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Unlocking Value from Ubiquitous Data

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Information and Communication Technologies in Education, Research, and Industrial Applications (ICTERI 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1007))

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Abstract

Data is growing at an alarming rate. This growth is spurred by varied array of sources, such as embedded sensors, social media sites, video cameras, the quantified-self and the internet-of-things. This is changing our reliance on data for making decisions, or data analytics, from being mostly carried out by an individual and in limited settings to taking place while on-the-move and in the field of action. Unlocking value from data directs that it must be assessed from multiple dimensions. Data’s value can be primarily classified as “information,” “knowledge” or “wisdom”. Data analytics addresses such matters as what and why, as well as what will and what should be done. In recent days, data analytics is moving from being reserved for domain experts to becoming necessary for the end-user. However, data availability is both a pertinent issue and a great opportunity for global businesses. This paper presents recent examples from work in our research team on ubiquitous data analytics and open up to a discussion on key questions relating methodologies, tools and frameworks to improve ubiquitous data team effectiveness as well as the potential goals for a ubiquitous data process methodology. Finally, we give an outlook on the future of data analytics, suggesting a few research topics, applications, opportunities and challenges. This paper is based on a keynote speech to the 14th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer, Kyiv, Ukraine on 16 May 2018.

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Notes

  1. 1.

    McKinsey&Business: Retrieved from https://www.mckinsey.com/business-functions/operations/our-insights/ops-4-0-fueling-the-next-20-percent-productivity-rise-with-digital-analytics.

  2. 2.

    McKinsey&Business: Retrieved from https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/analytics-comes-of-age.

  3. 3.

    LeMO: Retrieved from https://lemo-h2020.eu/.

  4. 4.

    BDEM: Retrieved from https://bdem.squarespace.com/.

  5. 5.

    All information can be accessed via http://www.mksmart.org/ and were last accessed on September 3, 2018.

  6. 6.

    https://www.vestforsk.no/en/project/ubiquitous-data-driven-urban-mobility.

  7. 7.

    http://setamobility.weebly.com/.

  8. 8.

    https://transformingtransport.eu/.

  9. 9.

    https://data.transformingtransport.eu/ was accessed on September 3, 2018.

  10. 10.

    http://noesis-project.eu/.

  11. 11.

    https://lemo-h2020.eu/overview/.

  12. 12.

    https://bdem.squarespace.com/.

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Acknowledgements

This work is partially supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 770038, UBIMOB project (270785) funded by the Norwegian Research Council in 2017 through the IKTPLUSS programme, and BDEM project funded by the Research Council of Norway (RCN) and the Norwegian Centre for International Cooperation in Education (SiU) through the INTPART programme. Authors contributed equally to this work.

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Authors and Affiliations

  1. Western Norway Research Institute, P.O. Box 163, 6851, Sogndal, Norway

    Rajendra Akerkar & Minsung Hong

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  1. Rajendra Akerkar
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  2. Minsung Hong
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Corresponding author

Correspondence to Rajendra Akerkar .

Editor information

Editors and Affiliations

  1. Zaporizhzhya National University, Zaporizhzhia, Ukraine

    Vadim Ermolayev

  2. Universidad Politecnica de Madrid, Boadilla del Monte, Madrid, Spain

    Mari Carmen Suárez-Figueroa

  3. Lviv Polytechnic National University, Lviv, Ukraine

    Vitaliy Yakovyna

  4. Institute of Applied Informatics, Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria

    Heinrich C. Mayr

  5. Department of Theory and Technology of Programming, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine

    Mykola Nikitchenko

  6. Kherson State University, Kherson, Ukraine

    Aleksander Spivakovsky

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Akerkar, R., Hong, M. (2019). Unlocking Value from Ubiquitous Data. In: Ermolayev, V., Suárez-Figueroa, M., Yakovyna, V., Mayr, H., Nikitchenko, M., Spivakovsky, A. (eds) Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2018. Communications in Computer and Information Science, vol 1007. Springer, Cham. https://doi.org/10.1007/978-3-030-13929-2_1

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