On the Generation of Time-Evolving Regional Data*
Abstract Benchmarking of spatio-temporal databases is an issue of growing importance. In case large real data sets are not available, benchmarking requires the generation of artificial data sets following the real-world behavior of spatial objects that change their locations, shapes and sizes over t...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Tzouramanis, Theodoros [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2002 |
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| Anmerkung: |
© Kluwer Academic Publishers 2002 |
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| Übergeordnetes Werk: |
Enthalten in: Geoinformatica - Kluwer Academic Publishers, 1997, 6(2002), 3 vom: Sept., Seite 207-231 |
|---|---|
| Übergeordnetes Werk: |
volume:6 ; year:2002 ; number:3 ; month:09 ; pages:207-231 |
| Links: |
|---|
| DOI / URN: |
10.1023/A:1019705618917 |
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OLC2038960895 |
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| 520 | |a Abstract Benchmarking of spatio-temporal databases is an issue of growing importance. In case large real data sets are not available, benchmarking requires the generation of artificial data sets following the real-world behavior of spatial objects that change their locations, shapes and sizes over time. Only a few innovative papers have recently addressed the topic of spatio-temporal data generators. However, all existing approaches do not consider several important aspects of continuously changing regional data. In this report, a new generator, called generator of time-evolving regional data (G-TERD), for this class of data is presented. The basic concepts that determine the function of G-TERD are the structure of complex 2-D regional objects, their color, maximum speed, zoom and rotation-angle per time slot, the influence of other moving or static objects on the speed and on the moving direction of an object, the position and movement of the scene-observer, the statistical distribution of each changing factor and finally, time. Apart from these concepts, the operation and basic algorithmic issues of G-TERD are presented. In the framework developed, the user can control the generator response by setting several parameters values. To demonstrate the use of G-TERD, the generation of a number of sample data sets is presented and commented. The source code and a visualization tool for using and testing the new generator are available on the Web.1 Thus, it is easy for the user to manipulate the generator according to specific application requirements and at the same time to examine the reliability of the underlying generalized data model. | ||
| 700 | 1 | |a Vassilakopoulos, Michael |4 aut | |
| 700 | 1 | |a Manolopoulos, Yannis |4 aut | |
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10.1023/A:1019705618917 doi (DE-627)OLC2038960895 (DE-He213)A:1019705618917-p DE-627 ger DE-627 rakwb eng 550 VZ Tzouramanis, Theodoros verfasserin aut On the Generation of Time-Evolving Regional Data* 2002 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 2002 Abstract Benchmarking of spatio-temporal databases is an issue of growing importance. In case large real data sets are not available, benchmarking requires the generation of artificial data sets following the real-world behavior of spatial objects that change their locations, shapes and sizes over time. Only a few innovative papers have recently addressed the topic of spatio-temporal data generators. However, all existing approaches do not consider several important aspects of continuously changing regional data. In this report, a new generator, called generator of time-evolving regional data (G-TERD), for this class of data is presented. The basic concepts that determine the function of G-TERD are the structure of complex 2-D regional objects, their color, maximum speed, zoom and rotation-angle per time slot, the influence of other moving or static objects on the speed and on the moving direction of an object, the position and movement of the scene-observer, the statistical distribution of each changing factor and finally, time. Apart from these concepts, the operation and basic algorithmic issues of G-TERD are presented. In the framework developed, the user can control the generator response by setting several parameters values. To demonstrate the use of G-TERD, the generation of a number of sample data sets is presented and commented. The source code and a visualization tool for using and testing the new generator are available on the Web.1 Thus, it is easy for the user to manipulate the generator according to specific application requirements and at the same time to examine the reliability of the underlying generalized data model. Vassilakopoulos, Michael aut Manolopoulos, Yannis aut Enthalten in Geoinformatica Kluwer Academic Publishers, 1997 6(2002), 3 vom: Sept., Seite 207-231 (DE-627)223334499 (DE-600)1357836-4 (DE-576)307633454 1384-6175 nnns volume:6 year:2002 number:3 month:09 pages:207-231 https://doi.org/10.1023/A:1019705618917 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO GBV_ILN_11 GBV_ILN_70 GBV_ILN_100 GBV_ILN_131 GBV_ILN_4318 GBV_ILN_4700 AR 6 2002 3 09 207-231 |
