publication . Article . 2021

Big data augmentated business trend identification: the case of mobile commerce

Ozcan Saritas; Pavel Bakhtin; Ilya Kuzminov; Elena Khabirova;
Open Access English
  • Published: 05 Jan 2021 Journal: Scientometrics (issn: 0138-9130, eissn: 1588-2861, Copyright policy)
  • Publisher: Springer Science and Business Media LLC
Abstract
Identifying and monitoring business and technological trends are crucial for innovation and competitiveness of businesses. Exponential growth of data across the world is invaluable for identifying emerging and evolving trends. On the other hand, the vast amount of data leads to information overload and can no longer be adequately processed without the use of automated methods of extraction, processing, and generation of knowledge. There is a growing need for information systems that would monitor and analyse data from heterogeneous and unstructured sources in order to enable timely and evidence-based decision-making. Recent advancements in computing and big data...
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free text keywords: General Social Sciences, Library and Information Sciences, Computer Science Applications, Article, M-commerce, COVID-19, Natural language processing, Machine learning, Horizon scanning, Tech mining, Global trends, Information overload, Business domain, Big data, business.industry, business, Data mart, Information system, Mobile commerce, Data science, Business intelligence, Computer science, Coronavirus disease 2019 (COVID-19)
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COVID-19
Digital Humanities and Cultural Heritage
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