Using compressed index structures for processing moving objects in large spatio-temporal databases

2012 ◽  
Vol 85 (1) ◽  
pp. 167-177 ◽  
Author(s):  
Hung-Yi Lin
Keyword(s):  
Moving Objects ◽  
Temporal Databases ◽  
Index Structures ◽  
Spatio Temporal ◽  
Compressed Index

Management of Large Moving Objects Databases

2011 ◽  
pp. 225-250 ◽  
Author(s):  
Talel Abdessalem ◽  
Cédric du Mouza ◽  
José Moreira ◽  
Philippe Rigaux
Keyword(s):  
Moving Objects ◽  
Temporal Databases ◽  
Index Structures ◽  
Moving Objects Databases ◽  
Spatio Temporal

This chapter deals with several important issues pertaining to the management of moving objects datasets in databases. The design of representative benchmarks is closely related to the formal characterization of the properties (that is, distribution, speed, nature of movement) of these datasets; uncertainty is another important aspect that conditions the accuracy of the representation and therefore the confidence in query results; finally, efficient index structures, along with their compatibility with existing softwares, is a crucial requirement for spatio-temporal databases, as it is for any other kind of data.

Simple and Incremental Nearest-Neighbor Search in Spatio-Temporal Databases

2011 ◽  
pp. 204-224
Author(s):  
Katerina Raptopoulou ◽  
Apostolos N. Papadopoulos ◽  
Yannis Manolopoulos
Keyword(s):  
Traffic Control ◽  
Moving Objects ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Temporal Databases ◽  
Wireless Technologies ◽  
Neighbor Search ◽  
Efficient Processing ◽  
Spatio Temporal ◽  
Nearest Neighbor Queries

The efficient processing of nearest-neighbor queries in databases of moving objects is considered very important for applications such as fleet management, traffic control, digital battlefields and more. Such applications have been rapidly spread due to the fact that mobile computing and wireless technologies nowadays are ubiquitous. This chapter presents important aspects towards simple and incremental nearest-neighbor search for spatio-temporal databases. More specifically, we describe the algorithms that have already been proposed for simple and incremental nearest neighbor queries and present a new algorithm regarding that issue. Finally, we study the problem of keeping a query consistent in the presence of insertions, deletions and updates of moving objects.

Research on Modeling and Indexing of Trajectories of Moving Objects in Road Networks

2013 ◽  
Vol 756-759 ◽  
pp. 1234-1239
Author(s):  
Yan Ling Zheng
Keyword(s):  
Road Network ◽  
Moving Objects ◽  
Road Networks ◽  
Experimental Results ◽  
Index Structure ◽  
Index Structures ◽  
The Road ◽  
The Past ◽  
Spatio Temporal ◽  
Temporal Trajectory

Proposed a new index structure, named MG2R*, can efficiently store and retrieve the past, present and future positions of network-constrained moving objects. It is a two-tier structure. The upper is a MultiGrid-R*-Tree (MGRT for short) that is used to index the road network. The lower is a group of independent R*-Tree. Each R*-Tree is relative to a route in the road network, can index the spatiotemporal trajectory of the moving objects in the road. Moreover, moving objects query is implemented based on this index structure. It compared to other index structures for road-network-based moving objects, such as MON-Tree, the experimental results shown that the MG2R* can effectively improve the query performance of the spatio-temporal trajectory of network-constrained moving objects.

Moving Objects Databases

2005 ◽  
pp. 378-382 ◽  
Author(s):  
M. Andrea Rodríguez-Tastets
Keyword(s):  
Moving Objects ◽  
Temporal Databases ◽  
Geometric Aspect ◽  
Change Over Time ◽  
Moving Objects Databases ◽  
Spatio Temporal ◽  
Over Time

Moving objects databases are particular cases of spatio-temporal databases that represent and manage changes related to the movement of objects. Unlike spatio-temporal applications associated with geographic phenomena where the identity of geographic features may change over time, in moving objects databases the objects maintain their identities but change their locations or shapes through time. That is, it is the geometric aspect of an object that changes rather than the object itself. Within this domain, the most suitable applications are those where objects are cars, airplanes, or any object with regular movements.

scholarly journals Extracting Stops from Spatio-Temporal Trajectories within Dynamic Contextual Features

2021 ◽  
Vol 13 (2) ◽  
pp. 690
Author(s):  
Tao Wu ◽  
Huiqing Shen ◽  
Jianxin Qin ◽  
Longgang Xiang
Keyword(s):  
Moving Objects ◽  
Major Effect ◽  
Location Based Services ◽  
Trajectory Data ◽  
The Road ◽  
Contextual Features ◽  
Novel Approach ◽  
Gps Trajectories ◽  
Time Distance ◽  
Spatio Temporal

Identifying stops from GPS trajectories is one of the main concerns in the study of moving objects and has a major effect on a wide variety of location-based services and applications. Although the spatial and non-spatial characteristics of trajectories have been widely investigated for the identification of stops, few studies have concentrated on the impacts of the contextual features, which are also connected to the road network and nearby Points of Interest (POIs). In order to obtain more precise stop information from moving objects, this paper proposes and implements a novel approach that represents a spatio-temproal dynamics relationship between stopping behaviors and geospatial elements to detect stops. The relationship between the candidate stops based on the standard time–distance threshold approach and the surrounding environmental elements are integrated in a complex way (the mobility context cube) to extract stop features and precisely derive stops using the classifier classification. The methodology presented is designed to reduce the error rate of detection of stops in the work of trajectory data mining. It turns out that 26 features can contribute to recognizing stop behaviors from trajectory data. Additionally, experiments on a real-world trajectory dataset further demonstrate the effectiveness of the proposed approach in improving the accuracy of identifying stops from trajectories.

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