نمونه ترجمه تخصصی هوش مصنوعی درباره رهگیری خط سیر اشیاء متحرک

Trajectory

The exponential growth of trajectory data volume has created challenges like storage, costly query computations, and increase in the costs and time of transferring information via mobile and satellite networks. Dealing with such issues needs scalable and effective solutions and one of them is horizontal expansion and utilization of distributed models to store and retrieve spatial-temporal information. The present paper proposes a distributed method known as distributed indexing method (DIM) for indexing, storing, and responding to queries about the trajectory data of moving objects. Taking into account queries and using the data structure of interval trees, not only the factor of time is added to data indexing by DIM, but also the storage process of spatial-temporal data of moving objects trajectory on road network is performed in a distributed manner. The effects of DIM method on creating index and responding to time interval, spatial range, spatial/temporal range, and kNN queries were examined. The results of comparisons performed on the empirical tests indicated high performance of the indexing structure. An ever-increasing development of location-based services has been witnessed along with expansion of accurate positioning devices like GPS, AIS, smart phones, RFID tags, navigation systems, and vehicles and given the decrease of prices accelerated by wireless communication technology. Consequently, the necessity of tracking moving objects is growing on daily bases. A wide range of applications needs storing and processing spatial information for extracting useful models from the data and respond to spatial-temporal queries. Among these applications, traffic management systems, smart transportation, tourism, location base social networks, and even computer games are notable. This trend has led to generation of an immense volume of data and storing and processing it needs considerable time and resources. This demand highlights the necessity of scalable and efficient methods for storing and responding to queries. A proper solution that has drawn a great deal of attention over the last few years is horizontal expansion and distributed models for storing and retrieving spatial-temporal information.