Abstract
These days a huge amount of data is generated, which is effectively tackled by various big data applications. However, it takes a lot of time to process these vast volumes of data. Hence, while developing big data applications, we need to build effective as well as smaller datasets. In this chapter we present an improved algorithm of pair-wise testing for this purpose. This improved algorithm of pairwise testing can be effectively used in big data applications. Pairwise testing is expensive and time consuming. In order to deal with the problems that are present in pairwise testing and exploiting the abundance of data that is presents, artificial intelligence (AI) and machine learning can be used to make the process of testing easier. These tests are conducted by using the ability of AI to create more advanced tests: the algorithm finds the pattern in the data and creates a comprehensive data model of observed parameters.
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