Semantic data modeling of spatiotemporal database applications

Adnan Yazici, Qinwei Zhu, Ning Sun

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

Due to the ubiquity of space-related and time-related information, the ability of a database system to deal with both spatial and temporal phenomenon facts in a spatiotemporal applications is highly desired. However, uncertain and fuzzy information in these applications highly increases the complexity of database modeling. In this paper we introduce a semantic data modeling approach for spatiotemporal database applications. We specifically focus on various aspects of spatial and temporal database issues and uncertainty and fuzziness in various abstract levels. The semantic data model that we introduce in this paper utilizes unified modeling language (UML) for handling spatiotemporal information, uncertainty, and fuzziness especially at the conceptual level of database design. An environmental information system (EIS) application is used to illustrate our modeling approach and extension made to UML. By incorporating uncertainty and fuzziness into the semantic data model of a spatiotemporal EIS database application, one can handle pollution summary, analysis, and even pollution predictions, in addition to the other common uses of a database system.

Original languageEnglish
Pages (from-to)881-904
Number of pages24
JournalInternational Journal of Intelligent Systems
Volume16
Issue number7
DOIs
Publication statusPublished - Jul 1 2001
Externally publishedYes

Fingerprint

Spatio-temporal Databases
Data Modeling
Data structures
Semantics
Fuzziness
Unified Modeling Language
Database Systems
Pollution
Uncertainty
Data Model
Information Systems
Temporal Databases
Database Design
Spatial Database
Fuzzy Information
Information systems
Modeling
Prediction

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Human-Computer Interaction
  • Artificial Intelligence

Cite this

Semantic data modeling of spatiotemporal database applications. / Yazici, Adnan; Zhu, Qinwei; Sun, Ning.

In: International Journal of Intelligent Systems, Vol. 16, No. 7, 01.07.2001, p. 881-904.

Research output: Contribution to journalArticle

@article{e5d956d2106942e888da6c862044dfe8,
title = "Semantic data modeling of spatiotemporal database applications",
abstract = "Due to the ubiquity of space-related and time-related information, the ability of a database system to deal with both spatial and temporal phenomenon facts in a spatiotemporal applications is highly desired. However, uncertain and fuzzy information in these applications highly increases the complexity of database modeling. In this paper we introduce a semantic data modeling approach for spatiotemporal database applications. We specifically focus on various aspects of spatial and temporal database issues and uncertainty and fuzziness in various abstract levels. The semantic data model that we introduce in this paper utilizes unified modeling language (UML) for handling spatiotemporal information, uncertainty, and fuzziness especially at the conceptual level of database design. An environmental information system (EIS) application is used to illustrate our modeling approach and extension made to UML. By incorporating uncertainty and fuzziness into the semantic data model of a spatiotemporal EIS database application, one can handle pollution summary, analysis, and even pollution predictions, in addition to the other common uses of a database system.",
author = "Adnan Yazici and Qinwei Zhu and Ning Sun",
year = "2001",
month = "7",
day = "1",
doi = "10.1002/int.1040",
language = "English",
volume = "16",
pages = "881--904",
journal = "International Journal of Intelligent Systems",
issn = "0884-8173",
publisher = "John Wiley and Sons Ltd",
number = "7",

}

TY - JOUR

T1 - Semantic data modeling of spatiotemporal database applications

AU - Yazici, Adnan

AU - Zhu, Qinwei

AU - Sun, Ning

PY - 2001/7/1

Y1 - 2001/7/1

N2 - Due to the ubiquity of space-related and time-related information, the ability of a database system to deal with both spatial and temporal phenomenon facts in a spatiotemporal applications is highly desired. However, uncertain and fuzzy information in these applications highly increases the complexity of database modeling. In this paper we introduce a semantic data modeling approach for spatiotemporal database applications. We specifically focus on various aspects of spatial and temporal database issues and uncertainty and fuzziness in various abstract levels. The semantic data model that we introduce in this paper utilizes unified modeling language (UML) for handling spatiotemporal information, uncertainty, and fuzziness especially at the conceptual level of database design. An environmental information system (EIS) application is used to illustrate our modeling approach and extension made to UML. By incorporating uncertainty and fuzziness into the semantic data model of a spatiotemporal EIS database application, one can handle pollution summary, analysis, and even pollution predictions, in addition to the other common uses of a database system.

AB - Due to the ubiquity of space-related and time-related information, the ability of a database system to deal with both spatial and temporal phenomenon facts in a spatiotemporal applications is highly desired. However, uncertain and fuzzy information in these applications highly increases the complexity of database modeling. In this paper we introduce a semantic data modeling approach for spatiotemporal database applications. We specifically focus on various aspects of spatial and temporal database issues and uncertainty and fuzziness in various abstract levels. The semantic data model that we introduce in this paper utilizes unified modeling language (UML) for handling spatiotemporal information, uncertainty, and fuzziness especially at the conceptual level of database design. An environmental information system (EIS) application is used to illustrate our modeling approach and extension made to UML. By incorporating uncertainty and fuzziness into the semantic data model of a spatiotemporal EIS database application, one can handle pollution summary, analysis, and even pollution predictions, in addition to the other common uses of a database system.

UR - http://www.scopus.com/inward/record.url?scp=0035396817&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0035396817&partnerID=8YFLogxK

U2 - 10.1002/int.1040

DO - 10.1002/int.1040

M3 - Article

VL - 16

SP - 881

EP - 904

JO - International Journal of Intelligent Systems

JF - International Journal of Intelligent Systems

SN - 0884-8173

IS - 7

ER -