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
