چكيده به لاتين
Nowadays, many improvements have been achieved in moving objects databases. The proliferation of positioning devices such as smart phones, RFID tags and vehicle navigation systems and development in wireless technologies have resulted in an increasing growth in location-based services. Tracking of moving in different applications such as Intelligent Transportation Systems (ITS), Location-Based Services (LBS), tourism and location-based social networks has resulted in massive amounts of data. The exponential increase in the amount of such trajectory data caused communicational and storage problems and it is difficult to run spatio-temporal queries. Storing and processing this amount of data requires a great deal of resources and time which highlight the need of scalable and efficient methods to store data and answer queries.
Utilizing the data in the databases is dependent on the efficient and efficient processing of trajectory queries. The purpose of the query is to evaluate the spatio-temporal communication between the data of objects in space. It is clear that spatio-temporal communication in the query is not just topological communication, but also measures of distance between spatial objects; simple criteria such as Euclidean distance or complex criteria such as the similarity between the sequences. There are challenges to define the similarity between two moving objects. In addition to changing location, speed and semantic features are also different. Most of the methods used to find the similarity of the sequences are geologically similar. In recent researches, the finding of the similarity of the errors to the semantic concepts has expanded. The study and use of distance measures and the similarity between moving objects is of great importance in many applications in the field of location-based systems and intelligent transportation systems.
In this research, algorithms and similarity criteria have been investigated first. Then, a categorization and comparison of the available techniques are presented. Finally, we propose a methodology that combines an optimal geographic metric with some semantic concepts and, with their help, we obtain a similarity criterion with better accuracy. Finally, the proposed method is evaluated and the results are presented.
