Abstract
The huge technological progress we have witnessed in the last decade has enabled us to generate data at an unprecedented rate, leading to what has become the era of big data. However, big data is not just about generating, storing, and retrieving massive amounts of data. The focus should rather be on new analytical approaches that would enable us to extract actionable intelligence from this ocean of data. From a security standpoint, one of the main issues that need to be addressed is the trustworthiness of each source or piece of information. In this paper, we propose an approach to assess and quantify the trust level of both information sources and information items. Our approach leverages the vast literature on citation ranking, and we clearly show the benefits of adapting citation ranking mechanisms to this new domain, both in terms of scalability and in terms of quality of the results.
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References
Amato, F., Mazzeo, A., Moscato, V., Picariello, A.: Semantic management of multimedia documents for e-government activity. In: International Conference on Complex, Intelligent and Software Intensive Systems (CISIS 2009), pp. 1193–1198 (2009)
Arrow, K.J.: Social Choice and Individual Values. John Wiley & Sons (1963)
Bapat, R.B., Raghavan, T.E.S.: Nonnegative Matrices and Applications. Encyclopedia of Mathematics and its Applications, vol. 64. Cambridge University Press (1997)
Eldén, L.: Matrix Methods in Data Mining and Pattern Recognition. Fundamentals of Algorithms, vol. 4. Society for Industrial and Applied Mathematics (2007)
Google Official Blog: We knew the web was big... (July 2008), http://googleblog.blogspot.com/2008/07/we-knew-web-was-big.html
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: Bringing order to the web. Technical Report 1999-66, Stanford InfoLab (1999)
Resnick, P., Kuwabara, K., Zeckhauser, R., Friedman, E.: Reputation systems. Communications of the ACM 43(12), 45–48 (2000)
Resnick, P., Zeckhauser, R.: Trust Among Strangers in Internet Transactions: Empirical Analysis of eBay’s Reputation System. In: The Economics of the Internet and E-commerce, Advances in Applied Microeconomics, vol. 11, pp. 127–157. Emerald Group Publishing (2002)
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Albanese, M. (2013). Measuring Trust in Big Data. In: Aversa, R., Kołodziej, J., Zhang, J., Amato, F., Fortino, G. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2013. Lecture Notes in Computer Science, vol 8286. Springer, Cham. https://doi.org/10.1007/978-3-319-03889-6_28
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DOI: https://doi.org/10.1007/978-3-319-03889-6_28
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03888-9
Online ISBN: 978-3-319-03889-6
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