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A Data-Agnostic Approach to Automatic Testing of Multi-dimensional Databases

Authors: Marius Alexandru Marin;

A Data-Agnostic Approach to Automatic Testing of Multi-dimensional Databases

Abstract

This paper presents a data-agnostic testing technique for multi-dimensional databases (OLAP cubes), with a fault-model, a test-coverage-model, a test automation technique and a technique for tests generation. The test generation is mostly suitable for systems with little or no transformation between the data loaded from the source, relational system and the data loaded into the target, analytical one, while the faults and coverage model discussions are broadly applicable. We present the main design considerations for the proposed testing approach and describe the implementation of the framework for test automation and of the test generation tool. The gains in tests maintainability, reliability and failure analysis effort are assessed in comparison with a record-and-replay technique using as show-case the test suites for the OLAP cubes delivered out-of-the-box with the Business Intelligence modules of the Microsoft Dynamics AX® Enterprise Resource Planning system.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Average
Average
Average
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