• Welcome to Spatio-temporal Data Management and Mining


    Mobile and wearable devices generate daily large amounts of spatio-temporal data, which has led to a widespread use of location information. Effective management of spatio-temporal data is important for a wide range of applications including personalized location-based services, location-based social networks, volunteered data from participatory sensor networks as well as traffic management. The analysis and extraction of knowledge from location check-ins to GPS trajectory data offers tremendous opportunities for new services. However, spatio-temporal data is large and dynamic in nature, which calls for novel methods to manage and mine this data. This subject will introduce students not only to established methods in the area but also expose them to the latest research development on managing and mining spatio-temporal data.

    Learning outcomes:

                   • Develop a deeper understanding of spatio-temporal data management concepts, models and applications

                   • Understand and apply recent algorithms and operations related to spatio-temporal data management and mining

                   • Get familiar with various query types and operations relevant to spatio-temporal data processing

                   • Gain an overview of important research directions relating to spatio-temporal data management and mining

                   • Investigate, discuss and present new research topics on spatio-temporal data in oral and written form


                   • Proficiency in at least programming language

                   • Knowledge of fundamental algorithms and data types

                   • Familiarity with fundamental database and data mining concepts

                   • Understanding of basic concepts in linear algebra and probability theory

    Organizer: Associate Professor, Hua Lu, luhua@cs.aau.dk

    Lecturers: Associate Professor, Lars Kulik, The University of Melbourne

    ECTS: 2

    Time: TBA

    Place: Aalborg University

    Zip code: 


    Number of seats:


    Important information concerning PhD courses 
    We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.