Generating Executable Mappings from RDF Data Cube Data Structure Definitions

2018 ◽  
pp. 333-350 ◽  
Author(s):  
Christophe Debruyne ◽  
Dave Lewis ◽  
Declan O’Sullivan
Keyword(s):  
Data Structure ◽  
Data Cube ◽  
Rdf Data

scholarly journals Adding value to Linked Open Data using a multidimensional model approach based on the RDF Data Cube vocabulary

2020 ◽  
Vol 68 ◽  
pp. 103378 ◽  
Author(s):  
Pilar Escobar ◽  
Gustavo Candela ◽  
Juan Trujillo ◽  
Manuel Marco-Such ◽  
Jesús Peral
Keyword(s):  
Open Data ◽  
Data Cube ◽  
Linked Open Data ◽  
Multidimensional Model ◽  
Rdf Data ◽  
Model Approach

scholarly journals INTEGRACuBe: Exploração de dados analíticos em RDF

2021 ◽  
Author(s):  
Jones O. Avelino ◽  
Kelli F. Cordeiro ◽  
Maria C. Cavalcanti
Keyword(s):  
Data Cube ◽  
Rdf Data

O crescimento de conjuntos de dados disponíveis na Web que utilizam o padrão RDF propicia análises de dados que envolvem múltiplas dimensões. Segundo a W3C, um dos recursos para analisar dados multidimensionais é a utilização do vocabulário RDF Data Cube. Contudo ainda há uma carência de instrumentos de apoio para aplicação deste vocabulário em conjuntos de dados. Nesse sentido, este artigo propõe o INTEGRACuBe, um ambiente que utiliza um metaesquema e mecanismos semiautomatizados para apoiar o mapeamento de recursos de dados ao metamodelo RDF Data Cube. Como resultado, será possível a exploração de dados analíticos em RDF. Adicionalmente, um estudo de caso é apresentado no cenário de Gerência de Desenvolvimento de Software.

scholarly journals Multidimensional enrichment of spatial RDF data for SOLAP

Semantic Web ◽  
2021 ◽  
pp. 1-35
Author(s):  
Nurefşan Gür ◽  
Torben Bach Pedersen ◽  
Katja Hose ◽  
Mikael Midtgaard
Keyword(s):  
Semantic Web ◽  
Spatial Data ◽  
Spatial Information ◽  
Complex Geometry ◽  
Open Data ◽  
Data Cube ◽  
Multidimensional Data ◽  
Wide Range ◽  
Advanced Analysis ◽  
Rdf Data

Large volumes of spatial data and multidimensional data are being published on the Semantic Web, which has led to new opportunities for advanced analysis, such as Spatial Online Analytical Processing (SOLAP). The RDF Data Cube (QB) and QB4OLAP vocabularies have been widely used for annotating and publishing statistical and multidimensional RDF data. Although such statistical data sets might have spatial information, such as coordinates, the lack of spatial semantics and spatial multidimensional concepts in QB4OLAP and QB prevents users from employing SOLAP queries over spatial data using SPARQL. The QB4SOLAP vocabulary, on the other hand, fully supports annotating spatial and multidimensional data on the Semantic Web and enables users to query endpoints with SOLAP operators in SPARQL. To bridge the gap between QB/QB4OLAP and QB4SOLAP, we propose an RDF2SOLAP enrichment model that automatically annotates spatial multidimensional concepts with QB4SOLAP and in doing so enables SOLAP on existing QB and QB4OLAP data on the Semantic Web. Furthermore, we present and evaluate a wide range of enrichment algorithms and apply them on a non-trivial real-world use case involving governmental open data with complex geometry types.

Computation of OLAP Data Cubes

2011 ◽  
pp. 286-292
Author(s):  
Amin A. Abdulghani
Keyword(s):  
Data Structure ◽  
Data Cube ◽  
Primary Interest ◽  
Total Cost ◽  
Sales Data ◽  
Data Cubes ◽  
Multiple Dimensions ◽  
Analytical Processing ◽  
Product Location ◽  
Total Sales

The focus of online analytical processing (OLAP) is to provide a platform for analyzing data (e.g., sales data) with multiple dimensions (e.g., product, location, time) and multiple measures (e.g., total sales or total cost). OLAP operations then allow viewing of this data from a number of perspectives. For analysis, the object or data structure of primary interest in OLAP is a data cube. A detailed introduction to OLAP is presented in (Han & Kambler, 2006).

Distributed Modeling Of Building Systems Through Cyber-Physical Integration

2019 ◽  
pp. 12-17
Keyword(s):  
Data Structure ◽  
Operating Conditions ◽  
Physical Models ◽  
Distributed Model ◽  
Physical Processes ◽  
Distributed Modeling ◽  
Distributed Models ◽  
Operating Modes ◽  
Building Systems ◽  
Important Practical Application

This article describes the proposed approaches to creating distributed models that can, with given accuracy under given restrictions, replace classical physical models for construction objects. The ability to implement the proposed approaches is a consequence of the cyber-physical integration of building systems. The principles of forming the data structure of designed objects and distributed models, which make it possible to uniquely identify the elements and increase the level of detail of such a model, are presented. The data structure diagram of distributed modeling includes, among other things, the level of formation and transmission of signals about physical processes inside cyber-physical building systems. An enlarged algorithm for creating the structure of the distributed model which describes the process of developing a data structure, formalizing requirements for the parameters of a design object and its operating modes (including normal operating conditions and extreme conditions, including natural disasters) and selecting objects for a complete group that provides distributed modeling is presented. The article formulates the main approaches to the implementation of an important practical application of the cyber-physical integration of building systems - the possibility of forming distributed physical models of designed construction objects and the directions of further research are outlined.

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