LAUSR

The ever-increasing volume of trajectories of moving objects and the diversity of intelligent transportation systems and location-based services that rely on spatio-temporal data of moving objects highlight the need for more efficient indexing techniques. The state-of-the-art methods index moving objects at three time modes of past (historical data), present, and future. An integrated method called “PCI” was proposed (past current indexing) to index and store spatial-temporal data of the past and present simultaneously. The method can handle queries in both the time modes and it processes and generates both the past and present indices using an integrated set of processing resources. Two interconnected data structures were utilized to store indices of both the time modes. Connecting the index of different time modes enforces efficiency challenges due to difference in updating costs. Since the method stores the indices in the main memory, the way the structures are connected to each other makes it possible to transfer the current data to the section responsible for historical data. This method indexes them in the trajectory of moving objects at a minimum time expense. As the quality of data inevitably affects the performance of applications, map matching methods was used in PCI to remove noises—e.g., stationary state noises—in the data received from the moving objects. This feature adds to the accuracy and reliability of query results. The effects of data reduction techniques on accelerating indexing, query processing, and reducing memory consumption (in voluminous data sets) were examined. Results of the comparisons, made based on the experiments, showed the higher efficiency of the indexing structure.