the semester and includes the implementation of small research projects to study economic and/or social phenomena. Finally, the evaluation criteria can be accessed by students in the course description in Open eClass.

    (5) ATTACHED BIBLIOGRAPHY

    - Suggested bibliography:

    • T. Dimitroulia, D. Goutsos and G. Fragakis (2023), Introduction to Digital Humanities. A practical guide, Kardamitsa Publications (in Greek).

    • C. Dawson (2021), The A's and Z's of Digital Research Methods, Field Publications (in Greek).

    - Related academic journals:

     

     

    • INFORMATION, TECHNOLOGY AND SOCIETY
      (ΒΣΑ406-ΙΙ)

    Type
    ELECTIVE

    Department Abbreviation
    BSO

    Department
    DEPARTMENT OF BALKAN, SLAVIC AND ORIENTAL STUDIES

    Course Outlines

    COURSE OUTLINE

    (1) GENERAL

    SCHOOL

    ECONOMIC AND REGIONAL STUDIES

    ACADEMIC UNIT

    BALKAN, SLAVIC AND ORIENTAL STUDIES

    LEVEL OF STUDIES

    UNDERGRADUATE

    COURSE CODE

    ΒΣΑ406-ΙΙ

    SEMESTER

    C

    COURSE TITLE

    INFORMATION TECHNOLOGY AND SOCIETY

    INDEPENDENT TEACHING ACTIVITIES
    if credits are awarded for separate components of the course, e.g. lectures, laboratory exercises, etc. If the credits are awarded for the whole of the course, give the weekly teaching hours and the total credits

    WEEKLY TEACHING HOURS

    CREDITS

    Lectures and laboratory exercises

    4

    6

     

     

     

     

     

     

    Add rows if necessary. The organisation of teaching and the teaching methods used are described in detail at (d).

     

     

    COURSE TYPE

    general background,
    special background, specialised general knowledge, skills development

    Background, general knowledge and skills development

    PREREQUISITE COURSES:

     

    No

    LANGUAGE OF INSTRUCTION and EXAMINATIONS:

    Greek

    IS THE COURSE OFFERED TO ERASMUS STUDENTS

    No

    COURSE WEBSITE (URL)

    https://openeclass.uom.gr/courses/BSO146/

               

    (2) LEARNING OUTCOMES

    Learning outcomes

    The course learning outcomes, specific knowledge, skills and competences of an appropriate level, which the students will acquire with the successful completion of the course are described.

    Consult Appendix A

    • Description of the level of learning outcomes for each qualifications cycle, according to the Qualifications Framework of the European Higher Education Area
    • Descriptors for Levels 6, 7 & 8 of the European Qualifications Framework for Lifelong Learning and Appendix B
    • Guidelines for writing Learning Outcomes

    The aim of the course is to develop basic skills and abilities for the use of computer systems and their applications as the main tools for data processing today.

     

    Upon successful completion of the course, students are expected to be able to:

    • Describe the role and importance of Information Technology (IT) in the social sciences and humanities

    • Know the organization and operation of computer systems, the Internet and the World Wide Web

    • Know the social and economic impact of IT in everyday life

    • Search and evaluate information as well as scientific literature on the World Wide Web

    • Implement applications for processing of quantitative and qualitative data by using computer tools to solve problems

    • Compose, format and present a scientific paper using standalone or collaborative software tools

    • Implement modern multimedia content presentation or storytelling applications using codeless tools for economics, education and culture

     

     

    General Competences

    Taking into consideration the general competences that the degree-holder must acquire (as these appear in the Diploma Supplement and appear below), at which of the following does the course aim?

    Search for, analysis and synthesis of data and information, with the use of the necessary technology

    Adapting to new situations

    Decision-making

    Working independently

    Team work

    Working in an international environment

    Working in an interdisciplinary environment

    Production of new research ideas

    Project planning and management

    Respect for difference and multiculturalism

    Respect for the natural environment

    Showing social, professional and ethical responsibility and sensitivity to gender issues

    Criticism and self-criticism

    Production of free, creative and inductive thinking

    ……

    Others…

    …….

    Search for, analysis and synthesis of data and information, with the use of the necessary technology

    Adapting to new situations

    Decision-making

    Working independently

    Team work

    Criticism and self-criticism

    Production of free, creative and inductive thinking

    (3) SYLLABUS

    The course "Information Technology and Society" consists of two sections. The first section covers the basic principles of computer science and technology, computer applications in economics, social sciences and humanities as well as ethical and social issues from the use of information technology. The second section includes practical activities for searching, collecting, evaluating, storing, processing, communicating, and sharing information using either MS-Office tools or Google web applications (such as Google Search, Google Drive, Google Sheets, Google Forms and Google Sites) with the aim of implementing digital scientific work. Finally, methodologies for developing multimedia content or digital storytelling applications for economics, education and culture are presented.

     

    (4) TEACHING and LEARNING METHODS - EVALUATION

    DELIVERY
    Face-to-face, Distance learning, etc.

