Mining Long, Sharable Patterns in Trajectories of Moving Objects

3rd STDBM Workshop on Spatio-Temporal Database Management

Gyozo Gidófalvi ; Torben Bach Pedersen

Others  :  http://CEUR-WS.org/Vol-174/paper7.pdf
PID  :  12057

来源: CEUR

【 摘 要 】

The efficient analysis of spatio–temporal data, generated by moving objects, is an essential requirement for intelligent location– based services. Spatio-temporal rules can be found by constructing spatio–temporal baskets, from which traditional association rule mining methods can discover spatio–temporal rules. When the items in the baskets are spatio–temporal identifiers and are derived from trajectories of moving objects, the discovered rules represent frequently travelled routes. For some applications, e.g., an intelligent ridesharing application, these frequent routes are only interesting if they are long and sharable, i.e., can potentially be shared by several users. This paper presents a data- base projection based method for efficiently extracting such long, sharable frequent routes. The method prunes the search space by making use of the minimum length and sharable requirements and avoids the generation of the exponential number of sub–routes of long routes. A SQL–based implementation is described, and experiments on real life data