Is Big Data a Paradigm Challange to Information Science
Is Big Data a Paradigm Challange to Information Science

Summary/Abstract: Purpose: The purpose of the paper is twofold: (i) to argue that fundamental ideas are the most important tokens in scientific and engineering endeavours rather than specific methods, procedures, metrics and artefacts; new ideas and concepts are transformative forces of how we understand science, its role in society and how we lead scientific research; and (ii) to identify the challenges and opportunities the emerging concept of big data brings about to information science. Approach/Methods: In order to investigate the impact of the new ideas in science we follow Thomas Kuhn’s approach presented in his landmark book The Structure of Scientific Revolutions according to which science develops by leaps, which he dubbed paradigm leaps, that are qualitative changes of the ways the world is conceptualised and understood. Results and conclusions: As a result of our investigation we identified four major paradigm leaps. The paper shortly depicts three paradigms that are already a canonical part of the past and contemporary science, and then a budding fourth paradigm that is still in statu nascendi, in its nascent stage,is described. We begin with Plato and Aristotle (first paradigm), and then through Francis Bacon(second paradigm), John von Neumann (third paradigm) we shall arrive at big data and knowledge discovery by means of computer facilities (potentially a fourth paradigm). Originality/Value: It is believed that the fourth paradigm can help information technology become a partner on a par with humans in scientific and other research endeavours going far beyond its present role of being mainly a mechanism to store, process, and disseminate information. It is argued also that the fourth paradigm is a challenge to information science in both its main dimensions: (i) development of its foundations and methodologies by studying information phenomena reflected in very large datasets, and (ii) providing users with the needed information and knowledge derived from very large datasets.

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