    Face-to-face or distance learning

    USE OF INFORMATION AND COMMUNICATIONS TECHNOLOGY
    Use of ICT in teaching, laboratory education, communication with students

    Slides and notes to support lectures

     

    Use of software (MS-Office and Google apps) for practical activities

     

    Use of the E-Learning platform Open eClass in order to:

    • Organize the course material (slides, notes, examples, etc)
    • Perform weekly online quizzes to evaluate the understanding of the related course material
    • Hand in homeworks
    • Communicate with the students and the class

     

    Open courses and open educational material

    TEACHING METHODS

    The manner and methods of teaching are described in detail.

    Lectures, seminars, laboratory practice, fieldwork, study and analysis of bibliography, tutorials, placements, clinical practice, art workshop, interactive teaching, educational visits, project, essay writing, artistic creativity, etc.

     

    The student's study hours for each learning activity are given as well as the hours of non-directed study according to the principles of the ECTS

    Activity

    Semester workload

    Lectures

    52

    Laboratory practice

    26

    Project

    72

     

     

     

     

     

     

     

     

     

     

     

     

    Course total

    150

     

    STUDENT PERFORMANCE EVALUATION

    Description of the evaluation procedure

     

    Language of evaluation, methods of evaluation, summative or conclusive, multiple choice questionnaires, short-answer questions, open-ended questions, problem solving, written work, essay/report, oral examination, public presentation, laboratory work, clinical examination of patient, art interpretation, other

     

    Specifically-defined evaluation criteria are given, and if and where they are accessible to students.

    The evaluation of students is done conclusively through written exams and assignments. The written examinations take place at the end of the semester during the examination period. The written final exam (50%) includes multiple choice questions and short answers. The work (50%) is carried out in the middle of the semester and it includes the implementation of applications using computer tools for data processing. Finally, the evaluation criteria are available to students at Open eClass.

    (5) ATTACHED BIBLIOGRAPHY

    - Suggested bibliography:

    • Glava, M. (2021) Introduction to Computers and Informatics, Dissigma Publications (in Greek)

    • O’Leary, T. (2021) Basic Principles in Informatics, Broken Hill Publishers Ltd (in Greek)

    • Evans, A., K. Martin, and M.A. Poatsy (2014) Introduction to Informatics, 1st edition, Kritiki (in Greek)

    • Forouzan, B. (2015) Introduction to Computer Science, 3rd edition, Klidarithmos (in Greek)

    • Brookshear, J.B. (2009) Computer Science: An Overview, 10th Edition, Klidarithmos (in Greek)

     

    - Related academic journals:

     

     

    • QUANTITATIVE METHODS OF SOCIAL SCIENCES - STATISTICS
      (ΒΣΑ410-ΙΙ)

    Type
    ELECTIVE

    Department Abbreviation
    BSO

    Department
    DEPARTMENT OF BALKAN, SLAVIC AND ORIENTAL STUDIES

    Course Outlines

    COURSE OUTLINE

    (1)    GENERAL

    SCHOOL

    ECONOMIC AND REGIONAL STUDIES

    ACADEMIC UNIT

    BALKAN, SLAVIC AND ORIENTAL STUDIES

    LEVEL OF STUDIES

    UNDERGRADUATE

    COURSE CODE

    ΒΣΑ410-ΙΙ

    SEMESTER

    D

    COURSE TITLE

    QUANTITATIVE METHODS SOCIAL SCIENCES - STATISTICS

    INDEPENDENT TEACHING ACTIVITIES
    if credits are awarded for separate components of the course, e.g. lectures, laboratory exercises, etc. If the credits are awarded for the whole of the course, give the weekly teaching hours and the total credits

    WEEKLY TEACHING HOURS

    CREDITS

    Lectures and laboratory exercises

    4

    6

     

     

     

     

     

     

    Add rows if necessary. The organisation of teaching and the teaching methods used are described in detail at (d).

     

     

    COURSE TYPE

    general background,
    special background, specialised general knowledge, skills development

    General Background

    PREREQUISITE COURSES:

     

    No

    LANGUAGE OF INSTRUCTION and EXAMINATIONS:

    Greek

    IS THE COURSE OFFERED TO ERASMUS STUDENTS

    No

    COURSE WEBSITE (URL)

    https://openeclass.uom.gr/courses/BSO103/

               

    (2)    LEARNING OUTCOMES

    Learning outcomes

    The course learning outcomes, specific knowledge, skills and competences of an appropriate level, which the students will acquire with the successful completion of the course are described.

    Consult Appendix A

    • Description of the level of learning outcomes for each qualifications cycle, according to the Qualifications Framework of the European Higher Education Area
    • Descriptors for Levels 6, 7 & 8 of the European Qualifications Framework for Lifelong Learning and Appendix B
    • Guidelines for writing Learning Outcomes

    The main purpose of the course is to familiarize students with the statistical analysis of data, the art of examining, summarizing and drawing conclusions from data. Specifically, the student, after understanding the basic principles of statistics in the field of economics and social sciences, can:

    • describe and organize the data collected using descriptive statistical techniques

    • handles probabilities and random variables effectively

    • solves inferential statistics problems

    • performs statistical analysis and analysis of data using one of the most popular statistical software packages (MS-Excel, SPSS, Stata, R) on PC

     

    General Competences

    Taking into consideration the general competences that the degree-holder must acquire (as these appear in the Diploma Supplement and appear below), at which of the following does the course aim?

