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Integration of Heterogeneous Spatial Databases for Disaster Management

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TLDR
This paper aims to propose a framework for the integration of heterogeneous spatial databases using novel approaches, such as web services and ontologies, in order to be able to interrogate the content of the different databases conveniently.
Abstract
The response phase in a disaster case is often considered to be the most critical in terms of saving lives and dealing with irreversible damage. The timely provision of geospatial information is crucial in the decision-making process. Thus, there is a need for the integration of heterogeneous spatial databases which are inherently distributed and created under different projects by various organizations. The integration of all relevant data for timely decision making is a challenging task due to syntactic, schematic and semantic heterogeneity. This paper aims to propose a framework for the integration of heterogeneous spatial databases using novel approaches, such as web services and ontologies. We focus on providing solutions for the three levels of heterogeneity, in order to be able to interrogate the content of the different databases conveniently. Based on the proposed framework, we implemented a use case using heterogeneous data belonging to La Rochelle city in France.

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Interpretation and automatic integration of geospatial data into the Semantic Web

TL;DR: This article presents an automatic approach for geospatial data integration in NSDS that does a schema mapping according to the result of an ontology matching corresponding to a semantic interpretation process, based on geocoding and natural language processing.
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Interpreting Heterogeneous Geospatial Data Using Semantic Web Technologies

TL;DR: The paper illustrates the work that firstly follows existing Semantic Web standards when dealing with vector geodata and secondly extends current standards when deals with raster geodATA and more advanced geospatial operations.
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A Classification Model for the Identification of Prominent Microblogs Users during a Disaster

TL;DR: This work proposes a classification model for the identification of prominent users who are sharing relevant and exclusive information during the disaster by modeled using three kinds of time-sensitive features, including topical, social and geographical features.
References
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Book

Building the data warehouse

TL;DR: This Second Edition of Building the Data Warehouse is revised and expanded to include new techniques and applications of data warehouse technology and update existing topics to reflect the latest thinking.

N-gram-based text categorization

TL;DR: An N-gram-based approach to text categorization that is tolerant of textual errors is described, which worked very well for language classification and worked reasonably well for classifying articles from a number of different computer-oriented newsgroups according to subject.
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The data warehouse toolkit: practical techniques for building dimensional data warehouses

Ralph Kimball
TL;DR: This definitive guide succinctly explains how to build a data warehouse by using actual case studies of existing data warehouses developed for specific types of business applications such as retail, manufacturing, banking, insurance, subcriptions and airline reservations.
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A federated architecture for information management

TL;DR: An approach to the coordinated sharing and interchange of computerized information is described emphasizing partial, controlled sharing among autonomous databases.
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Overcoming the semantic and other barriers to GIS interoperability

TL;DR: A concise explanation of the meaning of interoperability in the context of GIS is given and problems and challenges of the next interoperable GIS generation are identified.
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