Data Science and Knowledge Analytic Contexts on IoE Data for E-BI Application Case

Data Science and Knowledge Analytic Contexts on IoE Data for E-BI Application Case

Nilamadhab Mishra (Debre Berhan University, Ethiopia)
DOI: 10.4018/978-1-5225-8555-8.ch007


The progressive data science and knowledge analytic tasks are gaining popularity across various intellectual applications. The main research challenge is to obtain insight from large-scale IoE data that can be used to produce cognitive actuations for the applications. The time to insight is very slow, quality of insight is poor, and cost of insight is high; on the other hand, the intellectual applications require low cost, high quality, and real-time frameworks and algorithms to massively transform their data into cognitive values. In this chapter, the author would like to discuss the overall data science and knowledge analytic contexts on IoE data that are generated from smart edge computing devices. In an IoE-driven e-BI application, the e-consumers are using the smart edge computing devices from which a huge volume of IoE data are generated, and this creates research challenges to traditional data science and knowledge analytic mechanisms. The consumer-end IoE data are considered the potential sources to massively turn into the e-business goldmines.
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In the contemporary and forthcoming days, the convergence of data science, knowledge analytics, Internet of Everything (IoE), big-data, statistical learning, and computational machine learning mechanisms are gaining popularities across various Intellectual e-BI applications. The core intention is to extract the cognitive values from large scale network-centric data that can be potentially used to produce intelligence for the applications. The IoE has a most important influence on the Big Data background. The key awareness on IoE data science evolution is that every IoE object has an IP address and connects to each other. Now, bearing in mind the circumstances of trillions of such connections that may be producing massive volumes of data (IoE big data), and the competence of current data science and knowledge analytics mechanisms are going to be challenged. The IoE evolutionary network connects people, processes, places, and things to internet for communication in and around the universe. The IoE objects focus both physical and logical things. The logical things include process, framework, applications, software, and program, and the physical things include people, places, physical entities, and devices. The data of such physical and logical things constitute a comprehensive IoE data base, where the structured, semi-structured, and unstructured data are available (Mishra et al, 2014). In an IoE data base, ERP and CRM data are considered as structured data, XML data are normally considered as semi-structured data, and email documents, social web contents, pdf, ward, rich text documents are considered as un-structured data. The study reveals that in an IoE data base, around 80% data are unstructured with no pre-defined data models. Such un-structured data are textual, graphics, video, and symbols oriented. The spatial-temporal databases having the facts or events with time-stamps are also a part of IoE database. The rapid increasing of IoE big data applications in today’s IoE world progressively lead to several problem issues such as, data volume, velocity, varieties, and value. Analyzing and inferencing cognitive values (knowledge) from large scale IoE data base in a real-time basis is more challenging day by day with the extreme growing of volume, and varieties data that are associated with numerous IoE applications. Such IoE knowledge analytics and inference face a number of real-time problems such as, managing heterogeneous knowledge, transforming varieties data into knowledge, transforming knowledge into actions, transforming actions into cognitive decisions, and tuning the cognitive decisions to coordinate the IoE motivated applications (Mishra et al, 2014). The convergences of statistical and computational learning mechanisms have been researched to deal with the data science and knowledge analytic problems. Data science and knowledge analytic implements are also used for analyzing and exploring various operational tasks associated with the IoE big data submissions, such as-data transformation and analysis, data mining, knowledge discovery, semantic knowledge explorations, structural analysis, and many more. The machine learning technics are implemented in many areas of knowledge discovery and semantic knowledge analytics to explore the application intelligence. In almost all IoE big data applications, a huge amount of data is dumped into the storage that are highly redundant and unsuitable for the purpose of data analysis, modelling, information transformation, knowledge production, and the decision generation. A survey conducted by Par Stream shows that 94% of the organizations surveyed are facing challenges in IoE big data elicitations and analytics, and 70% organizations think that, the IoE big data analytics help to make better and more meaningful decisions for organizations (Mishra et al, 2014).

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