    Search for, analysis and synthesis of data and information, with the use of the necessary technology

    Adapting to new situations

    Decision-making

    Working independently

    Team work

    Working in an international environment

    Working in an interdisciplinary environment

    Production of new research ideas

    Project planning and management

    Respect for difference and multiculturalism

    Respect for the natural environment

    Showing social, professional and ethical responsibility and sensitivity to gender issues

    Criticism and self-criticism

    Production of free, creative and inductive thinking

    ……

    Others…

    …….

    Search for, analysis and synthesis of data and information, with the use of the necessary technology

    Decision-making

    Working independently

    Production of free, creative and inductive thinking

    (3)    SYLLABUS

    The aim of the course is the introduction to the field of Statistics and its connection with the Economic and Social Sciences. The course deals with issues related to a) sampling and data production, b) descriptive statistics, c) probability theory and d) inferential statistics. The purpose of sampling and data production is to select a representative sample from the population and collect data from the sample. Descriptive statistics aims to summarize the main data properties in a sample, using graphical and numerical methods. inferential statistics aims to draw conclusions about the behavior of a population from a sample of data with the help of probability theory. Application of statistical analysis using software.

     

    (4)    TEACHING and LEARNING METHODS - EVALUATION

    DELIVERY
    Face-to-face, Distance learning, etc.

    Face-to-face or distance learning

    USE OF INFORMATION AND COMMUNICATIONS TECHNOLOGY
    Use of ICT in teaching, laboratory education, communication with students

    Slides and notes to support lectures

     

    Use of statistical software (Excel or STATA) for data analysis

     

    Use of the E-Learning platform Open eClass in order to:

    • Organize the course material (slides, notes, examples, etc)
    • Perform weekly online quizzes to evaluate the understanding of the related course material
    • Hand in homeworks
    • Communicate with the students and the class

     

    Open courses and open educational material

    TEACHING METHODS

    The manner and methods of teaching are described in detail.

    Lectures, seminars, laboratory practice, fieldwork, study and analysis of bibliography, tutorials, placements, clinical practice, art workshop, interactive teaching, educational visits, project, essay writing, artistic creativity, etc.

     

    The student's study hours for each learning activity are given as well as the hours of non-directed study according to the principles of the ECTS

     

    Activity

    Semester workload

    Lectures

    52

    Laboratory practice

    26

    Tutorials

    26

    Project

    46

     

     

     

     

     

     

     

     

     

     

    Course total

    150

     

    STUDENT PERFORMANCE EVALUATION

    Description of the evaluation procedure

     

    Language of evaluation, methods of evaluation, summative or conclusive, multiple choice questionnaires, short-answer questions, open-ended questions, problem solving, written work, essay/report, oral examination, public presentation, laboratory work, clinical examination of patient, art interpretation, other

     

    Specifically-defined evaluation criteria are given, and if and where they are accessible to students.

    The evaluation of students is done conclusively through written exams and midterm exam. The written examinations take place at the end of the semester during the examination period. The written final exam (70%) includes multiple choice questions and short answers. The midterm exam (30%) is carried out in the middle of the semester and it includes data analysis questions using statistical software on PC. Finally, the evaluation criteria are available to students at Open eClass.

    (5)    ATTACHED BIBLIOGRAPHY

    - Suggested bibliography:

    • Ioannidis, D. and Michailidis, P. (2021) Quantitative Methods in Social-Economic Sciences, Tziolas Publications, 2021 (in Greek).

    • Agresti A (2021), Statistical Methods for the Social Sciences, 5th edition, Tziolas Publications, 2021 (in Greek).

    • Moore, D.S. and McCabe, G.P. (2014) Introduction to the Practice of Statistics, 8th Edition, W.H. Freeman.

     

    - Related academic journals:

    • Computational Statistics & Data Analysis

    • Statistics and Probability Letters

    • Journal of Applied Statistics

     

    • SPACE AND CULTURAL MANAGEMENT IN THE BALKANS AND SE EUROPE
      (HAC103)

