Efficient algorithms for analyzing the movement of groups

• Applicant: Professorin Dr. Maike Buchin
• Subject Area: Theoretical Computer Science
• Term: from 2016 to 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 314719246

Nowadays more and more data of moving objects, such as people, animals and goods, are being collected. The analysis of the growing amounts of data requires efficient algorithms. In previous years algorithms have been developed for the analysis of movement data of individual entities; but increasingly data is collected of several objects moving in a group.The goal of this project is to develop algorithms for the analysis of moving groups. In particular algorithms will be developed for representing, determining the similarity, and segmenting movement data of groups.The representation of a group should summarize its geometric properties in a compact way. Several moving groups can be summarized in a graph. Depending on the representation a number of algorithmic problems arise. For moving groups similarity measures for moving shapes will be developed, and for several groups similarity measures of geometric graphs. Segmentation asks to partition the data in particular by the behaviour of the moving objects. Here we will consider segmentation of moving groups given in different representations by behaviour.This project will lay the theoretical foundations of the analysis of the movement of groups. The analysis tasks considered are motivated by questions arising from the analysis of real movement data, in particular of animals. To therefore also ensure the practical applicability of the algorithms, we will closely collaborate with domain experts and evaluate the algorithms on data of moving animal groups.

• DFG Programme: Research Grants
• Co-Investigator: Dr. Andrea Kölzsch