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2760 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: B2249078219/19©BEIESP DOI: 10.35940/ijrte.B2249.078219 Abstract: The rapid increase of data generated has brought challenges on data quality level. Fog computing in general has been supporting the requirements of end user devices that could not be met by cloud computing solution and it is acknowledged to have a major impact on how an organisation decides to adopt for preprocessing a huge amount of data being generated by the devices. Since IoT devices generating very heterogeneous and dynamic data, there are challenges for the level of data quality. The limitation has hindered the development of fog systems framework that capable operating the dynamic execution of edge devices that handling generation and collection large amounts of data on-premise and off-premise. Thus,sufficient operations of identifying Quality of Result enable user to detect any problems when conducting the decision making. The aim of this paper is to address the factors that perceived likely to influence the adoption of fog computing in evaluating the data analysis on data transmitted from the ever increases devices.A conceptual framework has been constructed considering attributes such as heterogeneous data analysis (on-premise and off-premise) and Quality of Results (quality indicators, quality control, validity outcome and reliability outcome).Potential benefits from the implementation of this framework to organisation is it enable to provide greater value and benefits to the business process. The framework of this study could also be influencing and inhibiting the adoption of fog computing.Quality of result has higher chances to satisfy the defined industrial’s requirement. In addition, fog-computing adoption is important for serving an environment for industry to execute, monitor, and analyze a large form of data in a fog landscape.

Factors Influencing Fog Computing Adoption Based on Quality of Results (QoR) for Heterogeneous Data Analysis: A Proposed Framework