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In recent day’s social media, smartphones, mobile apps, and the internet of things generate huge amounts of data every second. This data is structured, unstructured, or semi-structured and available in various formats. Therefore, traditional approaches are not sufficient to handle such kind of data effectively. On the other hand, high utility based frequent patterns are also essential to make effective decisions, and its demand is also increasing in recent years. Many strategies have been proposed for high utility based pattern generation. But these methods are limited to data size and operate on standalone systems. To address this issue, we have proposed a parallel procedure for high utility based frequent pattern mining. The proposed approach can handle huge amounts of data, i.e., big data. It can be used in various real-life applications and benefited citizens. The proposed technique is implemented in …