摘要 :
Data Warehouses are a fundamental component of today's Business Intelligence infrastructure. They allow to consolidate heterogeneous data from distributed data stores and transform it into strategic indicators for decision making....
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Data Warehouses are a fundamental component of today's Business Intelligence infrastructure. They allow to consolidate heterogeneous data from distributed data stores and transform it into strategic indicators for decision making. In this tutorial we give an overview of current state of the art and point out to next challenges in the area. In particular, this includes to cope with more complex data, both in structure and semantics, and keeping up with the demands of new application domains such as Web, financial, manufacturing, genomic, biological, life science, multimedia, spatial, and spatiotemporal applications. We review consolidated resaerch in spatio-temporal databases, and open research fields, like real-time Business Intelligence and Semantic Web Data Warehousing and OLAP.
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摘要 :
Data Warehouses are a fundamental component of today's Business Intelligence infrastructure. They allow to consolidate heterogeneous data from distributed data stores and transform it into strategic indicators for decision making....
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Data Warehouses are a fundamental component of today's Business Intelligence infrastructure. They allow to consolidate heterogeneous data from distributed data stores and transform it into strategic indicators for decision making. In this tutorial we give an overview of current state of the art and point out to next challenges in the area. In particular, this includes to cope with more complex data, both in structure and semantics, and keeping up with the demands of new application domains such as Web, financial, manufacturing, genomic, biological, life science, multimedia, spatial, and spatiotemporal applications. We review consolidated resaerch in spatio-temporal databases, and open research fields, like real-time Business Intelligence and Semantic Web Data Warehousing and OLAP.
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摘要 :
This paper motivates a comprehensive methodological framework for dealing with some aspects of real-world complexity in information system analysis and design. By complex application problem, we mean a problem that cannot be solve...
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This paper motivates a comprehensive methodological framework for dealing with some aspects of real-world complexity in information system analysis and design. By complex application problem, we mean a problem that cannot be solved by the current technology in the way that it is perceived and analyzed by application domain specialists. The paper focuses on a motivating case study, the analysis of constraint violations in database management at the Belgian agency for social security. We then re-interpret practices and their problems in terms of current information system technology. Recommendations are derived both for suitable developments of the technology, that would allow a better treatment of complex real-world problems, and for methodological improvements in data management practices in the application domain, that would take better advantage of the current technology.
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The Semantic Web (SW) has drawn the attention of data enthusiasts, and also inspired the exploitation and design of multidimensional data warehouses, in an unconventional way. Traditional data ware-houses (DW) operate over static ...
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The Semantic Web (SW) has drawn the attention of data enthusiasts, and also inspired the exploitation and design of multidimensional data warehouses, in an unconventional way. Traditional data ware-houses (DW) operate over static data. However multidimensional (MD) data modeling approach can be dynamically extended by defining both the schema and instances of MD data as RDF graphs. The importance and applicability of MD data warehouses over RDF is widely studied yet none of the works support a spatially enhanced MD model on the SW. Spatial support in DWs is a desirable feature for enhanced analysis, since adding encoded spatial information of the data allows to query with spatial functions. In this paper we propose to empower the spatial dimension of data warehouses by adding spatial data types and topological relationships to the existing QB4OLAP vocabulary, which already supports the representation of the constructs of the MD models in RDF. With QB4SOLAP, spatial constructs of the MD models can be also published in RDF, which allows to implement spatial and metric analysis on spatial members along with OLAP operations. In our contribution, we describe a set of spatial OLAP (SOLAP) operations, demonstrate a spatially extended metamodel as, QB4SOLAP, and apply it on a use case scenario. Finally, we show how these SOLAP queries can be expressed in SPARQL.
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摘要 :
The Semantic Web (SW) has drawn the attention of data enthusiasts, and also inspired the exploitation and design of multidimensional data warehouses, in an unconventional way. Traditional data warehouses (DW) operate over static d...
