An algorithmic approach for analysis of animal movement with granular computing in relation with data mining

The paper aims to bring together researchers and domain experts involved in developing and utilizing methods for knowledge extraction from massive amounts of data from moving objects. Technologies for object tracking are low cost and increasingly reliable in terms of coverage and accuracy; hence movement records are nowadays generated in huge volumes on a routine basis, using diverse technologies such as radio frequency mapping, Global Navigation Satellite Systems, video sequences and Doppler radar. The computational analysis of movement data has seen a successful first decade with progress made in capturing, preprocessing, storing, indexing and querying movement data, combined with promising results in visualizing movement and detecting movement patterns. In many application fields, such as animal monitoring, transportation, or epidemiology, the need for analyzing large sets of trajectories is evident and crucial; however, only very rudimentary automated analysis tools are available and more advanced analyses are carried out manually. Thus the analysis part has been neglected and in comparison with the image processing part it is technologically far behind in the development. A reason for this is a lack of theoretical and practical solutions for many crucial fundamental problems.