Creating Moving Objects Representations for Spatiotemporal Databases

Creating Moving Objects Representations for Spatiotemporal Databases

José Moreira (University of Aveiro, Portugal), Paulo Dias (University of Aveiro, Portugal) and Luís Paulo (University of Aveiro, Portugal)
Copyright: © 2015 |Pages: 10
DOI: 10.4018/978-1-4666-5888-2.ch163

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The Database Management Systems (DBMS) provide standard methods for storing, managing and querying data efficiently. They have been initially tailored for handling one-dimensional data, such as, strings, dates or numbers, but the success of the relational model and the widespread use of DBMS in many organizations have led to the development of new solutions for dealing with more complex data types. This is the case of spatial DBMS extensions that focus on the representation of geographical objects or phenomena, which can be modeled using abstractions such as points, lines or polygons. Complexity increases because spatial data is usually represented in two or higher dimensional spaces and the topology of spatial entities need to be considered as well. Oracle Spatial and PostGIS are notable examples of spatial DBMS extensions currently in use.

In addition, spatial data may also be dynamic and the spatiotemporal behavior of geographical objects or entities across time is essential for several applications. This has gained particular importance with recent developments in remote sensing and communication technologies. One of the most challenging topic is the representation of continuously changing spatial data about moving objects with extent, hereafter referred to simply as moving objects.

The abstract definition of a moving object is a triple 978-1-4666-5888-2.ch163.m01 where 978-1-4666-5888-2.ch163.m02 is a temporal value, 978-1-4666-5888-2.ch163.m03 is the moving object’s geometry at a certain time and 978-1-4666-5888-2.ch163.m04 is a continuous function defining the transformation of 978-1-4666-5888-2.ch163.m05 during 978-1-4666-5888-2.ch163.m06 (Chomicki, Haesevoets, Kuijpers, & Revesz, 2003). The semantics is978-1-4666-5888-2.ch163.m07,where 978-1-4666-5888-2.ch163.m08 and 978-1-4666-5888-2.ch163.m09 are coordinates, 978-1-4666-5888-2.ch163.m10 is a time instant and 978-1-4666-5888-2.ch163.m11 is a continuous function that describes how the object moves or changes during time. This is an abstract representation of moving objects where the spatial and the temporal values are defined as infinite set of points, which needs to be translated into discrete (finite) data models suitable for implementation in databases. The main spatiotemporal data models were based on constraints (Grumbach, Rigaux, & Segoufin, 2001) and Abstract Data Types (ADT) (Güting, et al., 2000). Subsequent works have mainly followed the ADT approach because this data model can be smoothly built into extensible DBMS such as the object-relational DBMS.

Key Terms in this Chapter

OpenCV: The Open Source Computer Vision is a computer Vison library initially developed by Intel for the development of Computer Vision applications.

Morphing: Stands for a set of methods and tools to create a smooth transformation between a source and a target shape. The shapes may be images in raster format, 2D planar shapes, 2D free-form curves or 3D voxel-based representations.

Oracle Spatial: It is an option component of the Oracle Database that adds features for representation, management and querying of geographic and location-data using Oracle native data types.

PostGIS: Is an extension to the PostgreSQL database that adds features for representation, management and querying of spatial data and mapping.

ImageJ API: Is a library of programming functions for ImageJ image processing software. ImageJ and its Java source code are freely available and in the public domain.

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