{'fraction_non_alphanumeric': 0.019965277777777776, 'fraction_numerical': 0.0, 'mean_word_length': 5.068421052631579, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Data Science and Knowledge Analytic Contexts on IoE Data for E-BI Application Case

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.