Providing Clarity on Big Data Technologies: The BDTOnto Ontology

Providing Clarity on Big Data Technologies: The BDTOnto Ontology

Matthias Volk (Otto-von-Guericke University Magdeburg, Germany), Daniel Staegemann (Otto-von-Guericke University Magdeburg, Germany), Naoum Jamous (Otto-von-Guericke University Magdeburg, Germany), Matthias Pohl (Otto-von-Guericke University Magdeburg, Germany) and Klaus Turowski (Otto-von-Guericke University Magdeburg, Germany)
Copyright: © 2020 |Pages: 25
DOI: 10.4018/IJIIT.2020040103

Abstract

Big Data is a term that gained popularity due to its potential benefits in various fields, and is progressively being used. However, there are still many gaps and challenges to overcome, especially when it comes to the selection and handling of relevant technologies. A consequence of the huge number of manifestations in this area, growing each year, the uncertainty and complexity increase. The lack of a classification approach causes a growing demand for more experts with a broad knowledge and expertise. Using various techniques of ontology engineering and following the design science methodology, this work proposes the Big Data Technology Ontology (BDTOnto) as a comprehensive and sustainable classification approach to classify big data technologies and their manifestations. In particular, a reusable, extensible and adaptable artifact in the form of an ontology will be developed and evaluated.
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Introduction

In recent years the umbrella term of big data got increasingly popular, affecting both, our daily and business lives, as it is publicized by numerous studies and statistics such as (McLellan, 2017; Popovič, Hackney, Tassabehji, & Castelli, 2018; Reinsel, Gantz, & Rydning, 2017). Provided with ever-increasing growth rates, this topic will become even more indispensable in the future, making is a high priority subject for scientists and practitioners all over the world. By 2020, it is expected that businesses’ investments in big data-related projects will reach around $210 trillion (Turck, 2017). Organizations are aware of the far-reaching potentials (Günther, Rezazade Mehrizi, Huysman, & Feldberg, 2017) and the economic benefits have already been evidenced (Müller, Fay, & Vom Brocke, 2018). Furthermore, the application areas are manifold, comprising domains like health care (Wang, Kung, Wang, & Cegielski, 2014), logistics (Nguyen, Zhou, Spiegler, Ieromonachou, & Lin, 2018), tourism (Gajdošík, 2019), civil protection (Wu & Cui, 2018) and entertainment (Marr, 2016), rendering big data a huge factor in today’s everyday lives. Despite the constantly risen popularity and the intensive research on its definition as well as its application, several gaps are still to be bridged. Although these are critical success factors, it remains unclear which big data technologies exist, at which time a use appears suitable, and which concrete manifestation should be applied (Volk, Jamous, & Turowski, 2017). Currently, more than 1335 companies produce and/or distribute big data relevant products (Turck, 2019). Hence, it is not surprising that the demand for big data, data science, and data analytics experts is steadily increasing (Piatetsky, 2019). Small and medium enterprises (SME) are confronted with the complexity of the introduction and realization of big data projects (Mittal, 2017). Similar to the situation of big data, there is no unique definition for SMEs. Due to this reason, this work follows the definition of the European Commission, which categorizes enterprises with a number of employees of less than 250 and a yearly turnover with a maximum of 50 million Euros as SMEs (European Commission, 2019). In any case, compared to larger companies they are facing shortages in terms of employees and available budget for new investments. As such, huge monetary expenditures are commonly required, if infrastructural changes or specific resources are needed, like the right skill set provided by experts. While looking at the sheer endless number of new technologies, techniques, and methods, it is therefore not surprising that SMEs do not play the role of early adopters when it comes to the application of big data technologies (McKinsey Analytics, 2018). Resulting out of this situation, those enterprises are forced to perform well-considered decisions; otherwise, they might be burdened with a long-time commitment without having any major benefits from this. According to current research, there is nothing such as a comprehensive big data technology classification which provides an overview or may assist in making the right choice (Volk, Bosse, & Turowski, 2017). Thus, the realization of this kind of project needs a thorough examination by practitioners and decision-makers in advance. To address the previously described problem and enhancing the project success, the following research question arises:

How could a sustainable big data classification be created in order to provide both, an overview of current and future technologies as well as to achieve decision support during the realization of data-intensive application scenarios?

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