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10.1023/A:1019705618917 doi (DE-627)OLC2038960895 (DE-He213)A:1019705618917-p DE-627 ger DE-627 rakwb eng 550 VZ Tzouramanis, Theodoros verfasserin aut On the Generation of Time-Evolving Regional Data* 2002 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 2002 Abstract Benchmarking of spatio-temporal databases is an issue of growing importance. In case large real data sets are not available, benchmarking requires the generation of artificial data sets following the real-world behavior of spatial objects that change their locations, shapes and sizes over time. Only a few innovative papers have recently addressed the topic of spatio-temporal data generators. However, all existing approaches do not consider several important aspects of continuously changing regional data. In this report, a new generator, called generator of time-evolving regional data (G-TERD), for this class of data is presented. The basic concepts that determine the function of G-TERD are the structure of complex 2-D regional objects, their color, maximum speed, zoom and rotation-angle per time slot, the influence of other moving or static objects on the speed and on the moving direction of an object, the position and movement of the scene-observer, the statistical distribution of each changing factor and finally, time. Apart from these concepts, the operation and basic algorithmic issues of G-TERD are presented. In the framework developed, the user can control the generator response by setting several parameters values. To demonstrate the use of G-TERD, the generation of a number of sample data sets is presented and commented. The source code and a visualization tool for using and testing the new generator are available on the Web.1 Thus, it is easy for the user to manipulate the generator according to specific application requirements and at the same time to examine the reliability of the underlying generalized data model. Vassilakopoulos, Michael aut Manolopoulos, Yannis aut Enthalten in Geoinformatica Kluwer Academic Publishers, 1997 6(2002), 3 vom: Sept., Seite 207-231 (DE-627)223334499 (DE-600)1357836-4 (DE-576)307633454 1384-6175 nnns volume:6 year:2002 number:3 month:09 pages:207-231 https://doi.org/10.1023/A:1019705618917 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO GBV_ILN_11 GBV_ILN_70 GBV_ILN_100 GBV_ILN_131 GBV_ILN_4318 GBV_ILN_4700 AR 6 2002 3 09 207-231 |
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10.1023/A:1019705618917 doi (DE-627)OLC2038960895 (DE-He213)A:1019705618917-p DE-627 ger DE-627 rakwb eng 550 VZ Tzouramanis, Theodoros verfasserin aut On the Generation of Time-Evolving Regional Data* 2002 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 2002 Abstract Benchmarking of spatio-temporal databases is an issue of growing importance. In case large real data sets are not available, benchmarking requires the generation of artificial data sets following the real-world behavior of spatial objects that change their locations, shapes and sizes over time. Only a few innovative papers have recently addressed the topic of spatio-temporal data generators. However, all existing approaches do not consider several important aspects of continuously changing regional data. In this report, a new generator, called generator of time-evolving regional data (G-TERD), for this class of data is presented. The basic concepts that determine the function of G-TERD are the structure of complex 2-D regional objects, their color, maximum speed, zoom and rotation-angle per time slot, the influence of other moving or static objects on the speed and on the moving direction of an object, the position and movement of the scene-observer, the statistical distribution of each changing factor and finally, time. Apart from these concepts, the operation and basic algorithmic issues of G-TERD are presented. In the framework developed, the user can control the generator response by setting several parameters values. To demonstrate the use of G-TERD, the generation of a number of sample data sets is presented and commented. The source code and a visualization tool for using and testing the new generator are available on the Web.1 Thus, it is easy for the user to manipulate the generator according to specific application requirements and at the same time to examine the reliability of the underlying generalized data model. Vassilakopoulos, Michael aut Manolopoulos, Yannis aut Enthalten in Geoinformatica Kluwer Academic Publishers, 1997 6(2002), 3 vom: Sept., Seite 207-231 (DE-627)223334499 (DE-600)1357836-4 (DE-576)307633454 1384-6175 nnns volume:6 year:2002 number:3 month:09 pages:207-231 https://doi.org/10.1023/A:1019705618917 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO GBV_ILN_11 GBV_ILN_70 GBV_ILN_100 GBV_ILN_131 GBV_ILN_4318 GBV_ILN_4700 AR 6 2002 3 09 207-231 |
