Defining Big Data

ABSTRACT As Big Data becomes better understood, there is a need for a comprehensive definition of Big Data to support work in fields such as data quality for Big Data. Existing definitions of Big Data define Big Data by comparison with existing, usually relational, definitions, or define Big Data in terms of data characteristics or use an approach which combines data characteristics with the Big Data environment. In this paper we examine existing definitions of Big Data and discuss the strengths and limitations of the different approaches, with particular reference to issues related to data quality in Big Data. We identify the issues presented by incomplete or inconsistent definitions. We propose an alternative definition and relate this definition to our work on quality in Big Dat

How can SMEs benefit from big data? Challenges and a path forward

Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft

Reference Models for Digital Manufacturing Platforms

[EN] This paper presents an integrated reference model for digital manufacturing platforms, based on cutting edge reference models for the Industrial Internet of Things (IIoT) systems. Digital manufacturing platforms use IIoT systems in combination with other added-value services to support manufacturing processes at different levels (e.g., design, engineering, operations planning, and execution). Digital manufacturing platforms form complex multi-sided ecosystems, involving different stakeholders ranging from supply chain collaborators to Information Technology (IT) providers. This research analyses prominent reference models for IIoT systems to align the definitions they contain and determine to what extent they are complementary and applicable to digital manufacturing platforms. Based on this analysis, the Industrial Internet Integrated Reference Model (I3RM) for digital manufacturing platforms is presented, together with general recommendations that can be applied to the architectural definition of any digital manufacturing platform.This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 825631 and from the Operational Program of the European Regional Development Fund (ERDF) of the Valencian Community 2014-2020 IDIFEDER/2018/025.Fraile Gil, F.; Sanchis, R.; Poler, R.; Ortiz Bas, Á. (2019). Reference Models for Digital Manufacturing Platforms. Applied Sciences. 9(20):1-25. https://doi.org/10.3390/app9204433S125920Pedone, G., & Mezgár, I. (2018). Model similarity evidence and interoperability affinity in cloud-ready Industry 4.0 technologies. Computers in Industry, 100, 278-286. doi:10.1016/j.compind.2018.05.003Mehrpouya, M., Dehghanghadikolaei, A., Fotovvati, B., Vosooghnia, A., Emamian, S. S., & Gisario, A. (2019). The Potential of Additive Manufacturing in the Smart Factory Industrial 4.0: A Review. Applied Sciences, 9(18), 3865. doi:10.3390/app9183865Tran, Park, Nguyen, & Hoang. (2019). Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context. Applied Sciences, 9(16), 3325. doi:10.3390/app9163325Fernandez-Carames, T. M., & Fraga-Lamas, P. (2019). A Review on the Application of Blockchain to the Next Generation of Cybersecure Industry 4.0 Smart Factories. IEEE Access, 7, 45201-45218. doi:10.1109/access.2019.2908780Moghaddam, M., Cadavid, M. N., Kenley, C. R., & Deshmukh, A. V. (2018). Reference architectures for smart manufacturing: A critical review. Journal of Manufacturing Systems, 49, 215-225. doi:10.1016/j.jmsy.2018.10.006Sutherland, W., & Jarrahi, M. H. (2018). The sharing economy and digital platforms: A review and research agenda. International Journal of Information Management, 43, 328-341. doi:10.1016/j.ijinfomgt.2018.07.004Corradi, A., Foschini, L., Giannelli, C., Lazzarini, R., Stefanelli, C., Tortonesi, M., & Virgilli, G. (2019). Smart Appliances and RAMI 4.0: Management and Servitization of Ice Cream Machines. IEEE Transactions on Industrial Informatics, 15(2), 1007-1016. doi:10.1109/tii.2018.2867643Gerrikagoitia, J. K., Unamuno, G., Urkia, E., & Serna, A. (2019). Digital Manufacturing Platforms in the Industry 4.0 from Private and Public Perspectives. Applied Sciences, 9(14), 2934. doi:10.3390/app9142934Digital Manufacturing Platforms, Factories 4.0 and beyondhttps://www.effra.eu/digital-manufacturing-platformsZero Defect Manufacturing Platform Project 2019https://www.zdmp.eu/Zezulka, F., Marcon, P., Vesely, I., & Sajdl, O. (2016). Industry 4.0 – An Introduction in the phenomenon. IFAC-PapersOnLine, 49(25), 8-12. doi:10.1016/j.ifacol.2016.12.002Announcing the IoT Industrie 4.0 Reference Architecturehttps://www.ibm.com/cloud/blog/announcements/iot-industrie-40-reference-architectureVelásquez, N., Estevez, E., & Pesado, P. (2018). Cloud Computing, Big Data and the Industry 4.0 Reference Architectures. Journal of Computer Science and Technology, 18(03), e29. doi:10.24215/16666038.18.e29Pisching, M. A., Pessoa, M. A. O., Junqueira, F., dos Santos Filho, D. J., & Miyagi, P. E. (2018). An architecture based on RAMI 4.0 to discover equipment to process operations required by products. Computers & Industrial Engineering, 125, 574-591. doi:10.1016/j.cie.2017.12.029Calvin, T. (1983). Quality Control Techniques for «Zero Defects». IEEE Transactions on Components, Hybrids, and Manufacturing Technology, 6(3), 323-328. doi:10.1109/tchmt.1983.113617

Bridging the demand and the offer in data science

During the last several years, we have observed an exponential increase in the demand for Data Scientists in the job market. As a result, a number of trainings, courses, books, and university educational programs (both at undergraduate, graduate and postgraduate levels) have been labeled as “Big data” or “Data Science”; the fil‐rouge of each of them is the aim at forming people with the right competencies and skills to satisfy the business sector needs. In this paper, we report on some of the exercises done in analyzing current Data Science education offer and matching with the needs of the job markets to propose a scalable matching service, ie, COmpetencies ClassificatiOn (E‐CO‐2), based on Data Science techniques. The E‐CO‐2 service can help to extract relevant information from Data Science–related documents (course descriptions, job Ads, blogs, or papers), which enable the comparison of the demand and offer in the field of Data Science Education and HR management, ultimately helping to establish the profession of Data Scientist.publishedVersio