Results 221 to 230 of about 35,332 (261)

Fundamentals of Data Warehouses

2000
This book presents the first comparative review of the state-of-the-art and the best current practices of data warehouses. It covers source and data integration, multidimensional aggregation, query optimization, metadata management, quality assessment, and design optimization. A conceptual framework is presented by which the architecture and quality of
Jarke Matthias, LENZERINI, Maurizio, Vassiliou Yannis, Vassiliadis Panos   +3 more
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Feeding data warehouses

Proceedings 1999 International Symposium on Database Applications in Non-Traditional Environments (DANTE'99) (Cat. No.PR00496), 2003
Data warehouses have become very popular with academics, industry, and users. The idea of a global repository for strategic information is seen as a sesame door to a world of magic, where spontaneously generated information breakthroughs turn ordinary business into a multimillionaire production activity.
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From Traditional Data Warehouse To Real Time Data Warehouse

2017
The Traditional data warehouse did not contain data as today. Hence, it is difficult to retrieve these data and treat them. Furthermore, its content is not updated, which may lead to bad decisions. Data are typically loaded from conventional operational systems.
Senda Bouaziz, Ahlem Nabli, Faïez Gargouri   +2 more
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Querying Multiversion Data Warehouses

2015
Data warehouses (DWs) change in their content and structure due to changes in the feeding sources, business requirements, the modeled reality, and legislation, to name a few. Keeping the history of changes in the content and structure of a DW enables the user to analyze the state of the business world retrospectively or prospectively. Multiversion data
Waqas, Ahmed, Zimanyi, Esteban
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Minimizing detail data in data warehouses

1998
Data warehouses collect and maintain large amounts of data from several distributed and heterogeneous data sources. Because of security reasons, operational requirements, and technical feasibility it is often impossible for data warehouses to access the data sources directly.
Akinde, M., Jensen, Ole Guttorm, Böhlen, M.   +2 more
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Data Warehouses: Next Challenges

2012
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.
Vaisman, Alejandro Ariel, Zimanyi, Esteban   +1 more
openaire   +2 more sources

Deductive Data Warehouses

2019
This chapter presents the concept of “deductive data warehouses.” Deductive data warehouses rely on deductive databases but use a data warehouse in the background instead of a database. The authors show how Datalog, as a logic programming language, can be used to perform on-line analytical processing (OLAP) analysis on data.
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Knowledge discovery in data warehouses

ACM SIGMOD Record, 2000
As the size of data warehouses increase to several hundreds of gigabytes or terabytes, the need for methods and tools that will automate the process of knowledge extraction, or guide the user to subsets of the dataset that are of particular interest, is becoming prominent.
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Research on design and optimization of green warehouse system based on case analysis

Journal of Cleaner Production, 2023
Yaoyao Ku, Peng Wu
exaly  

Genome Warehouse: A Public Repository Housing Genome-Scale Data

Genomics, Proteomics and Bioinformatics, 2021
Meili Chen, Yingke Ma, Song Wu
exaly