Regression from Distributed Data Sources Using Discrete Neighborhood Representations and Modified Stalked Generalization Models

2015 ◽  
pp. 249-258
Author(s):  
Héctor Allende-Cid ◽  
Claudio Moraga ◽  
Héctor Allende ◽  
Raúl Monge
Keyword(s):  
Data Sources ◽  
Distributed Data

An Ontology-based approach to enable data-driven research in the field of NDT in Civil Engineering

2021 ◽  
Author(s):  
Benjamin Moreno-Torres ◽  
Christoph Völker ◽  
Sabine Kruschwitz
Keyword(s):  
Knowledge Representation ◽  
Civil Engineering ◽  
Common Knowledge ◽  
Measurement Data ◽  
Data Access ◽  
Semantic Knowledge ◽  
Data Sources ◽  
Data Sets ◽  
Extensive Literature ◽  
Distributed Data

<div> <p>Non-destructive testing (NDT) data in civil engineering is regularly used for scientific analysis. However, there is no uniform representation of the data yet. An analysis of distributed data sets across different test objects is therefore too difficult in most cases.</p> <p>To overcome this, we present an approach for an integrated data management of distributed data sets based on Semantic Web technologies. The cornerstone of this approach is an ontology, a semantic knowledge representation of our domain. This NDT-CE ontology is later populated with the data sources. Using the properties and the relationships between concepts that the ontology contains, we make these data sets meaningful also for machines. Furthermore, the ontology can be used as a central interface for database access. Non-domain data sources can be integrated by linking them with the NDT ontology, making them directly available for generic use in terms of digitization. Based on an extensive literature research, we outline the possibilities that result for NDT in civil engineering, such as computer-aided sorting and analysis of measurement data, and the recognition and explanation of correlations.</p> <p>A common knowledge representation and data access allows the scientific exploitation of existing data sources with data-based methods (such as image recognition, measurement uncertainty calculations, factor analysis or material characterization) and simplifies bidirectional knowledge and data transfer between engineers and NDT specialists.</p> </div>

Using Grid Technology for Maximizing Collaborative Emergency Response Decision Making

2009 ◽  
pp. 256-275 ◽  
Author(s):  
Eleana Asimakopoulou ◽  
Chimay J. Anumba ◽  
Bouchlaghem ◽  
Bouchlaghem
Keyword(s):  
Decision Making ◽  
Emergency Response ◽  
Data Grid ◽  
Informed Decision ◽  
Data Sources ◽  
Grid Technology ◽  
Distributed Data ◽  
Grid Environment ◽  
Design And Implementation ◽  
Response Decision

Much work is under way within the Grid technology community on issues associated with the development of services to foster collaboration via the integration and exploitation of multiple autonomous, distributed data sources through a seamless and flexible virtualized interface. However, several obstacles arise in the design and implementation of such services. A notable obstacle, namely how clients within a data Grid environment can be kept automatically informed of the latest and relevant changes about data entered/committed in single or multiple autonomous distributed datasets is identified. The view is that keeping interested users informed of relevant changes occurring across their domain of interest will enlarge their decision-making space which in turn will increase the opportunities for a more informed decision to be encountered. With this in mind, the chapter goes on to describe in detail the model architecture and its implementation to keep interested users informed automatically about relevant up-to-date data.

Integrating distributed data sources with OGSA–DAI DQP and V iews

2010 ◽  
Vol 368 (1926) ◽  
pp. 4133-4145 ◽  
Author(s):  
Bartosz Dobrzelecki ◽  
Amrey Krause ◽  
Alastair C. Hume ◽  
Alistair Grant ◽  
Mario Antonioletti ◽  
...  
Keyword(s):  
Data Integration ◽  
Query Processing ◽  
Data Access ◽  
Data Sources ◽  
Distributed Data ◽  
Grid Services ◽  
Distributed Query Processing ◽  
Processing Resource ◽  
Distributed Query ◽  
Open Grid Services Architecture

OGSA-DAI (Open Grid Services Architecture Data Access and Integration) is a framework for building distributed data access and integration systems. Until recently, it lacked the built-in functionality that would allow easy creation of federations of distributed data sources. The latest release of the OGSA-DAI framework introduced the OGSA-DAI DQP (Distributed Query Processing) resource. The new resource encapsulates a distributed query processor, that is able to orchestrate distributed data sources when answering declarative user queries. The query processor has many extensibility points, making it easy to customize. We have also introduced a new OGSA-DAI V iews resource that provides a flexible method for defining views over relational data. The interoperability of the two new resources, together with the flexibility of the OGSA-DAI framework, allows the building of highly customized data integration solutions.

Integrating Data Mining Models from Distributed Data Sources

2010 ◽  
pp. 389-396 ◽  
Author(s):  
Ingrid Wilford-Rivera ◽  
Daniel Ruiz-Fernández ◽  
Alejandro Rosete-Suárez ◽  
Oscar Marín-Alonso
Keyword(s):  
Data Mining ◽  
Data Sources ◽  
Distributed Data

Integrating Heterogeneous Data Sources in the Web

2009 ◽  
pp. 2472-2488
Author(s):  
Angelo Brayner ◽  
Marcelo Meirelles ◽  
José de Aguiar Moraes Filho
Keyword(s):  
Query Language ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Distributed Data ◽  
Local Data ◽  
Multidatabase System ◽  
Integration Strategy ◽  
Heterogeneous Data Sources ◽  
Integration Problems ◽  
The Web

Integrating data sources published on the Web requires an integration strategy that guarantees the local data sources’ autonomy. A multidatabase system (MDBS) has been consolidated as an approach to integrate multiple heterogeneous and distributed data sources in flexible and dynamic environments such as the Web. A key property of MDBSs is to guarantee a higher degree of local autonomy. In order to adopt the MDBS strategy, it is necessary to use a query language, called the MultiDatabase Language (MDL), which provides the necessary constructs for jointly manipulating and accessing data in heterogeneous data sources. In other words, the MDL is responsible for solving integration conflicts. This chapter describes an extension to the XQuery Language, called MXQuery, which supports queries over several data sources and solves such integration problems as semantic heterogeneity and incomplete information.

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