Data warehouse testing

ElGamal, Neveen; El-Bastawissy, Ali; Galal-Edeen, Galal H.

doi:10.1145/2457317.2457319

Cited by 6 publications

(11 citation statements)

References 3 publications

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“…Automated testing tools were also used for testing a data warehouse by [19], and it was concluded that DbFit, TSQLUnit and utPLSQL are the tools which can be used but for application domains like reporting, OLAP and information presentation more testing tools can be examined. Data warehouse testing methodologies have been proposed by many researchers as existing in the literature which can be seen in [21,22]. Based on these methodologies, data warehouse testing techniques have been explained by many authors and have been compiled in [15] and it has been concluded that for front-end data warehouse testing, the best techniques which can be used are performance, regression, security and report testing.…”

Section: Data Warehouse Testing Techniquesmentioning

confidence: 99%

Data Warehouse Testing: A Step Towards using Business Intelligence in Banking

Mathur*,

Gupta,

Pahwa

2019

IJRTE

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In the modern era, technology plays a vital role by contributing in the development of business by providing various tools and techniques to enhance the business decision-making process. Data warehouse is an important entity that contributes to the decision-making process, which can be seen in the literature available over the years. Data warehouse provides the basis for quality analysis of available data by deriving accurate information from data. Like many other industries, banking sector is also facing challenges due to various reasons like large over-dues, non-performing assets, changing customer demographics, matching customer expectation levels, increased competition from financial technological companies and banking competitors, etc. Thus data warehouse system serves to be the best solution for the banks to overcome challenges as data warehouse system integrates all the data at one place and provides a consolidated view of the past transactions which can be used for report generation and performing analytical analysis in order to help the management to maximize business performance. In addition to making strategic decisions, data warehouse also assist in helping the banks to improve customer retention, optimize discounting, market segmentation, business performance, customer deposits, etc. Thus, a data warehouse system provides a solution to all data management problem and generate patterns and reports for analytical end users for enhancing decision making processes. In this research paper, we shall be representing a bank model that focuses on the loan department of a bank data warehouse and shall be explaining how business intelligence plays a role in improving the loan analysis for the banks. Loan analysis may include summarizing the classified loans, analyzing loans within and exceeding the threshold limits of Loan-To-Value (LTV) ratio, analysis of loan rejections and other analysis pertaining to loans.

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Section: Data Warehouse Testing Techniquesmentioning

confidence: 99%

Data Warehouse Testing: A Step Towards using Business Intelligence in Banking

Mathur*,

Gupta,

Pahwa

2019

IJRTE

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“…Algorithms for loading routines for the various types of dimensions can be found in [14]. Test Cases categories for ETL routines are pointed in [6]. This material, together with the extensive experience of the authors in DW projects in the public and private sectors, provided the basis for the elaboration of categories and test cases to be considered by the framework.…”

Section: A) Independent Variablesmentioning

confidence: 99%

“…In [6] it is presented a directed models approach for automatic generation and execution of test cases based in formal models of systems. The formal model adopted is based on the UML language.…”

Section: Threats To External Validitymentioning

confidence: 99%

“…The motivation for adopting this approach meets the problems pointed out in [19], as the main causes for the poor quality of data in a DW environment. Another motivation, also pointed in [6,7,8] is the need to adopt different strategies, considering the differences between traditional environments and DW environments, which can contribute to the adoption of testing processes.…”

Section: Threats To External Validitymentioning

confidence: 99%

“…The low adoption of testing activities in DW environment is credited to the differences between the architecture of this environment and architectures of the generic software systems. These differences mean that the testing techniques used by the latter need to be adjusted for a DW environment [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Experimentation in the Industry for Automation of Unit Testing in a Business Intelligence Environment

Santos¹,

Nascimento²,

Costa³

et al. 2016

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-This paper presents an approach to automate the selection and execution of previously indentified test cases for loading procedures in Business Intelligence (BI) environments based on Data Warehouse (DW). To verify and validate the approach, a unit test framework was developed. The overall goal is achieve data quality improvement. The specific aim is reduce test effort and, consequently, promote test activities in data warehousing process. A controlled experiment evaluation was carried out to investigate the adequacy of the proposed method for data warehouse procedures development. The results of the experiment show that our approach clearly reduces test effort when compared with manual execution of test cases.

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