Results 51 to 60 of about 219,327 (94)
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Spatial Data Warehouses

2008
It is estimated that about 80% of the data stored in databases has a spatial or location component. Therefore, the location dimension has been widely used in data warehouse and OLAP systems. However, this dimension is usually represented in an alphanumeric, nonspatial manner (i.e., using solely the place name) since these systems are not able to ...
Esteban Zimányi, Elzbieta Malinowski
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Data Warehouse Concepts

2014
This chapter introduces the basic concepts of data warehouses. A data warehouse is a particular database targeted toward decision support. It takes data from various operational databases and other data sources and transforms it into new structures that fit better for the task of performing business analysis.
Alejandro Vaisman, Esteban Zimányi
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The Data Warehouse

2017
The fewer data needed, the better the information. And an overload of information…leads to information blackout.
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Data Warehouse Patterns

2012
SQL Server Integration Services is an excellent all-purpose ETL tool. Because of its versatility, it is used by DBAs, developers, BI professionals, and even business principals in many different scenarios. Sometimes it’s a dump truck, used for the wholesale movement of enormous amounts of data.
Andy Leonard   +4 more
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Data Warehouses

2005
Data warehouses (DW) appeared first in industry in the mid 1980s. When their impact on businesses and database practices became clear, a flurry or research took place in academia in the late 1980s and 1990s. However, the concept of DW still remains rooted on its practical origins.
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Designing Secure Data Warehouses

2006
Organizations depend increasingly on information systems, which rely upon databases and data warehouses (DWs), which need increasingly more quality and security. Generally, we have to deal with sensitive information such as the diagnosis made on a patient or even personal beliefs or other sensitive data.
Rodolfo Villarroel   +3 more
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Data Quality in Data Warehouses

2005
Fayyad and Uthursamy (2002) have stated that the majority of the work (representing months or years) in creating a data warehouse is in cleaning up duplicates and resolving other anomalies. This article provides an overview of two methods for improving quality. The first is data cleaning for finding duplicates within files or across files.
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Schema Evolution in Data Warehouses

Knowledge and Information Systems, 2002
In this paper, we address the issues related to the evolution and maintenance of data warehousing systems, when underlying data sources change their schema capabilities. These changes can invalidate views at the data warehousing system. We present an approach for dynamically adapting views according to schema changes arising on source relations.
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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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Designing a Data Warehouse

2012
Designing a data warehouse is one of the most important aspects of a business intelligence solution. If the data warehouse is designed correctly, all other aspects of the solution will benefit. Conversely, if it is created incorrectly, it will cause no end of problems.
Randal Root, Caryn Mason
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