Mining from Massive Datasets

Course Information
TitleΕΞΟΡΥΞΗ ΣΕ ΜΕΓΑΛΑ ΔΕΔΟΜΕΝΑ / Mining from Massive Datasets
CodeNIS22
FacultySciences
SchoolInformatics
Cycle / Level2nd / Postgraduate
Teaching PeriodSpring
CoordinatorApostolos Papadopoulos
CommonNo
StatusActive
Course ID40003486

Programme of Study: PPS School of Informatics (2014-today)

Registered students: 8
OrientationAttendance TypeSemesterYearECTS
TECΗNOLOGIES GNŌSĪS DEDOMENŌN KAI LOGISMIKOUElective Courses belonging to the selected specialization217.5
TECΗNOLOGIES PLĪROFORIAS KAI EPIKOINŌNIŌN STĪN EKPAIDEUSĪElective Courses217.5
PSĪFIAKA MESA- YPOLOGISTIKĪ NOĪMOSYNĪElective Courses217.5
DIKTYAKA SYSTĪMATAElective Courses217.5

Class Information
Academic Year2015 – 2016
Class PeriodSpring
Faculty Instructors
Weekly Hours3
Class ID
600011307
Type of the Course
  • Scientific Area
  • Skills Development
Course Category
Specific Foundation / Core
Mode of Delivery
  • Face to face
Digital Course Content
Language of Instruction
  • Greek (Instruction, Examination)
  • English (Instruction, Examination)
Learning Outcomes
After the course completion students will have a concrete view of the methods used for mining massive data sets. Moreover, they will be able to apply some of these techniques in practice during their development of the course assignments.
General Competences
  • Apply knowledge in practice
  • Adapt to new situations
  • Make decisions
  • Work autonomously
  • Work in teams
  • Generate new research ideas
  • Advance free, creative and causative thinking
Course Content (Syllabus)
- MapReduce, Spark and distributed data management - Advanced issues in association rules, clustering and outlier detection - Mining data streams - Link analysis - Recommendation systems - Advanced hashing techniques (locality sensitive hashing)
Keywords
data mining, streams, links, clustering, similarity
Educational Material Types
  • Slide presentations
  • Book
  • scientific papers
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
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures391.3
Reading Assigment903
Project602
Written assigments361.2
Total2257.5
Student Assessment
Description
Students' grades are based on written examination (20-30%), in-class presentation of a scientific paper (20-30%) and system development in C++ or JAVA related to mining of massive datasets (40-60%).
Student Assessment methods
  • Written Exam with Short Answer Questions (Formative, Summative)
  • Written Assignment (Formative, Summative)
  • Oral Exams (Formative, Summative)
  • Performance / Staging (Formative, Summative)
  • Written Exam with Problem Solving (Formative, Summative)
  • Report (Formative, Summative)
  • written examination (Formative, Summative)
Bibliography
Course Bibliography (Eudoxus)
Mining of Massive Datasets, Anand Rajaraman and Jeff Ullman. Cambridge University Press.
Additional bibliography for study
1. Jiawei Han, Micheline Kamber, Data Mining : Concepts and Techniques, 3rd edition, Morgan Kaufmann, 2011. 2. Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Pearson Addison Wesley, 2006
Last Update
15-03-2016