    Type
    COMPULSORY

    Department Abbreviation
    ΜΙΑΠΑΝΕ

    Department
    MASTER'S DEGREE IN HISTORY, ANTHROPOLOGY AND CULTURE IN EASTERN AND SOUTH-EASTERN EUROPE

    Course Outlines

    Αντικείμενο του μαθήματος αποτελεί η διαχείριση και η προβολή της πολιτιστικής κληρονομιάς ενός τόπου. Η ανάδειξη και προστασία της εικόνας της πόλης με άξονα την βιώσιμη ανάπτυξη, αποτελεί το βασικό παράδειγμα αυτής της διερεύνησης. Στο πλαίσιο της γεωγραφικής περιοχής αναφοράς του μαθήματος (ΝΑ Ευρώπη) εξετάζονται ζητήματα σχεδιασμού της πόλης και οι επικοινωνιακές πολιτικές για τη διαχείριση της πολιτιστικής της κληρονομιάς. Η πρόσβαση σε γνώσεις και πρακτικές σχετικά με τη συνάντηση των διαφορετικών πολιτισμών, την κατανόηση λαών και πολιτισμών, την ανάδειξη και προβολή της τοπικής κουλτούρας, την αποδοχή της ετερότητας, όπως αυτά αποτυπώνονται στον χώρο και δημιουργούν εναύσματα για τη διαχείριση της πολιτιστικής κληρονομιάς στα Βαλκάνια και την Ανατολική Ευρώπη, αποτελούν ζητούμενα/ στόχους αυτού του μαθήματος. Παράλληλα επιχειρείται η εκπαίδευση σε πολιτικές προώθησης και διαχείρισης της “πολιτιστικής ταυτότητας” ενός τόπου μέσα από τη χρήση των νέων τεχνολογιών ψηφιοποίησης.

    Ενδεικτική βιβλιογραφία:

    Gavra, E.G. «Ekistics monumental heritage in today’s Turkey: current status and management prospects». «MENON: Journal Of Educational Research», Issue 1, July 2012. University of Western Macedonia, Florina, Greece. (pp. 32-44). [στην αγγλική]. ISSN: 1792-8494.

    Γαβρά, Ε. «Πλαίσιο και πολιτικές προστασίας και διαχείρισης του οικιστικού και πολιτισμικού αποθέματος στην π.ΓΔΜ σήμερα. Η ευρωπαϊκή προοπτική» στο: Ι.Κολιόπουλος, Κ.Χατζηκωνσταντίνου, Β.Γούναρης (επιμ.), Η διασυνοριακή συνεργασία Ελλάδας-ΠΓΔΜ ως ζητούμενο, Εκδόσεις Επίκεντρο, Θεσσαλονίκη 2008. (σσ.149-244) [στην ελληνική – με κριτές]. ISBN: 960-458-175-9.

    Γαβρά, Ε.Γ. «Η Διαχείριση του Πολιτιστικού και Οικιστικού Αποθέματος στη Ν.Α. Ευρώπη, στο πλαίσιο των Πολιτικών Χωρικής Ανάπτυξης: Η Περίπτωση της Ρουμανίας», Α’ Επιστημονικό Συνέδριο του Τμήματος Βαλκανικών Σπουδών: Διαστάσεις της Μετάβασης και η ευρωπαϊκή προοπτική των χωρών της Βαλκανικής, Φλώρινα 20-22/10/2006, σσ. 571-593 [στην ελληνική – με κριτές].

    Gavra, E.G. «Urban Policies and Architectural Heritage: concrete examples, real scenarios and cities networks in the area of SE Europe». «Studia Universitatis Babes – Bolyai, Studia Europaea», L, 2-3, 2005. Babes Bolyai University, Cluj-Napoca, Romania, pp.379-394 [στην αγγλική – με κριτές]. ISSN: 1224-8746. Διαθέσιμο και στο: http://www.euro.ubbcluj.ro/studia/issues/steur2005_2_3.pdf

    Γαβρά, Ε.Γ. (2004) Πολιτισμικό απόθεμα και αρχιτεκτονική κληρονομιά στα Βαλκάνια. Διαχείριση στο πλαίσιο της ευρωπαϊκής ολοκλήρωσης, Θεσσαλονίκη: Κυριακίδη [στην ελληνική].

    Γοσποδίνη, Α. και Μπεριάτος, Η. (2006) Τα νέα αστικά τοπία και η ελληνική πόλη, Αθήνα: Κριτική

    Hall, T. (2005) Αστική γεωγραφία, Αθήνα: Κριτική

    Konsola N.N., 2006, World Cultural Development and Policy, Publications Papazisis, Athens

    Κόνσολα, Ντ. (1995) Η Διεθνής Προστασία της Παγκόσμιας Πολιτιστικής Κληρονομιάς, Αθήνα: Παπαζήση.

    Μητούλα, Ρ. (2006) Βιώσιμη Περιφερειακή Ανάπτυξη στην Ευρωπαϊκή Ένωση και Ανασυγκρότηση του Ελληνικού Αστικού Περιβάλλοντος, Αθήνα: Σταμούλη.

    Ζήβας, Δ.Α. (1997) Τα μνημεία και η πόλη, Αθήνα: Libro

    Σ.Ν. Δημητριάδης, Α.Σ. Πομπόρτσης, Ε. Γ. Τριανταφύλλου, Τεχνολογία πολυμέσων θεωρία και πράξη, Εκδόσεις Τζιόλα, 2004.

    Φ. Λαζαρίνης, Τεχνολογίες Πολυμέσων: Θεωρία, Υλικό, Λογισμικό, 1η έκδοση, Εκδόσεις κλειδάριθμος, 2007.

