ADVANCED INDEXING TECHNIQUES
| Title | ΠΡΟΗΓΜΕΝΗ ΕΥΡΕΤΗΡΙΑΣΗ ΔΕΔΟΜΕΝΩΝ / ADVANCED INDEXING TECHNIQUES |
| Code | IS24 |
| Faculty | Sciences |
| School | Informatics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Spring |
| Coordinator | Konstantinos Tsichlas |
| Common | No |
| Status | Active |
| Course ID | 40002454 |
| Academic Year | 2017 – 2018 |
| Class Period | Spring |
| Faculty Instructors | |
| Weekly Hours | 3 |
| Class ID | 600124895
|
Course Type 2016-2020
- Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600124895
- At the Website of the School: http://delab.csd.auth.gr/~tsichlas/massive.html
Language of Instruction
- Greek (Instruction, Examination)
Learning Outcomes
The students will be able to grasp the basic techniques used to handle massive data. They acquire the necessary knowledge so that they can handle massive data sets supporting efficiently a particular set of operations
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Work in teams
- Advance free, creative and causative thinking
Course Content (Syllabus)
Algorithms and data structures with emphasis in massive data sets. External memory models. Algorithms and data structures for two level model (B-trees, Computational geometry algorithms in external memory, string algorithms in external memory). Algorithms and data structures for the Cache Oblivious model (Cache Oblivious B-trees). Advanced hashing techniques. Predecessor structures. Algorithms for data compression. Algorithms for the streaming model. Data structures in P2P networks (overlays).
Keywords
External Memory, Streaming model, Compression, Predecessor problem, Hashing
Educational Material Types
- Notes
- Slide presentations
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | 1.3 | ✓ | ||
| Reading Assigment | 15 | 0.5 | ✓ | ||
| Project | 111 | 3.7 | ✓ | ✓ | |
| Written assigments | 60 | 2 | ✓ | ✓ | |
| Total | 225 | 7.5 |
Student Assessment
Description
The students must complete up to three programming projects. They must also present a paper related to the content of the course while they must also solve a set of theory exercises in order to understand the content of the course better. The evaluation criteria are published in the webpage of the course.
Student Assessment methods
- Written Exam with Extended Answer Questions (Summative)
- Written Assignment (Summative)
- Performance / Staging (Summative)
- Written Exam with Problem Solving (Formative)
- Report (Summative)
Bibliography
Additional bibliography for study
1. J. Abello, P.M. Pardalos and M.G.C. Resende (editors). Handbook of Massive Data Sets. Kluwer Academic Publishers. 2002, ISBN: 1-4020-0489-3.
2. D. Menta and S Sahni, Handbook of Data Structures and Application. 2005, ISBN 1-5848-8435-5
3. J. Vitter, Algorithms and Data Structures for External Memory, Book, (http://www.ittc.ku.edu/~jsv/Papers/Vit.IO_book.pdf)
4. External Memory Geometric Data Structures. L. Arge, Duke University Lecture notes
5. Erik Demaine, Cache Oblivious Algorithms and Data-Structures, in Lecture Notes from the EEF Summer School on Massive Data Sets, Lecture Notes in Computer Science, BRICS, University of Aarhus, Denmark, June 27-July 1, 2002, (http://erikdemaine.org/papers/BRICS2002/)
6. *Muthu Muthukrishnan, Data Streams: Algorithms and Applications (ebook) (http://algo.research.googlepages.com/eight.ps)
Last Update
21-03-2016