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The Semantic Web (SW) has drawn the attention of data enthusiasts, and also inspired the exploitation and design of multidimensional data warehouses, in an unconventional way. Traditional data warehouses (DW) operate over static data. However multidimensional (MD) data modeling approach can be dynamically extended by defining both the schema and instances of MD data as RDF graphs. The importance and applicability of MD data warehouses over RDF is widely studied yet none of the works support a spatially enhanced MD model on the SW. Spatial support in DWs is a desirable feature for enhanced analysis, since adding encoded spatial information of the data allows to query with spatial functions. In this paper we propose to empower the spatial dimension of data warehouses by adding spatial data types and topolog-ical relationships to the existing QB4OLAP vocabulary, which already supports the representation of the constructs of the MD models in RDF. With QB4SOLAP, spatial constructs of the MD models can be also published in RDF, which allows to implement spatial and metric analysis on spatial members along with OLAP operations. In our contribution, we describe a set of spatial OLAP (SOLAP) operations, demonstrate a spatially extended metamodel as, QB4SOLAP, and apply it on a use case scenario. Finally, we show how these SOLAP queries can be expressed in SPARQL.
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摘要 :
During recent years, more and more data has been published as native RDF datasets. In this setup, both the size of the datasets and the need to process aggregate queries represent challenges for standard SPARQL query processing te...
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During recent years, more and more data has been published as native RDF datasets. In this setup, both the size of the datasets and the need to process aggregate queries represent challenges for standard SPARQL query processing techniques. To overcome these limitations, materialized views can be created and used as a source of precomputed partial results during query processing. However, materialized view techniques as proposed for relational databases do not support RDF specifics, such as incompleteness and the need to support implicit (derived) information. To overcome these challenges, this paper proposes MARVEL (MAterialized Rdf Views with Entailment and incompLetness). The approach consists of a view selection algorithm based on an associated RDF-specific cost model, a view definition syntax, and an algorithm for rewriting SPARQL queries using materialized RDF views. The experimental evaluation shows that MARVEL can improve query response time by more than an order of magnitude while effectively handling RDF specifics.
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摘要 :
During recent years, more and more data has been published as native RDF datasets. In this setup, both the size of the datasets and the need to process aggregate queries represent challenges for standard SPARQL query processing te...
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During recent years, more and more data has been published as native RDF datasets. In this setup, both the size of the datasets and the need to process aggregate queries represent challenges for standard SPARQL query processing techniques. To overcome these limitations, materialized views can be created and used as a source of precomputed partial results during query processing. However, materialized view techniques as proposed for relational databases do not support RDF specifics, such as incompleteness and the need to support implicit (derived) information. To overcome these challenges, this paper proposes MARVEL (MAterialized Rdf Views with Entailment and incompLetness). The approach consists of a view selection algorithm based on an associated RDF-specific cost model, a view definition syntax, and an algorithm for rewriting SPARQL queries using materialized RDF views. The experimental evaluation shows that MARVEL can improve query response time by more than an order of magnitude while effectively handling RDF specifics.
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摘要 :
During recent years, more and more data has been published as native RDF datasets. In this setup, both the size of the datasets and the need to process aggregate queries represent challenges for standard SPARQL query processing te...
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During recent years, more and more data has been published as native RDF datasets. In this setup, both the size of the datasets and the need to process aggregate queries represent challenges for standard SPARQL query processing techniques. To overcome these limitations, materialized views can be created and used as a source of precomputed partial results during query processing. However, materialized view techniques as proposed for relational databases do not support RDF specifics, such as incompleteness and the need to support implicit (derived) information. To overcome these challenges, this paper proposes MARVEL (MAterialized Rdf Views with Entailment and incompLetness). The approach consists of a view selection algorithm based on an associated RDF-specific cost model, a view definition syntax, and an algorithm for rewriting SPARQL queries using materialized RDF views. The experimental evaluation shows that MARVEL can improve query response time by more than an order of magnitude while effectively handling RDF specifics.
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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...
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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...
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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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