摘要 :
Business Intelligence (BI) tools provide fundamental support for analyzing large volumes of information. Data Warehouses (DW) and Online Analytical Processing (OLAP) tools are used to store and analyze data. Nowadays more and more...
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Business Intelligence (BI) tools provide fundamental support for analyzing large volumes of information. Data Warehouses (DW) and Online Analytical Processing (OLAP) tools are used to store and analyze data. Nowadays more and more information is available on the Web in the form of Resource Description Framework (RDF), and BI tools have a huge potential of achieving better results by integrating real-time data from web sources into the analysis process. In this paper, we describe a framework for so-called exploratory OLAP over RDF sources. We propose a system that uses a multidimensional schema of the OLAP cube expressed in RDF vocabularies. Based on this information the system is able to query data sources, extract and aggregate data, and build a cube. We also propose a computer-aided process for discovering previously unknown data sources and building a multidimensional schema of the cube. We present a use case to demonstrate the applicability of the approach.
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摘要 :
Business Intelligence (BI) tools provide fundamental support for analyzing large volumes of information. Data Warehouses (DW) and Online Analytical Processing (OLAP) tools are used to store and analyze data. Nowadays more and more...
展开
Business Intelligence (BI) tools provide fundamental support for analyzing large volumes of information. Data Warehouses (DW) and Online Analytical Processing (OLAP) tools are used to store and analyze data. Nowadays more and more information is available on the Web in the form of Resource Description Framework (RDF), and BI tools have a huge potential of achieving better results by integrating real-time data from web sources into the analysis process. In this paper, we describe a framework for so-called exploratory OLAP over RDF sources. We propose a system that uses a multidimensional schema of the OLAP cube expressed in RDF vocabularies. Based on this information the system is able to query data sources, extract and aggregate data, and build a cube. We also propose a computer-aided process for discovering previously unknown data sources and building a multidimensional schema of the cube. We present a use case to demonstrate the applicability of the approach.
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摘要 :
The web is changing the way in which data warehouses are designed, used, and queried. With the advent of initiatives such as Open Data and Open Government, organizations want to share their multidimensional data cubes and make the...
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The web is changing the way in which data warehouses are designed, used, and queried. With the advent of initiatives such as Open Data and Open Government, organizations want to share their multidimensional data cubes and make them available to be queried online. The RDF data cube vocabulary (QB), the W3C standard to publish statistical data in RDF, presents several limitations to fully support the multidimensional model. The QB4OLAP vocabulary extends QB to overcome these limitations, and provides the distinctive feature of being able to implement several OLAP operations, such as rollup, slice, and dice using standard SPARQL queries. In this paper we present QB4OLAP Engine, a tool that transforms multidimensional data stored in relational DWs into RDF using QB4OLAP, and apply the solution to a real-world case, based on the national survey of housing, health services, and income, carried out by the government of Uruguay.
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摘要 :
The web is changing the way in which data warehouses are designed, used, and queried. With the advent of initiatives such as Open Data and Open Government, organizations want to share their multidimensional data cubes and make the...
展开
The web is changing the way in which data warehouses are designed, used, and queried. With the advent of initiatives such as Open Data and Open Government, organizations want to share their multidimensional data cubes and make them available to be queried online. The RDF data cube vocabulary (QB), the W3C standard to publish statistical data in RDF, presents several limitations to fully support the multidimensional model. The QB4OLAP vocabulary extends QB to overcome these limitations, and provides the distinctive feature of being able to implement several OLAP operations, such as rollup, slice, and dice using standard SPARQL queries. In this paper we present QB4OLAP Engine, a tool that transforms multidimensional data stored in relational DWs into RDF using QB4OLAP, and apply the solution to a real-world case, based on the national survey of housing, health services, and income, carried out by the government of Uruguay.
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