Data Warehouse Maintenance, Evolution and Versioning

2010 ◽  
pp. 171-188 ◽  
Author(s):  
Johann Eder ◽  
Karl Wiggisser
Keyword(s):  
Decision Support ◽  
Data Warehouse ◽  
Building Blocks ◽  
Data Warehouses ◽  
Master Data ◽  
Analytical Processing ◽  
The Common ◽  
Instance Analysis ◽  
Transactional Data ◽  
Common Understanding

Data Warehouses typically are building blocks of decision support systems in companies and public administration. The data contained in a data warehouse is analyzed by means of OnLine Analytical Processing tools, which provide sophisticated features for aggregating and comparing data. Decision support applications depend on the reliability and accuracy of the contained data. Typically, a data warehouse does not only comprise the current snapshot data but also historical data to enable, for instance, analysis over several years. And, as we live in a changing world, one criterion for the reliability and accuracy of the results of such long period queries is their comparability. Whereas data warehouse systems are well prepared for changes in the transactional data, they are, surprisingly, not able to deal with changes in the master data. Nonetheless, such changes do frequently occur. The crucial point for supporting changes is, first of all, being aware of their existence. Second, once you know that a change took place, it is important to know which change (i.e., knowing about differences between versions and relations between the elements of different versions). For data warehouses this means that changes are identified and represented, validity of data and structures are recorded and this knowledge is used for computing correct results for OLAP queries. This chapter is intended to motivate the need for powerful maintenance mechanisms for data warehouse cubes. It presents some basic terms and definitions for the common understanding and introduces the different aspects of data warehouse maintenance. Furthermore, several approaches addressing the problem are presented and classified by their capabilities.

Data Warehouse Maintenance, Evolution and Versioning

2011 ◽  
pp. 566-583
Author(s):  
Johann Eder ◽  
Karl Wiggisser
Keyword(s):  
Decision Support ◽  
Data Warehouse ◽  
Building Blocks ◽  
Data Warehouses ◽  
Master Data ◽  
Analytical Processing ◽  
The Common ◽  
Instance Analysis ◽  
Transactional Data ◽  
Common Understanding

Data Warehouses typically are building blocks of decision support systems in companies and public administration. The data contained in a data warehouse is analyzed by means of OnLine Analytical Processing tools, which provide sophisticated features for aggregating and comparing data. Decision support applications depend on the reliability and accuracy of the contained data. Typically, a data warehouse does not only comprise the current snapshot data but also historical data to enable, for instance, analysis over several years. And, as we live in a changing world, one criterion for the reliability and accuracy of the results of such long period queries is their comparability. Whereas data warehouse systems are well prepared for changes in the transactional data, they are, surprisingly, not able to deal with changes in the master data. Nonetheless, such changes do frequently occur. The crucial point for supporting changes is, first of all, being aware of their existence. Second, once you know that a change took place, it is important to know which change (i.e., knowing about differences between versions and relations between the elements of different versions). For data warehouses this means that changes are identified and represented, validity of data and structures are recorded and this knowledge is used for computing correct results for OLAP queries. This chapter is intended to motivate the need for powerful maintenance mechanisms for data warehouse cubes. It presents some basic terms and definitions for the common understanding and introduces the different aspects of data warehouse maintenance. Furthermore, several approaches addressing the problem are presented and classified by their capabilities.

scholarly journals Data Warehouse Benchmarking with DWEB

2011 ◽  
pp. 302-323
Author(s):  
Jérôme Darmont
Keyword(s):  
Performance Evaluation ◽  
Decision Support ◽  
Data Warehouse ◽  
Database Management ◽  
Ad Hoc ◽  
Synthetic Data ◽  
Transaction Processing ◽  
Data Warehouses ◽  
On Line ◽  
Analytical Processing

Performance evaluation is a key issue for designers and users of Database Management Systems (DBMSs). Performance is generally assessed with software benchmarks that help, for example test architectural choices, compare different technologies, or tune a system. In the particular context of data warehousing and On-Line Analytical Processing (OLAP), although the Transaction Processing Performance Council (TPC) aims at issuing standard decision-support benchmarks, few benchmarks do actually exist. We present in this chapter the Data Warehouse Engineering Benchmark (DWEB), which allows generating various ad-hoc synthetic data warehouses and workloads. DWEB is fully parameterized to fulfill various data warehouse design needs. However, two levels of parameterization keep it relatively easy to tune. We also expand on our previous work on DWEB by presenting its new Extract, Transform, and Load (ETL) feature, as well as its new execution protocol. A Java implementation of DWEB is freely available online, which can be interfaced with most existing relational DMBSs. To the best of our knowledge, DWEB is the only easily available, up-to-date benchmark for data warehouses.

