Developing use cases for big data analytics: Data integration with social media metrics

Abstract

The magnitude of data generated and shared by various stakeholders has been exacerbated due in part to social media networks and platforms. These datasets emanate from disparate sources, with varied formats, such as unstructured, semi-structured and structured, and in a variety of types, including multimedia, textual contents, etc. Today, various entities (i.e. humans, computer systems, devices, firms, etc.) generate these Big Data, which are in huge volumes, and requiring non-traditional database management technologies for storage and processing. Some concerns exist with the handling, integration and processing of the datasets, as they come from diverse sources. The essence of data integration is such that an event that occurs in one social media metric would automatically trigger an event in one or more separate metrics. The approach adapted was site scraping to collect datasets, and system design principles applied to conceptualize a solution. Key contribution is the social media metrics computational and virtual data integration model aimed at assisting stakeholders as a guide in gathering, processing, analyzing and utilizing insights for competitive advantage. Typical outcomes are innovative trends, patterns and even, outliers. The outliers may be hidden novelty that ought to be explored further. SMEs in developing economies are likely to find this study useful. Further research may experiment with actual social media metrics. � 2019 River Publishers.