    T. Vaughan, Πολυμέσα Αναλυτικός Οδηγός, 8η Έκδοση, Εκδόσεις Γκιούρδας, 2012.

    V. Costello, S. Youngblood, N. E. Youngblood, Multimedia Foundations: Core Concepts for Digital Design, Focal Press, 1 edition, 2012.

    • TOPICS IN COMPUTER SCIENCE
      (ΒΣ0826)

    Type
    ELECTIVE

    Department Abbreviation
    BSO

    Department
    DEPARTMENT OF BALKAN, SLAVIC AND ORIENTAL STUDIES

    Course Outlines

    The course offers an introduction to the basic principles of database design and implementation, as well as data mining applications. Specifically, the course covers the analysis of requirements according to the needs of a small business unit, the Entity and Relationship diagrams, its implementation in relational tables and finally the design and implementation of tables, queries, forms and reports with the help of MS-Access. The course also covers basic principles, methods and applications of data mining from large data sets.

    Publications


    • Books (8 records)

    Περιλαμβάνει Βιβλία ή/και μονογραφίες σε διεθνείς ή ελληνικούς εκδοτικούς οίκους. Κεφάλαια ή άρθρα συλλογικών τόμων ή επιμέλεια τόμων σε διεθνείς ή ελληνικούς εκδοτικούς οίκους.

      2023

      • A. Chatsiopoulou and P. Michailidis, Cultural Heritage Applications Based on Augmented Reality: A Literature Review, in Proceedings of the International Conference on Extended Reality, Lecture Notes in Computer Science 14219, pp. 194-209, Springer-Verlag, 2023.

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      2021

      • Δ. Ιωαννίδης, Π. Μιχαηλίδης, Ποσοτικές Μέθοδοι στις Κοινωνικοοικονομικές Επιστήμες, Εκδόσεις Τζιόλα, 2021.

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      2011

      • C. Kouzinopoulos, P.D. Michailidis and K.G. Margaritis, Parallel Processing ofMultiple Pattern Matching Algorithms for Biological Sequences: Methods and PerformanceResults, in book "Bioinformatics - Computational Biology and Modeling", Ning-Sun Yang (eds.),Ch. 8, pp. 161 - 182, Intech - Open Access Publisher, 2011.

      2008

      • P.D. Michailidis and K.G. Margaritis, Parallel and Distributed ComputationalMethods for Flexible Text Searching, in book "Supercomputing Research Advances",Yongge Huang (eds.), Ch. 2, pp. 61 - 112, Nova Science Publishers, 2008.

      2005

      • P.D. Michailidis, V. Stefanidis and K.G. Margaritis, Performance Analysis of Overheads for Matrix - Vector Multiplication in Cluster Environment, in Proceedings of the 10th Panhellenic Conference on Informatics (PCI'2005), Lecture Notes in Computer Science 3746, Volos, Greece, pp. 245-255, Springer-Verlag, 2005.

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      2003

      • P.D. Michailidis and K.G. Margaritis, Performance Analysis of Approximate String Searching Implementations for Heterogeneous Computing Platform, in Proceedings of the 10th Euro PVM/MPI 2003 Conference (PVM-MPI'2003), Lecture Notes in Computer Science, Venice, Italy, pp. 242-246, Springer-Verlag, 2003.

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      2002

      • P.D. Michailidis and K.G. Margaritis, A Performance Study of Load Balancing Strategies for Approximate String Matching on an MPI Heterogeneous System Environment, in Proceedings of the 9th Euro PVM/MPI 2002 Conference (PVM-MPI'2002), Lecture Notes in Computer Science 2474, Linz, Austria, pp. 432-440, Springer-Verlag, 2002.

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      2001

      • P.D. Michailidis and K.G. Margaritis, Text Searching on a Heterogeneous Cluster of Workstations, in Proceedings of the 8th Euro PVM/MPI 2001 Conference (PVM-MPI'2001), Lecture Notes in Computer Science 2131, Santorini, Greece, pp. 378-385, Springer-Verlag, 2001.

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      • Scientific Journals (22 records)

      Περιλαμβάνει Άρθρα σε διεθνή ή ελληνικά επιστημονικά περιοδικά (με κριτές).

        2024

        • P.D. Michailidis, A Comparative Study of Sentiment Classification Models for Greek Reviews, Big Data and Cognitive Computing, 8(9), 107, 2024

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        2022

        • P.D. Michailidis, A Scientometric Study of the Stylometric Research Field, Informatics, 9(3), 60, 2022.

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        • P.D. Michailidis, Visualizing Social Media Research in the Age of COVID-19, Information, 13(8), 372, 2022.

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        2021

        • P. Michailidis, V. Vlasidis, S. Karekla, An Exploratory Study on the Attitudes of the Greek Believers towards the State’s Measures during the First Wave of Coronavirus Pandemic, Social Sciences, 10(2), pp. 1–22, 67, 2021.

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        2019

        • P.D. Michailidis, An Efficient Multi-core Implementation of the Jaya Optimisation Algorithm, International Journal of Parallel, Emergent and Distributed Systems, vol. 34, no. 3, pp. 288-320, 2019.

