چكيده به لاتين
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 moving objects has many applications in different areas such as traffic and transportation management systems, tourism and location-based social networks. As a result of this process massive amounts of data is produced. Storing and processing this amount of data requires a great deal of resources and time which highlights the need for scalable and efficient methods to store data and answer queries. A reasonable solution which have gained a lot of interest over the past few years is horizontal scaling and usage of distributed methods to store and retrieve spatial-temporal data.
Very few works have been done around this subject and some of them ignore the temporal aspect of data and only index spatial part. This point of view limits us on using a wide range of queries and using gathered data for applications such as congested parts over a certain time period. Also in some works the emphasis is on a certain property of data such as the volume and the need to decrease that.
In this research our emphasis is on the range queries, both spatial and temporal. We also consider the nearest neighbor queries and try to come up with a distributed storage system which can efficiently store, index and answer queries for spatial-temporal data on road networks. At the end we also evaluate the proposed method and show the results on a cluster of commodity computers.
Keywords: moving objects, trajectory, spatial-temporal data, indexing, map-reduce
