Advanced Topics in Databases

Course Information
TitleΠροχωρημένα Θέματα Βάσεων Δεδομένων / Advanced Topics in Databases
Cycle / Level2nd / Postgraduate
Teaching PeriodSpring
CoordinatorEleftherios Tiakas
Course ID600016265

Programme of Study: PMS EPISTĪMĪ DEDOMENŌN KAI PAGKOSMIOU ISTOU (2018 éōs sīmera) PF

Registered students: 17
OrientationAttendance TypeSemesterYearECTS
KORMOSElective Courses belonging to the selected specialization217.5

Class Information
Academic Year2021 – 2022
Class PeriodSpring
Instructors from Other Categories
Weekly Hours3
Class ID
Course Type 2021
Specific Foundation
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
  • Face to face
General Prerequisites
There is no list of prerequisite courses in the strict sense. Students should master topics related to Databases, Programming and Data Mining.
Learning Outcomes
Cognitive: Learning methods used for spatial and multimedia data management, and advanced topics in data base management. Skills: After the course completion students will have a concrete view of the methods used for spatial and multimedia data management. Moreover, they will be able to apply indexing schemes (as well as other methods) in spatial and multimedia data. In addition, the assignments will help students to gain additional knowledge and solve practical problems.
General Competences
  • Apply knowledge in practice
  • Make decisions
  • Work autonomously
  • Generate new research ideas
Course Content (Syllabus)
Spatial and Multimedia Databases with emphasis on high dimensional data, Spatial Data Models, Spatial Query Languages, Spatial Storage and Indexing, Spatial Query Processing Algorithms and Optimization, Spatial Networks, Information Retrieval, Content-based Information Systems, Multidimensional Indexing Techniques, B-Trees and their variants, persistent trees, buffer trees, R-Trees and their variants, X-Trees, M-Trees, Slim-Trees, Similarity Query Processing Algorithms in Multidimensional Spaces, Similarity Query Processing Algorithms in Metric Spaces, Preference Queries (top-k, skylines), hash indexing techniques.
Spatial and Multimedia Databases, Indexing methods, Query Processing, Top-k, Skyline
Educational Material Types
  • Notes
  • Slide presentations
  • Book
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Laboratory Teaching
  • Use of ICT in Communication with Students
Course Organization
Reading Assigment100
Written assigments32
Student Assessment
Students grades are based on: (a) written examination (50%), and (b) project on development of a system related to spatial/multimedia data management, submission of a report (50%).
Student Assessment methods
  • Written Exam with Multiple Choice Questions (Formative, Summative)
  • Written Exam with Short Answer Questions (Formative, Summative)
  • Written Exam with Extended Answer Questions (Formative, Summative)
  • Written Assignment (Formative, Summative)
  • Written Exam with Problem Solving (Formative, Summative)
  • Report (Formative, Summative)
Additional bibliography for study
S. Shekhar, S. Chawla: Spatial Databases: A Tour, Prentice Hall, 2003. P. Rigaux, M. Scholl, A. Voisard: Spatial Databases: With Application to GIS, Morgan Kaufmann, 2001. H. Samet: Foundations of Multidimensional and Metric Data Structures, Morgan Kaufmann, 2006. R. H. Guting, M. Schneider, Moving Objects Databases, Morgan Kaufmann, 2005. A. Baughman, J. Gao, J.-Y. Pan, V.A. Petrushin (eds): Multimedia Data Mining and Analytics: Disruptive Innovation, Springer, 2015. K. S. Candan, M. L.Sapino, Data Management for Multimedia Retrieval, Cambridge University Press, 2010. O. Marques, B. Furht: Content-Based Image and Video Retrieval, Springer, 2002. P. Muneesawang, N. Zhang, L. Guan: Multimedia Database Retrieval: Technology and Applications, Springer, 2014. L. Liu, M. T. Ozsu (eds): Encyclopedia of Database Systems, Springer, 2009. J. Vitter, Algorithms and Data Structures for External Memory, 2008. J. Abello, P.M. Pardalos and M.G.C. Resende (editors), Handbook of Massive Data Sets, Kluwer Academic Publishers, 2002. D. Menta and S Sahni, Handbook of Data Structures and Application. 2005.
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