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10.1023/A:1019705618917 doi (DE-627)OLC2038960895 (DE-He213)A:1019705618917-p DE-627 ger DE-627 rakwb eng 550 VZ Tzouramanis, Theodoros verfasserin aut On the Generation of Time-Evolving Regional Data* 2002 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 2002 Abstract Benchmarking of spatio-temporal databases is an issue of growing importance. In case large real data sets are not available, benchmarking requires the generation of artificial data sets following the real-world behavior of spatial objects that change their locations, shapes and sizes over time. Only a few innovative papers have recently addressed the topic of spatio-temporal data generators. However, all existing approaches do not consider several important aspects of continuously changing regional data. In this report, a new generator, called generator of time-evolving regional data (G-TERD), for this class of data is presented. The basic concepts that determine the function of G-TERD are the structure of complex 2-D regional objects, their color, maximum speed, zoom and rotation-angle per time slot, the influence of other moving or static objects on the speed and on the moving direction of an object, the position and movement of the scene-observer, the statistical distribution of each changing factor and finally, time. Apart from these concepts, the operation and basic algorithmic issues of G-TERD are presented. In the framework developed, the user can control the generator response by setting several parameters values. To demonstrate the use of G-TERD, the generation of a number of sample data sets is presented and commented. The source code and a visualization tool for using and testing the new generator are available on the Web.1 Thus, it is easy for the user to manipulate the generator according to specific application requirements and at the same time to examine the reliability of the underlying generalized data model. Vassilakopoulos, Michael aut Manolopoulos, Yannis aut Enthalten in Geoinformatica Kluwer Academic Publishers, 1997 6(2002), 3 vom: Sept., Seite 207-231 (DE-627)223334499 (DE-600)1357836-4 (DE-576)307633454 1384-6175 nnns volume:6 year:2002 number:3 month:09 pages:207-231 https://doi.org/10.1023/A:1019705618917 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO GBV_ILN_11 GBV_ILN_70 GBV_ILN_100 GBV_ILN_131 GBV_ILN_4318 GBV_ILN_4700 AR 6 2002 3 09 207-231 |
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10.1023/A:1019705618917 doi (DE-627)OLC2038960895 (DE-He213)A:1019705618917-p DE-627 ger DE-627 rakwb eng 550 VZ Tzouramanis, Theodoros verfasserin aut On the Generation of Time-Evolving Regional Data* 2002 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 2002 Abstract Benchmarking of spatio-temporal databases is an issue of growing importance. In case large real data sets are not available, benchmarking requires the generation of artificial data sets following the real-world behavior of spatial objects that change their locations, shapes and sizes over time. Only a few innovative papers have recently addressed the topic of spatio-temporal data generators. However, all existing approaches do not consider several important aspects of continuously changing regional data. In this report, a new generator, called generator of time-evolving regional data (G-TERD), for this class of data is presented. The basic concepts that determine the function of G-TERD are the structure of complex 2-D regional objects, their color, maximum speed, zoom and rotation-angle per time slot, the influence of other moving or static objects on the speed and on the moving direction of an object, the position and movement of the scene-observer, the statistical distribution of each changing factor and finally, time. Apart from these concepts, the operation and basic algorithmic issues of G-TERD are presented. In the framework developed, the user can control the generator response by setting several parameters values. To demonstrate the use of G-TERD, the generation of a number of sample data sets is presented and commented. The source code and a visualization tool for using and testing the new generator are available on the Web.1 Thus, it is easy for the user to manipulate the generator according to specific application requirements and at the same time to examine the reliability of the underlying generalized data model. Vassilakopoulos, Michael aut Manolopoulos, Yannis aut Enthalten in Geoinformatica Kluwer Academic Publishers, 1997 6(2002), 3 vom: Sept., Seite 207-231 (DE-627)223334499 (DE-600)1357836-4 (DE-576)307633454 1384-6175 nnns volume:6 year:2002 number:3 month:09 pages:207-231 https://doi.org/10.1023/A:1019705618917 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO GBV_ILN_11 GBV_ILN_70 GBV_ILN_100 GBV_ILN_131 GBV_ILN_4318 GBV_ILN_4700 AR 6 2002 3 09 207-231 |
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On the Generation of Time-Evolving Regional Data* |
