Advances in Databases and Information Systems, Lecture Notes in Computer Science

Recent data acquisition tools have resulted in huge amounts of data that have spatial and temporal components. The movement represents an important category of such data. Some phenomena may have attributes that vary continuously over space, such as wildfires and storms. Nevertheless, for simplificat...

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Main Authors: Ibrahim, A., Turdukulov, Ulanbek, Kraak, M.
Other Authors: Yannis Manolopoulos
Format: Book Chapter
Published: Springer 2014
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/11433
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author Ibrahim, A.
Turdukulov, Ulanbek
Kraak, M.
author2 Yannis Manolopoulos
author_facet Yannis Manolopoulos
Ibrahim, A.
Turdukulov, Ulanbek
Kraak, M.
author_sort Ibrahim, A.
building Curtin Institutional Repository
collection Online Access
description Recent data acquisition tools have resulted in huge amounts of data that have spatial and temporal components. The movement represents an important category of such data. Some phenomena may have attributes that vary continuously over space, such as wildfires and storms. Nevertheless, for simplification purpose, most applications represent such phenomena as objects by neglecting their internal continuous structure. Moreover, little consideration has been given to such characteristics in moving objects database. At this end, this paper presents a data model for managing raster data and internal heterogenous attributes in moving objects. The data model utilizes the abstract data types. We add two abstractions (moving raster, and combined type) to describe the change of the raster data and internal varying characteristics of the moving objects along with specific operations that permit to analyse them. Query examples are provided to demonstrate the application of these operations.
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spelling curtin-20.500.11937-114332023-02-13T08:01:37Z Advances in Databases and Information Systems, Lecture Notes in Computer Science Ibrahim, A. Turdukulov, Ulanbek Kraak, M. Yannis Manolopoulos Goce Trajcevski Margita Kon-Popovska internal varying characteristics Spatiotemporal data model moving objects moving objects databases Recent data acquisition tools have resulted in huge amounts of data that have spatial and temporal components. The movement represents an important category of such data. Some phenomena may have attributes that vary continuously over space, such as wildfires and storms. Nevertheless, for simplification purpose, most applications represent such phenomena as objects by neglecting their internal continuous structure. Moreover, little consideration has been given to such characteristics in moving objects database. At this end, this paper presents a data model for managing raster data and internal heterogenous attributes in moving objects. The data model utilizes the abstract data types. We add two abstractions (moving raster, and combined type) to describe the change of the raster data and internal varying characteristics of the moving objects along with specific operations that permit to analyse them. Query examples are provided to demonstrate the application of these operations. 2014 Book Chapter http://hdl.handle.net/20.500.11937/11433 10.1007/978-3-319-10933-6_16 Springer restricted
spellingShingle internal varying characteristics
Spatiotemporal data model
moving objects
moving objects databases
Ibrahim, A.
Turdukulov, Ulanbek
Kraak, M.
Advances in Databases and Information Systems, Lecture Notes in Computer Science
title Advances in Databases and Information Systems, Lecture Notes in Computer Science
title_full Advances in Databases and Information Systems, Lecture Notes in Computer Science
title_fullStr Advances in Databases and Information Systems, Lecture Notes in Computer Science
title_full_unstemmed Advances in Databases and Information Systems, Lecture Notes in Computer Science
title_short Advances in Databases and Information Systems, Lecture Notes in Computer Science
title_sort advances in databases and information systems, lecture notes in computer science
topic internal varying characteristics
Spatiotemporal data model
moving objects
moving objects databases
url http://hdl.handle.net/20.500.11937/11433