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arXiv · 1509.04545

Resilient Big Data Monetization

Rossi Kamal·Choong Seon Hong
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Abstract

Resilient Big Data monetization is devised as k-dominance and m-connectivity problems, such that common-interests are connected by k-ways to measurement tools, which are tied within each other in m-ways. Consequently, a greedy approximation algorithm Plutus (i.e resembling Greek god of wealth) is proposed, which isolates measurement tools to ac-quire domination over common-interests, establishes synergy from common-interests to measurement tools and then acquires divergence and sustains it within measurement tools

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Rossi Kamal, Choong Seon Hong. 2015-09-15. Resilient Big Data Monetization. https://arxiv.org/abs/1509.04545

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