An object-relational prototype of GIS-based disaster database

Zhou, Yan-xi; Liu, Gangjun; Fu, Erjiang; Zhang, Kefei

doi:10.1016/j.proeps.2009.09.163

Cited by 14 publications

(8 citation statements)

References 70 publications

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“…Research on Object-Relational models has started in 2008 and 2009 by Malinowski [21] and Zhou [22], respectively and continued by Le [23] in subsequent years. The categories of spatio-temporal data models in terms of their structure are shown in Fig.…”

Section: Spatio-temporal Data Modelsmentioning

confidence: 99%

Hair-Oriented Data Model for Spatio-Temporal Data Mining

Madraky

Othman

Hamdan

2015

IRECOS

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Spatio-temporal data are complex in terms of number of attributes for spatial and temporal values, and the data are changing towards time. Traditional method to mining the spatio-temporal data is the fact that the data is stored in data warehouse in un-normalization form as union of spatial and temporal data know as tabular data warehouse. A Hair-Oriented Data Model (HODM) has been proved as a suitable data model for spatio-temporal data. It has reduced the file size and decreased query execution time. The spatio-temporal data stored using the HODM known as Hair-Oriented Data warehouse. However, this paper aims to presents a method to develop spatio-temporal data mining model using the Hair-Oriented data warehouse. The Hair-Oriented data model also provide with various functions for easy maintenance on spatio-temporal data warehouse. Experiment conducted using Climate-change spatio-temporal data set benchmark. Two Climate-change spatio-temporal models been developed using regression and knearest neighbor techniques. The performance of the Hair-Oriented Data Warehouse is evaluated by comparing its performance with traditional tabular data warehouse. The result shows that developing data mining spatio-temporal model using Hair-Oriented data warehouse is faster compare using the tabular data warehouse, therefore it can be concluded that the Hair-Oriented Data Model is suitable for Spatio-temporal data mining.

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Section: Spatio-temporal Data Modelsmentioning

confidence: 99%

Hair-Oriented Data Model for Spatio-Temporal Data Mining

Madraky

Othman

Hamdan

2015

IRECOS

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“…Insufficient supporting information can lead to hazard susceptibility [6]. Investigation techniques have been explored and applied (e.g., object-oriented databases [11], object-relational GIS databases [12], and web-based databases [13]- [15]). Several studies have applied database systems to disasters; however, difficulties are likely to arise with respect to finding appropriate data (e.g., river capacity) and methods to apply rainfall and water-level data for regular monitoring, flood risk assessment and early warning system.…”

Section: Literature Reviewmentioning

confidence: 99%

An Information-Systems Approach to Identifying Flood Risks in Central Japan

Limlahapun¹,

Fukui²

2014

IJESD

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Abstract-This study explored using database environments to develop a tailor-made geospatial web-based system to identify flood risk. This approach can directly target specific needs and has advantages over existing noncommercial systems. We analyzed the river network in the Chubu region, which encompasses nine prefectures in Central Japan, to understand rainfall-water-level relationships. A spatial web-based system was then developed to monitor rainfall and river-water levels in the study area. This database system assesses near-real-time rainfall and water-level data. A map service was configured using OpenLayers, an open-source software package. This proposed system will deliver early-warning flood messages via a scripting mailer system. However, device error could occur, which necessitates human verification before a final decision is made. Self-geolocation helps individual users determine their risk. Improvements in the visualization and recognition of existing data using map-service tools and technology may help reduce tragedies and damage resulting from flooding.Index Terms-Database information systems, map services, web-based systems, warning systems, environmental disaster, floods.

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“…The technology required to integrate spatial data exists, permitting initiatives such as the Brazilian Geowebsite Spatial Data Infrastructure [67], although there is a lack of public policies in disaster management to support data sharing in this area. The National Spatial Data Infrastructure was created by the Brazilian government in 2008 to establish an integrated set of technologies, policies, mechanisms and procedures to coordinate and monitor data sharing to facilitate the dissemination and use of public-produced geospatial data.…”

Section: The Importance Of Web-based Data-sharing Systems To Facilitamentioning

confidence: 99%

An Analysis of Geospatial Technologies for Risk and Natural Disaster Management

Manfré

Hirata

Silva

et al. 2012

IJGI

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This paper discusses the use of spatial data for risk and natural disaster management. The importance of remote-sensing (RS), Geographic Information System (GIS) and Global Navigation Satellite System (GNSS) data is stressed by comparing studies of the use of these technologies for natural disaster management. Spatial data sharing is discussed in the context of the establishment of Spatial Data Infrastructures (SDIs) for natural disasters. Some examples of SDI application in disaster management are analyzed, and the need for participation from organizations and governments to facilitate the exchange of information and to improve preventive and emergency plans is reinforced. Additionally, the potential involvement of citizens in the risk and disaster management process by providing voluntary data collected from volunteered geographic information (VGI) applications is explored. A model relating all of the spatial data-sharing aspects discussed in the article was suggested to elucidate the importance of the issues raised.

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An object-relational prototype of GIS-based disaster database

Cited by 14 publications

References 70 publications

Hair-Oriented Data Model for Spatio-Temporal Data Mining

Hair-Oriented Data Model for Spatio-Temporal Data Mining

An Information-Systems Approach to Identifying Flood Risks in Central Japan

An Analysis of Geospatial Technologies for Risk and Natural Disaster Management

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