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        2018

        • P.D. Michailidis, A preliminary performance study on nonlinear regression models using the Jaya optimisation algorithm, IAENG International Journal of Applied Mathematics, vol. 48, no. 4, pp. 424-428, 2018.

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        2017

        • C.S. Kouzinopoulos, P.D. Michailidis and K.G. Margaritis, Parallel Two-Dimensional Pattern Matching Algorithms on GPU, Neural, Parallel and Scientific Computations, vol. 25, pp. 165 - 180, 2017.

        2016

        • P.D. Michailidis and K.G. Margaritis, Scientific Computations on Multi-core Systems using Different Programming Frameworks, Applied Numerical Mathematics, vol. 104, pp. 62-80, Elsevier-Science, 2016.

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        2015

        • C.S. Kouzinopoulos, P.D. Michailidis and K.G. Margaritis, Multiple String Matching on a GPU using CUDA, Scalable Computing: Practice and Experience, vol. 16, no. 2, pp. 121-137, 2015.

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        2013

        • P.D. Michailidis and K.G. Margaritis, Accelerating Kernel Density Estimation on the GPU using the CUDA framework, Applied Mathematical Sciences, vol. 7, no. 30, pp. 1447 - 1476, Hikari Ltd., 2013.

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        • P.D. Michailidis and K.G. Margaritis, Computing Dot - Product on Heterogeneous Master - Worker Platforms, International Journal of Pure and Applied Mathematics, vol. 84, no. 1, pp. 115-140, Academic Publications Ltd., 2013.

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        2011

        • P.D. Michailidis and K.G. Margaritis, Parallel Direct Methods for Solving System of Linear Equations with Pipelining on a MultiCore using OpenMP, Journal of Computational and Applied Mathematics, vol. 236, no. 3, pp. 326-341, Elsevier-Science, 2011.

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        2008

        • P.D. Michailidis and K.G. Margaritis, Processor Array Architectures for Flexible Approximate String Matching, Journal of Systems Architecture, vol. 54, no. 1-2, pp. 35-54, Elsevier-Science, 2008.

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        2007

        • P.D. Michailidis and K.G. Margaritis, A Programmable Array Processor Architecture for Flexible Approximate String Matching Algorithms, Journal of Parallel and Distributed Computing, vol. 67, no. 2, pp. 131-141, Elsevier-Science, 2007.

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        • V.K. Stefanidis, P.D. Michailidis and K.G. Margaritis, An Improved Performance Prediction Model for Matrix-Vector Multiplication on Clusters of Workstations, HERMIS: The International Journal of Computer Mathematics and its Applications, vol.8, pp. 77-83, online, 2007.

        2006

        • P.D. Michailidis, T. Typou, V. Stefanidis and K.G. Margaritis, Performance Models for Matrix Computations on Networks of Heterogeneous Workstations, Neural, Parallel and Scientific Computations, vol. 14, no. 2/3, pp. 177 - 204, 2006.

        2005

        • P.D. Michailidis and K.G. Margaritis, New Processor Array Architectures for the Longest Common Subsequence Problem, The Journal of Supercomputing, vol. 32, no. 1, pp. 51-69, Springer Science + Business Media, 2005.

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        • P.D. Michailidis and K.G. Margaritis, Parallel Flexible Text Searching Applications on a Heterogeneous Cluster Architecture, International Journal of Computational Science and Engineering, vol. 1, no. 1, pp. 45-59, Inderscience Publishers, 2005.

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        2003

        • P.D. Michailidis and K.G. Margaritis, Performance Evaluation of Load Balancing Strategies for Approximate String Matching Application on an MPI Cluster of Heterogeneous Workstations, Future Generation Computer Systems, vol. 19, no. 7, pp. 1075-1104,Elsevier-Science, 2003.

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        2002

        • P.D. Michailidis and K.G. Margaritis, Parallel Implementations for String Matching Problem on a Cluster of Distributed Workstations, Neural, Parallel and Scientific Computations, vol. 10, pp. 287-312, 2002.
        • P.D. Michailidis and K.G. Margaritis, On-line Approximate String Searching Algorithms: Survey and Experimental Results, International Journal of Computer Mathematics, vol. 79, no. 8, pp. 867-888, 2002.

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        2001

        • P.D. Michailidis and K.G. Margaritis, On-line String Matching Algorithms: Survey and Experimental Results, International Journal of Computer Mathematics,vol. 76, no. 4, pp. 411-434, 2001.

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        • Conferences (30 records)

        Περιλαμβάνει Άρθρα σε δημοσιευμένα πρακτικά διεθνών ή ελληνικών συνεδρίων (με κριτές).

          2014

          • M. Dasygenis and P.D. Michailidis, Evaluating modern parallelization techniques on block matching algorithms, in Proceedings of the 18th Panhellenic Conference on Informatics (PCI'2014), pp. 1-6, Athens, Greece, 2014.

