COURSE UNIT TITLE

: TEMPORAL INFORMATION SYSTEMS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CSE 5091 TEMPORAL INFORMATION SYSTEMS ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR DERYA BIRANT

Offered to

Computer Engineering (Non-Thesis-Evening) (English)
Computer Engineering Non-Thesis (English)
Computer Engineering (English)
Computer Engineering (English)
COMPUTER ENGINEERING (ENGLISH)

Course Objective

This is an advanced course on researches focusing on collecting and/or processing data about time, i.e., on the discovery of patterns change over time. The Temporal Information Systems (TIS) will cover analysing the data which has temporal aspects and discovering patterns, relationships, and rules from temporal data.

Learning Outcomes of the Course Unit

1   Learn patterns present in temporal data.
2   Develop temporal information system.
3   Build prediction models from temporal data.
4   Realize temporal data management.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Temporal Information Systems
2 Temporal Information Systems: A Closer View
3 Knowledge Discovery in Temporal Data
4 Temporal Data Collection and Preparation
5 Temporal Data Mining
6 Pattern Mining in Temporal Data
7 Temporal Classification and Prediction
8 Temporal Clustering
9 Anomaly Detection in Temporal Data
10 Advanced Time-Series Forecasting
11 Advanced Time-Series Forecasting
12 Intelligent Analysis to Mine Temporal Data
13 Intelligent Analysis to Mine Temporal Data
14 Project presentations

Recomended or Required Reading

Temporal Data Mining via Unsupervised Ensemble Learning, Yun Yang, 2016.

Temporal Data Mining, Theophano Mitsa, 2010.

Advanced Time Series Forecasting Using Data Mining Techniques: Intelligent Analysis to Mine Temporal Data, Francisco Martinez Alvarez, 2010.

Anomaly Detection In Temporal Data Mining, Mehmet Yavuz Onat, 2015.

Temporal Information Systems in Medicine, Carlo Combi, 2014.

Temporal and Spatio-temporal Data Mining, Wynne Hsu, Mong Li Lee, Junmei Wang, 2008.

Recently published papers in scientific journals.

Planned Learning Activities and Teaching Methods

Lectures,
Literature Review / Research,
Application Development,
Presentation,
Term project.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG ASSIGNMENT
2 PRS PRESENTATION
3 FCG FINAL COURSE GRADE ASG * 0.50 + PRS * 0.50


Further Notes About Assessment Methods

None

Assessment Criteria

Course outcomes will be evaluated with the presentation of the student about a topic, literature review, and project / report prepared by the student.

Language of Instruction

English

Course Policies and Rules

Code writing knowledge and skills are required.
Participation is mandatory.

Contact Details for the Lecturer(s)

Prof. Dr. Derya BIRANT
Dokuz Eylul University, Faculty of Engineering, Department of Computer Engineering
Tinaztepe Campus, Buca, Izmir
+90 (232) 301 74 03
derya@cs.deu.edu.tr

Office Hours

Will be announced at the beginning of the term.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 2 28
Project Preparation 1 65 65
Web Search and Library Research 1 5 5
Preparing presentations 1 30 30
Preparing report 1 25 25
TOTAL WORKLOAD (hours) 195

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11
LO.155
LO.255
LO.3555
LO.455

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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