moving object database
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2019 ◽  
Vol 8 (4) ◽  
pp. 170 ◽  
Author(s):  
Alejandro Vaisman ◽  
Esteban Zimányi

The interest in mobility data analysis has grown dramatically with the wide availability of devices that track the position of moving objects. Mobility analysis can be applied, for example, to analyze traffic flows. To support mobility analysis, trajectory data warehousing techniques can be used. Trajectory data warehouses typically include, as measures, segments of trajectories, linked to spatial and non-spatial contextual dimensions. This paper goes beyond this concept, by including, as measures, the trajectories of moving objects at any point in time. In this way, online analytical processing (OLAP) queries, typically including aggregation, can be combined with moving object queries, to express queries like “List the total number of trucks running at less than 2 km from each other more than 50% of its route in the province of Antwerp” in a concise and elegant way. Existing proposals for trajectory data warehouses do not support queries like this, since they are based on either the segmentation of the trajectories, or a pre-aggregation of measures. The solution presented here is implemented using MobilityDB, a moving object database that extends the PostgresSQL database with temporal data types, allowing seamless integration with relational spatial and non-spatial data. This integration leads to the concept of mobility data warehouses. This paper discusses modeling and querying mobility data warehouses, providing a comprehensive collection of queries implemented using PostgresSQL and PostGIS as database backend, extended with the libraries provided by MobilityDB.



2018 ◽  
Vol 26 (5) ◽  
pp. 551-561
Author(s):  
Esraa Rslan ◽  
Hala Abdelhameed ◽  
Ehab Ezzat


2015 ◽  
Vol 16 (4) ◽  
pp. 1918-1928 ◽  
Author(s):  
Zhiming Ding ◽  
Bin Yang ◽  
Ralf Hartmut Guting ◽  
Yaguang Li


2014 ◽  
Vol 530-531 ◽  
pp. 823-826 ◽  
Author(s):  
Chun Ping Wang

Since the mobile radio communication in a wireless mobile environment, which inevitably subject to fading and interference effects of the various signals, multipath and time-domain signal to bring the mobile domain and frequency dispersion problems, bandwidth resources restricted and transmission delay increases and so on. In this paper, the use of Class R-Tree data structure to store the data on the basis of historical trajectory, the update query results by the beach line iterative way to solve the problem of nearest neighbor queries efficiently update existing intermediate results.



Author(s):  
Nikos Pelekis ◽  
Yannis Theodoridis


2011 ◽  
Vol 38 (12) ◽  
pp. 15075-15083 ◽  
Author(s):  
Jaegeol Yim ◽  
Jaehun Joo ◽  
Chansik Park


2011 ◽  
Vol 21 (2) ◽  
pp. 265-286 ◽  
Author(s):  
Su Chen ◽  
Beng Chin Ooi ◽  
Zhenjie Zhang




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