AN EFFICIENT PROCESSING OF SPATIO TEMPORAL AGGREGATE QUERIES

Abstract

With recent advances in positioning, telemetry, and telecommunication technologies, the wide availability of devices that produce information about the position of an object in some time, enormous amounts of data about moving objects are being collected and employed by many applications. Moving objects are objects (points) that change their locations (geometric attributes) over time, which requires a higher update frequency. Querying and analyzing this data can give more insightful knowledge on the pattern of the mobility of the object and the interest evinced by visitors in a geographic location. AN EFFICIENT PROCESSING OF SPATIO TEMPORAL AGGREGATE QUERIES uses an algorithm called SemTraClus which helps in identifying, clustering, and prioritizing semantic regions. Significant locations of a geographical area called “Points of Interest” are extracted using three main methods stay point detection, revisited locations, and intersecting points of different trajectories. These identified regions are clustered using the DBSCAN method and finally, it generates a Weightage Participation value which provides priorities of user interest in different semantic cluster regions. The approach is evaluated through experiments and compared to existing methods. The results show that the proposed approach was able to identify significant locations and prioritize them. In my project many hidden semantic regions have been identified by considering the spatial, temporal, and semantic factors of moving objects, this knowledge can help many application areas like transportation systems for setting up the architectural platform, design of supply chain networks, preparations of travel itinerary of tourist, etc