PhD in Computer Science

Mining Moving Objects: Challenges and Directions

Academic year 2012/2013

Sommario del corso

• Course objectives
• Teaching tools

Course objectives

Nowadays, the use of many electronic devices in real world applications has led to an increasingly large amount of data containing moving object information. One of the objectives of spatiotemporal data mining is to analyze such datasets to understand the object movement behavior. In this context, many recent studies have been defined to mine moving object clusters including flocks, moving clusters, convoy queries, closed swarms, group patterns, etc. Although these patterns are interesting and useful, mining and analyzing them is still challenging. There are several key questions, to answer, such as: 1) How can we extract and compare them efficiently? 2) Are they good enough to represent the complex object movement behavior? 3) Among the thousands of patterns, which ones are the most important to summarize a moving object dataset? In this talk, we introduce, analyze and address these three challenges and propose future research directions.

Teacher: Dino Ienco (Chargé de Recherche @ IRSTEA, Montpellier, France)

Class schedule

• Teaching materials
• Exams and finals
• Go to Moodle
• Visit the forums