Resilient Big Data Monetization

TL;DR

This paper introduces a novel approach to resilient big data monetization using k-dominance and m-connectivity frameworks, proposing a greedy algorithm called Plutus to optimize the connection and synergy between measurement tools and common interests.

Contribution

It presents a new theoretical model for resilient data monetization and an algorithmic solution, Plutus, to enhance the robustness and effectiveness of data-driven monetization strategies.

Findings

Plutus effectively isolates measurement tools for domination.

The approach establishes strong synergy between interests and tools.

The method sustains divergence within measurement tools.

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