A cloud based business intelligence framework for a cellular Internet of Things network

Abstract

In this research, a Business Intelligence (BI) framework for a cellu-lar Internet of Things (IoT) environment is researched, designed, im-plemented and evaluated. The BI framework provides a structure that supports development of a BI platform (solution) by first defining a structured platform to provide data, and then following a process flow to ensure valid Artificial Intelligence (AI) models are created. Systems Engineering (SE) principles were applied to define the BI framework, with theoretically grounded Data Mining methods included in the process flow. This system under evaluation is a cellular IoT network of edge devices linked to the cloud via secure, managed data chan-nels. By applying the BI framework, a BI platform is designed and implemented to extract insights from the management data provided by the system. In addition, by following the BI framework’s process flow model, AI models are fitted to the available data and included in the BI platform as a total solution. From the BI platform, insights extracted from data are converted into key performance indicators, or used in models to predict or clas-sify anomalies that indicate operational failures (risk). These models include time series anomaly detection, clustering and classification models. The research was conducted in a Design Science Research paradigm, with Action Design Research as the method with which to conduct the action research. Quality Research Management was used to pro-vide traceability and to ensure the defined goals were achieved in a systematic manner. Research challenges were identified from obser-vations and a literature survey, researched in literature focus areas, systematically addressed by means of synthesis from literature and creative input, and implemented as a means of validation. The final BI platform solution was applied to real-world data and successfully addressed the initial research challenge.