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Chapter 11 Advanced Technology for Event Management

DOI: 10.23912/9781911635734-4774

ISBN: 9781911635734

Published: February 2021

Component type: chapter

Published in: Event Project Management

Parent DOI: 10.23912/9781911635734-4390

10.23912/9781911635734-4774

Abstract

Event management is a dynamic field that has always benefited from latest advances in technology. In this chapter we will review some of the newest and most promising fields in information technology and discuss how they could be used to support event managers. Data is at the heart of information technology, in particular, data science aims to extract knowledge from data using machine learning techniques. The amount of data might make it not possible to process it on personal computers, leading to the field of big data. We will explore the fields of data science, big data as well as machine learning. Stored and transiting data might hold high value that attracts cyber criminals, information security focuses on how to protect data from accidental release and tampering of data. Basic concepts of information security, particularly cryptography, had a major contribution in the creation of the new paradigm of blockchains.

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Cite as

Ragab & Salama, 2021

Ragab, H. & Salama, M. (2021) "Chapter 11 Advanced Technology for Event Management" In: Salama, M. (ed) . Oxford: Goodfellow Publishers http://dx.doi.org/10.23912/9781911635734-4774

References

Gardner, H. (1983). Frames of Mind: The Theory of Multiple Intelligences. Basic Books.

Goodfellow, I., Bengio, Y. & Courville, A. (2018). Deep Learning. MIT Press.

Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, pp. 210-229.

https://doi.org/10.1147/rd.33.0210

Shearer, C. (2000). The CRISP-DM Model: The New Blueprint for Data Mining. Journal of Data Warehousing, 5, 13-22.

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Published in Event Project Management

Chapter 11 Advanced Technology for Event Management [Details]Price: USD $7.99*Licences / Downloadable file
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