AL - 1 Data Mining in Spatio-Temporal sets (DMiST)

NICTA Project - Dr Joachim Gudmundsson (Joachim.gudmundsson@nicta.com.au) and Dr Thomas Wolle (Thomas.wolle@nicta.com.au)

Interesting patterns for moving objects involve some subset of the objects that have the same behaviour. Two simple examples are encounter (a large enough subset of points meet in the same region) and flocking (a large enough subset of points is moving along paths close to each other for a certain time). Also, in some applications repetitive patterns (commuting patterns or driving patterns) are of interest, such as: recurrence, concurrent recurrence, regular recurrence.

The most crucial step is the development of efficient algorithms for detecting and reporting patterns. Since the spatio-temporal patterns are very complex compared to patterns that have been considered before, it is unlikely that any approach neglecting the spatial information would be able to generate successful algorithms. The aim of the project is to come up with fast algorithms that can be proven to be efficient and correct.

This is a new and exciting area where the group at NICTA is one of the world leaders. Research is done in close collaboration with all the researchers, and students are expected to contribute to the positive and international atmosphere in the group.

The aim is to develop algorithms for a new set of real-world problems using tools from the fields of algorithms, computational geometry and data mining. The project has a strong mix of theory and programming.

An expected outcome of the research includes efficient algorithms or heuristics for reporting patterns in spatio-temporal data, e.g., flock, leadership, commuting patterns, meetings, and so on.

For further information, please contact the supervisors or consider www.dmist.net

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