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Designing Secure Data Warehouses
2006Organizations 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
2005Fayyad 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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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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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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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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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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From Traditional Data Warehouse To Real Time Data Warehouse
2017The 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.
Faiez Gargouri +2 more
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A General Model for Data Warehouses
2009Basically, the schema of a data warehouse lies on two kinds of elements: facts and dimensions. Facts are used to memorize measures about situations or events. Dimensions are used to analyse these measures, particularly through aggregation operations (counting, summation, average, etc.).
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Data Analytics and Data Warehouses
2011This chapter focuses on how to extract information from package software systems. This information will be used for decision support system (DSS) purposes and typically be presented to the user in an on-line display format or as a report. The concept behind DSS is that it deals with data “after the fact,” meaning that the data are no longer in a ...
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Streaming Data into the Warehouse
2020In the last chapter, we covered myriad ways to take your data and load it into your BigQuery data warehouse. Another significant way of getting your data into BigQuery is to stream it. In this chapter, we will cover the pros and cons of streaming data, when you might want to use it, and how to do it.
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International Journal of Computer Integrated Manufacturing, 2021
Qiang Liu, Berna Senturk, Hao Zhang
exaly
Qiang Liu, Berna Senturk, Hao Zhang
exaly