scholarly journals Data Analytical Processing in Data Warehouses

2010 ◽  
Vol 2 (1) ◽  
pp. 99-116
Author(s):  
Katarzyna Rostek
Keyword(s):  
Data Processing ◽  
Data Warehouse ◽  
Analytical Data ◽  
Data Warehouses ◽  
Practical Guidelines ◽  
Processing Information ◽  
Scope Of Application ◽  
Analytical Processing ◽  
Effective Use

Data Analytical Processing in Data Warehouses The article presents issues connected with processing information from data warehouses (the analytical enterprise databases) and two basic types of analytical data processing in data warehouse. The genesis, main definitions, scope of application and real examples from business implementations will be described for each type of analysis. There will be presented copyrighted method of knowledge discovering in databases, together with practical guidelines for its proper and effective use in the enterprise.

Using OCL to Model Constraints in Data Warehouses

2013 ◽  
pp. 212-224
Author(s):  
François Pinet ◽  
Myoung-Ah Kang ◽  
Kamal Boulil ◽  
Sandro Bimonte ◽  
Gil De Sousa ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Data Warehouse ◽  
Support Systems ◽  
General Information ◽  
Data Warehouses ◽  
Object Constraint Language ◽  
Software Applications ◽  
Constraint Language ◽  
Transactional Databases

Recent research works propose using Object-Oriented (OO) approaches, such as UML to model data warehouses. This paper overviews these recent OO techniques, describing the facts and different analysis dimensions of the data. The authors propose a tutorial of the Object Constraint Language (OCL) and show how this language can be used to specify constraints in OO-based models of data warehouses. Previously, OCL has been only applied to describe constraints in software applications and transactional databases. As such, the authors demonstrate in this paper how to use OCL to represent the different types of data warehouse constraints. This paper helps researchers working in the fields of business intelligence and decision support systems, who wish to learn about the major possibilities that OCL offer in the context of data warehouses. The authors also provide general information about the possible types of implementation of multi-dimensional models and their constraints.

scholarly journals Harvesting Information from a Library Data Warehouse

2017 ◽  
Vol 19 (1) ◽  
pp. 17-28 ◽  
Author(s):  
Siew-Phek T. Su ◽  
Ashwin Needamangala
Keyword(s):  
Decision Support ◽  
Knowledge Discovery ◽  
Data Warehouse ◽  
Data Warehousing ◽  
End Users ◽  
Cutting Edge ◽  
Data Warehouses ◽  
Student Data ◽  
Integrated Platform ◽  
Academic Sector

Data warehousing technology has been defined by John Ladley as "a set of methods, techniques, and tools that are leveraged together and used to produce a vehicle that delivers data to end users on an integrated platform." (1) This concept h s been applied increasingly by industries worldwide to develop data warehouses for decision support and knowledge discovery. In the academic sector, several universities have developed data warehouses containing the universities' financial, payroll, personnel, budget, and student data. (2) These data warehouses across all industries and academia have met with varying degrees of success. Data warehousing technology and its related issues have been widely discussed and published. (3) Little has been done, however, on the application of this cutting edge technology in the library environment using library data.

Data Warehousing

2008 ◽  
pp. 2749-2761
Author(s):  
Hugh J. Watson ◽  
Barbara H. Wixom ◽  
Dale L. Goodhue
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Data Warehouse ◽  
Data Warehousing ◽  
Top Management ◽  
Data Warehouses ◽  
Web Based ◽  
Good Data ◽  
Customer Services ◽  
Enterprise Data Warehouse

Data warehouses are helping resolve a major problem that has plagued decision support applications over the years — a lack of good data. Top management at 3M realized that the company had to move from being product-centric to being customer savvy. In response, 3M built a terabyte data warehouse (global enterprise data warehouse) that provides thousands of 3M employees with real-time access to accurate, global, detailed information. The data warehouse underlies new Web-based customer services that are dynamically generated based on warehouse information. There are useful lessons that were learned at 3M during their years of developing the data warehouse.

