par Orakzai, Faisal Moeen 
;Calders, Toon ;Pedersen, Torben Bach
Référence Proceedings (IEEE International Conference on Mobile Data Management), 2016-July, page (122-131), 7517786
Publication Publié, 2016-07
;Calders, Toon ;Pedersen, Torben BachRéférence Proceedings (IEEE International Conference on Mobile Data Management), 2016-July, page (122-131), 7517786
Publication Publié, 2016-07
Article révisé par les pairs
| Résumé : | Due to the wide spread of mobile devices equipped with location sensors, the amount of mobility data being generated is enormous. Mining this data to reveal interesting behavioral patterns has gained attention in recent years. Various mobility patterns have been proposed which describe collective mobility behaviour. One such pattern is the convoy pattern which can be used to find groups of people moving together in public transport or for prevention of traffic jams. A convoy consists of at least m objects moving together for at least k consecutive time instants where m and k are user-defined parameters. Existing algorithms for detecting convoy patterns, however, do not scale to real-life dataset sizes. Therefore in this paper, we propose a generic distributed convoy pattern mining algorithm and show how such an algorithm can be implemented using the MapReduce framework. Our experimental results show that our distributed algorithm is scalable and more efficient than the existing sequential convoy pattern mining algorithms. |