          2013

          • P.D. Michailidis and K.G. Margaritis, Parallel Computing of Kernel Density Estimation with Different Multi-core Programming Models, in Proceedings of the 21st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP'2013), pp. 77 – 85, Belfast, Northern Ireland, IEEE Computer Society, 2013.

          2012

          • P.D. Michailidis and K.G. Margaritis, Efficient Multi-Core Computations in Computational Statistics and Econometrics, in Proceedings of the 15th IEEE International Conference on Computational Science and Engineering (CSE'2012), pp. 267–274, Pafos, Cyprus, IEEE Computer Society, 2012
          • P.D. Michailidis and K.G. Margaritis, Implementing Basic Computational Kernels of Linear Algebra on Multicore, in Proceedings of the 16th Panhellenic Conference on Informatics (PCI'2012), pp. 217-222, Piraeus, Greece, IEEE Computer Society, 2012.
          • P.D. Michailidis and K.G. Margaritis, Performance Study of Matrix Computations using Multicore Programming Tools, in Proceedings of the 5th Balkan Conference on Informatics (BCI'2012), pp. 186-192, Novi Sad, Serbia, 2012.
          • P.D. Michailidis and K.G. Margaritis, Computational Comparison of Some Multi-core Programming Tools for Matrix Computations, in Proceedings of the 14th IEEE International Conference on High Performance Computing and Communications (HPCC-2012), pp. 143-150, Liverpool, UK, 2012.
          • C.S. Kouzinopoulos, P.D. Michailidis and K.G. Margaritis, Performance Study of Parallel Hybrid Multiple Pattern Matching Algorithms for Biological Sequences, in Proceedings of International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS 2012), pp. 182-187, Vilamoura, Portugal, 2012.

          2011

          • P.D. Michailidis and K.G. Margaritis, Open Multi Processing (OpenMP) of Gauss-Jordan Method for Solving System of Linear Equations, in Proceedings of the 11th IEEE International Conference on Computer and Information Technology (CIT-2011), pp. 314–319, Pafos, Cyprus, 2011.
          • C. Kouzinopoulos, P.D. Michailidis, K.G. Margaritis, Experimental Results on Multiple Pattern Matching Algorithms for Biological Sequences, in Proceedings of International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS 2011), pp. 274-277, Rome, Italy, 2011.

          2010

          • P.D. Michailidis and K.G. Margaritis, Implementing Parallel LU Factorization with Pipelining on a MultiCore using OpenMP, in Proceedings of the 13th IEEE International Conference on Computational Science and Engineering (CSE-2010), pp. 253-260, Hong Kong SAR, China, 2010.
          • P.D. Michailidis and K.G. Margaritis, Performance Models for Matrix Computations on MultiCore Processors using OpenMP, in Proceedings of the 11th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT'2010), pp. 375-380, Wuhan, China, 2010.

          2009

          • P.D. Michailidis and K.G. Margaritis, Experimental Study on Variants of the Zhu-Takaoka String Matching Algorithm, in Proceedings of the Balkan Conference on Informatics (BCI'2009), pp. 116-122, Thessaloniki, Greece, 2009.

          2008

          • P.D. Michailidis and K.G. Margaritis, Performance Evaluation of Multiple Approximate String Matching Algorithms Implemented with MPI Paradigm in an Experimental Cluster Environment, in Proceedings of the 12th Panhellenic Conference on Informatics (PCI'2008), pp. 168 - 172, Samos, Greece, IEEE Computer Society, 2008.
          • P.D. Michailidis and K.G. Margaritis, A Parallel Implementation for Finding the Longest Common Subsequence, in Proceedings of the 6th International Conference on Engineering Computational Technology (ECT'2008), Athens, Greece, 2008.

          2007

          • P.D. Michailidis and K.G. Margaritis, Generalized Performance Model for Flexible Approximate String Matching on a Distributed System, in Proceedings of the 11th Panhellenic Conference on Informatics (PCI'2007), vol. II, pp. 279 – 288, Patras, Greece, New Technologies Publications, 2007.

          2006

          • P.D. Michailidis and K.G. Margaritis, Implementation of a Programmable Array Processor Architecture for Approximate String Matching Algorithms on FPGAs, in Proceedings of 20th IEEE International Parallel and Distributed Processing Symposium and the 13th Reconfigurable Architectures Workshop (RAW 2006), Rhodes, Greece, 4 pages, 2006.

          2005

          • P.D. Michailidis and K.G. Margaritis, A Programmable Array Processor Architecture for Flexible Approximate String Matching Algorithms, in Proceedings of the 2005 International Conference on Parallel Processing Workshops (ICPP'2005), Oslo, Norway, pp. 201-209, 2005.

          2004

          • T. Typou, V. Stefanidis, P.D. Michailidis and K.G. Margaritis, Implementing Matrix Multiplication on an MPI Cluster of Workstations, in Proceedings of the 1st International Conference "From Scientific Computing to Computational Engineering" (IC-SCCE'2004), Athens, Greece, vol. II, pp. 631-639, 2004.
          • T. Typou, V. Stefanidis, P.D. Michailidis and K.G. Margaritis, Matrix - Vector Multiplication on a Cluster of Workstations, in Proceedings of the 1st International Conference "From Scientific Computing to Computational Engineering" (IC-SCCE'2004), Athens, Greece, vol. II, pp. 713-726, 2004.