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Abstract Benchmarking of spatio-temporal databases is an issue of growing importance. In case large real data sets are not available, benchmarking requires the generation of artificial data sets following the real-world behavior of spatial objects that change their locations, shapes and sizes over time. Only a few innovative papers have recently addressed the topic of spatio-temporal data generators. However, all existing approaches do not consider several important aspects of continuously changing regional data. In this report, a new generator, called generator of time-evolving regional data (G-TERD), for this class of data is presented. The basic concepts that determine the function of G-TERD are the structure of complex 2-D regional objects, their color, maximum speed, zoom and rotation-angle per time slot, the influence of other moving or static objects on the speed and on the moving direction of an object, the position and movement of the scene-observer, the statistical distribution of each changing factor and finally, time. Apart from these concepts, the operation and basic algorithmic issues of G-TERD are presented. In the framework developed, the user can control the generator response by setting several parameters values. To demonstrate the use of G-TERD, the generation of a number of sample data sets is presented and commented. The source code and a visualization tool for using and testing the new generator are available on the Web.1 Thus, it is easy for the user to manipulate the generator according to specific application requirements and at the same time to examine the reliability of the underlying generalized data model. © Kluwer Academic Publishers 2002 |
| abstractGer |
Abstract Benchmarking of spatio-temporal databases is an issue of growing importance. In case large real data sets are not available, benchmarking requires the generation of artificial data sets following the real-world behavior of spatial objects that change their locations, shapes and sizes over time. Only a few innovative papers have recently addressed the topic of spatio-temporal data generators. However, all existing approaches do not consider several important aspects of continuously changing regional data. In this report, a new generator, called generator of time-evolving regional data (G-TERD), for this class of data is presented. The basic concepts that determine the function of G-TERD are the structure of complex 2-D regional objects, their color, maximum speed, zoom and rotation-angle per time slot, the influence of other moving or static objects on the speed and on the moving direction of an object, the position and movement of the scene-observer, the statistical distribution of each changing factor and finally, time. Apart from these concepts, the operation and basic algorithmic issues of G-TERD are presented. In the framework developed, the user can control the generator response by setting several parameters values. To demonstrate the use of G-TERD, the generation of a number of sample data sets is presented and commented. The source code and a visualization tool for using and testing the new generator are available on the Web.1 Thus, it is easy for the user to manipulate the generator according to specific application requirements and at the same time to examine the reliability of the underlying generalized data model. © Kluwer Academic Publishers 2002 |
| abstract_unstemmed |
Abstract Benchmarking of spatio-temporal databases is an issue of growing importance. In case large real data sets are not available, benchmarking requires the generation of artificial data sets following the real-world behavior of spatial objects that change their locations, shapes and sizes over time. Only a few innovative papers have recently addressed the topic of spatio-temporal data generators. However, all existing approaches do not consider several important aspects of continuously changing regional data. In this report, a new generator, called generator of time-evolving regional data (G-TERD), for this class of data is presented. The basic concepts that determine the function of G-TERD are the structure of complex 2-D regional objects, their color, maximum speed, zoom and rotation-angle per time slot, the influence of other moving or static objects on the speed and on the moving direction of an object, the position and movement of the scene-observer, the statistical distribution of each changing factor and finally, time. Apart from these concepts, the operation and basic algorithmic issues of G-TERD are presented. In the framework developed, the user can control the generator response by setting several parameters values. To demonstrate the use of G-TERD, the generation of a number of sample data sets is presented and commented. The source code and a visualization tool for using and testing the new generator are available on the Web.1 Thus, it is easy for the user to manipulate the generator according to specific application requirements and at the same time to examine the reliability of the underlying generalized data model. © Kluwer Academic Publishers 2002 |
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| title_short |
On the Generation of Time-Evolving Regional Data* |
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https://doi.org/10.1023/A:1019705618917 |
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| author2 |
Vassilakopoulos, Michael Manolopoulos, Yannis |
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Vassilakopoulos, Michael Manolopoulos, Yannis |
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| doi_str |
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| up_date |
2024-07-03T21:00:59.256Z |
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