Results 181 to 190 of about 9,065 (221)
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Database and Expert Systems Applications. 8th International Conference, DEXA '97. Proceedings, 2002
A data warehouse collects and integrates data from multiple, autonomous, heterogeneous sources with the purpose of efficiently implementing decision support or OLAP queries. Much working data warehousing has been performed on view materialization and data integration, we focus on access and security management in OLAP and N-dimensional cube. Since data
Remzi Kirkgöze +3 more
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A data warehouse collects and integrates data from multiple, autonomous, heterogeneous sources with the purpose of efficiently implementing decision support or OLAP queries. Much working data warehousing has been performed on view materialization and data integration, we focus on access and security management in OLAP and N-dimensional cube. Since data
Remzi Kirkgöze +3 more
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Proceedings of the 2nd ACM international workshop on Data warehousing and OLAP, 1999
OLAP systems support data analysis through a multidimensional data model, according to which data facts are viewed as points in a space of application-related “dimensions” , organized into levels which conform a hierarchy. Although the usual assumption is that these points reflect the dynamic aspect of the data warehouse while dimensions are relatively
Carlos A. Hurtado +2 more
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OLAP systems support data analysis through a multidimensional data model, according to which data facts are viewed as points in a space of application-related “dimensions” , organized into levels which conform a hierarchy. Although the usual assumption is that these points reflect the dynamic aspect of the data warehouse while dimensions are relatively
Carlos A. Hurtado +2 more
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2019
The expansion of IoT devices and monitoring needs, powered by the capabilities and accessibility of Cloud Computing, has led to an explosion of streaming data and exposed the need for every organization to exploit it. This paper reviews the evolution of Data Stream Management Systems (DSMS) and the convergence into Online Analytical Processing (OLAP ...
Carlos Garcia-Alvarado +3 more
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The expansion of IoT devices and monitoring needs, powered by the capabilities and accessibility of Cloud Computing, has led to an explosion of streaming data and exposed the need for every organization to exploit it. This paper reviews the evolution of Data Stream Management Systems (DSMS) and the convergence into Online Analytical Processing (OLAP ...
Carlos Garcia-Alvarado +3 more
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Proceedings 13th International Conference on Data Engineering, 2002
On-line analytical processing (OLAP) is a recent and important application of database systems. Typically, OLAP data is presented as a multidimensional "data cube." OLAP queries are complex and can take many hours or even days to run, if executed directly on the raw data.
Himanshu Gupta 0001 +3 more
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On-line analytical processing (OLAP) is a recent and important application of database systems. Typically, OLAP data is presented as a multidimensional "data cube." OLAP queries are complex and can take many hours or even days to run, if executed directly on the raw data.
Himanshu Gupta 0001 +3 more
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OLAP and bibliographic databases
Scientometrics, 2003The application of online analytical processing (OLAP) technology to bibliographic databases is addressed. We show that OLAP tools can be used by librarians for periodic and ad hoc reporting, quality assurance, and data integrity checking, as well as by research policy makers for monitoring the development of science and evaluating or comparing ...
Emil Hudomalj, Gaj Vidmar
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Detecting summarizability in OLAP
Data & Knowledge Engineering, 2014The industry trend towards self-service business intelligence is impeded by the absence, in commercially-available information systems, of automated identification of potential issues with summarization operations. Research on statistical databases and on data warehouses have both produced widely-accepted categorisations of measure attributes, the ...
Tapio Niemi +3 more
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Encyclopedia with Semantic Computing and Robotic Intelligence, 2017
The objective of this tutorial is to present an overview of machine learning (ML) methods. This paper outlines different types of ML as well as techniques for each kind. It covers popular applications for different types of ML. On-Line Analytic Processing (OLAP) enables users of multidimensional databases to create online comparative summaries of data.
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The objective of this tutorial is to present an overview of machine learning (ML) methods. This paper outlines different types of ML as well as techniques for each kind. It covers popular applications for different types of ML. On-Line Analytic Processing (OLAP) enables users of multidimensional databases to create online comparative summaries of data.
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Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems - PODS '02, 2002
In multidimensional data models intended for online analytic processing (OLAP), data are viewed as points in a multidimensional space. Each dimension has structure, described by a directed graph of categories, a set of members for each category, and a child/parent relation between members.
Carlos A. Hurtado, Alberto O. Mendelzon
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In multidimensional data models intended for online analytic processing (OLAP), data are viewed as points in a multidimensional space. Each dimension has structure, described by a directed graph of categories, a set of members for each category, and a child/parent relation between members.
Carlos A. Hurtado, Alberto O. Mendelzon
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Multivariate and multidimensional OLAP
1998The author presents a new relational approach to multivariate and multidimensional OLAP. In this approach, a multivariate aggregate view (MAV) is defined. MAV contains categorized univariate and multivariate aggregated data, which can be used to support many more advanced statistical methods not currently supported by any OLAP models.
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Proceedings of the ACM tenth international workshop on Data warehousing and OLAP, 2007
Expressing preferences when querying databases is a natural way to avoid empty results and information flooding, and in general to rank results so that the user may first see the data that better match his tastes. In this paper we outline the main research issues to be faced in order to develop a system for handling user preferences on OLAP cubes.
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Expressing preferences when querying databases is a natural way to avoid empty results and information flooding, and in general to rank results so that the user may first see the data that better match his tastes. In this paper we outline the main research issues to be faced in order to develop a system for handling user preferences on OLAP cubes.
openaire +3 more sources