An Experimental Replication With Data Warehouse Metrics

2008 ◽  
pp. 408-428
Author(s):  
Manuel Serrano ◽  
Coral Calero ◽  
Mario Piattini
Keyword(s):  
Decision Support ◽  
Data Warehouse ◽  
Relational Databases ◽  
Software Metrics ◽  
Empirical Work ◽  
Data Warehouses ◽  
Present Part ◽  
Data Warehouse Quality ◽  
Bad Data ◽  
Integrate Data

Data warehouses are large repositories that integrate data from several sources for analysis and decision support. Data warehouse quality is crucial, because a bad data warehouse design may lead to the rejection of the decision support system or may result in non-productive decisions. In the last years, we have been working on the definition and validation of software metrics in order to assure data warehouse quality. Some of the metrics are adapted directly from previous ones defined for relational databases, and others are specific for data warehouses. In this paper, we present part of the empirical work we have developed in order to know if the proposed metrics can be used as indicators of data warehouse quality. Previously, we have developed an experiment and its replication, and in this paper, we present the second replication we have made with the purpose of assessing data warehouse maintainability. As a result of the whole empirical work, we have obtained a subset of the proposed metrics that seem to be good indicators of data warehouse quality.

Storage Strategies in Data Warehouses

2011 ◽  
pp. 1054-1058
Author(s):  
Xinjian Lu
Keyword(s):  
Data Warehouse ◽  
Data Aggregation ◽  
Historical Data ◽  
Response Times ◽  
Data Warehouses ◽  
Individual Values ◽  
Processing Data ◽  
On Line ◽  
Analytical Processing ◽  
Require Data

A data warehouse stores and manages historical data for on-line analytical processing, rather than for on-line transactional processing. Data warehouses with sizes ranging from gigabytes to terabytes are common, and they are much larger than operational databases. Data warehouse users tend to be more interested in identifying business trends rather than individual values. Queries for identifying business trends are called analytical queries. These queries invariably require data aggregation, usually according to many different groupings. Analytical queries are thus much more complex than transactional ones. The complexity of analytical queries combined with the immense size of data can easily result in unacceptably long response times. Effective approaches to improving query performance are crucial to a proper physical design of data warehouses.

Data Warehouse Development

2005 ◽  
pp. 729-733
Author(s):  
José María Cavero Barca ◽  
Esperanza Marcos Martinez ◽  
Mario G. Piattini ◽  
Adolfo Sánchez de Miguel
Keyword(s):  
Decision Support ◽  
Data Warehouse ◽  
Processing Technology ◽  
Online Analytical Processing ◽  
Data Synthesis ◽  
Analytical Processing

The concept of data warehouse first appeared in Inmon (1993) to describe a “subject oriented, integrated, non-volatile, and time variant collection of data in support of management’s decisions” (31). It is a concept related to the OLAP (online analytical processing) technology, first introduced by Codd et al. (1993) to characterize the requirements of aggregation, consolidation, view production, formulae application, and data synthesis in many dimensions. A data warehouse is a repository of information that mainly comes from online transactional processing (OLTP) systems that provide data for analytical processing and decision support.

scholarly journals Innovative Approaches for Efficiently Warehousing Complex Data from the Web

Data Mining ◽  
2013 ◽  
pp. 1422-1448
Author(s):  
Fadila Bentayeb ◽  
Nora Maïz ◽  
Hadj Mahboubi ◽  
Cécile Favre ◽  
Sabine Loudcher ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Data Warehouse ◽  
Design Management ◽  
Complex Data ◽  
Data Warehouses ◽  
Process Data ◽  
Access Methods ◽  
Olap Analysis ◽  
The Web

Research in data warehousing and OLAP has produced important technologies for the design, management, and use of Information Systems for decision support. With the development of Internet, the availability of various types of data has increased. Thus, users require applications to help them obtaining knowledge from the Web. One possible solution to facilitate this task is to extract information from the Web, transform and load it to a Web Warehouse, which provides uniform access methods for automatic processing of the data. In this chapter, we present three innovative researches recently introduced to extend the capabilities of decision support systems, namely (1) the use of XML as a logical and physical model for complex data warehouses, (2) associating data mining to OLAP to allow elaborated analysis tasks for complex data and (3) schema evolution in complex data warehouses for personalized analyses. Our contributions cover the main phases of the data warehouse design process: data integration and modeling, and user driven-OLAP analysis.

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