Research On Trajectory Frequent Patterns In Cloud Computing Environment

With the rapid development of the information technology, Global Position System devices and mobile terminals with positioning function are widely-spread,moreover,mobile internet promotes the development of the LBS(Location Based Service),so spatial trajectory data has been mounting up. Mining the frequent pattern from large trajectory data efficiently is significant to understanding the move objects’travel habit and pattern of movement.However, it is a great challenge that mining the pattern from the huge amounts of trajectory data quickly.Cloud computing which has a new parallel computing model provides an efficient solution for mining large data.This thesis focuses on mining the frequent trajectory pattern by parallel programming framework named MapReduce.The main achievements are as follows:1. Proposed the method to discover the regions of interest by the stop point of trajectory.Some coordinate points of spatio-temporal in the trajectory are important,such as the points sampled in shopping mall.How to find region of interest such as shopping mall is meaningful to understand user behavior. this thesis using the stop point of trajectory to find regions of interest.First.geographic space is divided into a set of grid cells.Then, calculate the number of the stop point for each grid cell.If the number is greater than certain threshold,this grid cell is interest cell,last, merge the interest cells to find regions of interest.2. Based on grid cells to find the frequent subtrajectory pattern parallel.With the wide use of positioning technology,every day has mass trajectory data to deal,and it is slow to discover frequent pattern in single computer.so it is necessary to mining the trajectory pattern in parallel environment.Using the spatial characteristics of trajectory data to find frequent pattern parallel.First, geographic space is divided into a set of grid cells.Then,the trajectories located in different grid cell are computed by different computer.In order to avoid the boundary problem,every computer also compute the trajectories in the grid which adjacent to grid that belong to this computer.3. Using the suffix tree to mine the frequent trajectory pattern parallel Spatio-temporal trajectory is location sequence in time dimension.Frequent sequence represents travel habit of the moving object.Through the suffix tree it can find the frequent location sequence from trajectory data.First of all according to the regions of interest to represent trajectory as a sequence of regions of interest with time tags.Second.Construct the suffix tree of the sequences to mining the frequent trajectory pattern.In order to improve the efficiency of the algorithm,also use the MapReduce programming framework to construct the suffix tree.