          2003

          • P.D. Michailidis and K.G. Margaritis, Bit-Level Processor Array Architecture for Flexible String Matching, in Proceedings of the 1st Balkan Conference in Informatics (BCI'2003), Thessaloniki, Greece, pp. 517-526, 2003.
          • P.D. Michailidis and K.G. Margaritis, Performance Analysis of Approximate String Searching Implementations for Heterogeneous Computing Platform, in Proceedings of the 2003 International Conference on Parallel Processing Workshops (ICPP'2003), Taiwan, pp. 173-180, 2003.
          • P.D. Michailidis and K.G. Margaritis, Parallel Architecture for Flexible Approximate Text Searching, in CD-ROM Proceedings of the 7th WSEAS International Multiconference on Circuits, Systems, Communications and Computers, (WSEAS-CSCC'2003), Corfu, Greece, 2003. Also it is published in Recent Advances in Communications and Computer Science, pp. 384-391, WSEAS Press.
          • P.D. Michailidis and K.G. Margaritis, Flexible Approximate String Matching Application on a Heterogeneous Distributed Environment, in Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA'03), Las Vegas, USA, vol. 1, pp. 164-172, 2003.

          2002

          • P.D. Michailidis and K.G. Margaritis, A Processor Array for Approximate Limited Expression Matching, in Proceedings of the First Workshop on Application Specific Processors (WASP'2002), Instabul, Turkey, pp. 137-144, 2002.
          • P.D. Michailidis and K.G. Margaritis, Implementation of the Approximate String Matching Application on a Cluster of Heterogeneous Workstations, in Proceedings of the First International Symposium on Parallel and Distributed Computing (ISPDC'2002), Iasi, Romania, pp. 193-204, 2002.

          2001

          • P.D. Michailidis and K.G. Margaritis, Implementation of the String Matching Problem on a Cluster of Workstations, in Proceedings of the 8th Panhellenic Conference on Informatics (PCI'2001), Nicosia, Cyprus, Vol. 2, pp. 72-81, 2001.
          • P.D. Michailidis and K.G. Margaritis, Parallel Text Searching Application on a Heterogeneous Cluster of Workstations, in Proceedings of the 2001 International Conference on Parallel Processing Workshops (ICPP'01), Valencia, Spain, pp. 169-175, 2001.
          • P.D. Michailidis and K.G. Margaritis, String Matching Problem on a Cluster of Personal Computers: Performance Modeling, in Proceedings of the 15th International Conference on Systems for Automation of Engineering and Research (SAER'2001), Sofia, Bulgaria, pp. 76-81, 2001.
          • P.D. Michailidis and K.G. Margaritis, String Matching Problem on a Cluster of Personal Computers: Experimental Results, in Proceedings of the 15th International Conference on Systems for Automation of Engineering and Research (SAER'2001), Sofia, Bulgaria, pp. 71-75, 2001.
          • P.D. Michailidis and K.G. Margaritis, Implementing String Searching Algorithms on a Network of Workstations using MPI, in Proceedings of the 5th Hellenic European Conference on Computer Mathematics and its Applications (HERCMA'2001), Athens, Greece, Vol. 1, pp. 298-305, 2001.
          • Other (5 records)

          Περιλαμβάνει Παρουσιάσεις σε διεθνή ή ελληνικά συνέδρια χωρίς δημοσίευση σε πρακτικά.

            2012

            • P.D. Michailidis and K.G. Margaritis, An Experimental Study for Parallelizing Basic Kernels From Scientific Computing using Multi-core Libraries, in Book of Abstracts of the 5th Conference on Numerical Analysis (NumAn'2012), pp. 33, Ioannina, Greece, 2012.

            2009

            • P.D. Michailidis and K.G. Margaritis, MPI Implementations for Solving Dot - Product on Heterogeneous Platforms, in Proceedings of the 9th Hellenic European Conference on Computer Mathematics and its Applications (HERCMA'2009), Athens, Greece, 2009.

            2007

            • P.D. Michailidis and K.G. Margaritis, Parallelization of Multiple String Matching on a Cluster Platform, in Proceedings of the 8th Hellenic European Conference on Computer Mathematics and its Applications (HERCMA'2007), Athens, Greece, 2007.

            2005

            • V. Stefanidis, P.D. Michailidis and K.G. Margaritis, An Improved Performance Prediction Model for Matrix - Vector Multiplication on Cluster of Workstations, in Proceedings of the 7th Hellenic European Conference on Computer Mathematics and its Applications (HERCMA'2005), Athens, Greece, 2005.
            • V. Stefanidis, P.D. Michailidis and K.G. Margaritis, Parallelization of Matrix - Vector Multiplication on a Cluster Platform, in Proceedings of the 2nd International Conference of Applied Mathematics, Plovdiv, Bulgaria, p. 258, 2005.
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