Mapping and Matching Algorithms: Data Mining by Adaptive Graphs

TL;DR

This paper introduces a novel adaptive graph mining approach to match two unknown bijective functions based on observed outputs across different locations and times, without knowing the inputs.

Contribution

It formulates a new problem of matching functions as an adaptive graph mining task and provides a simple, efficient solution with practical implementation.

Findings

The proposed method effectively matches functions in real-world scenarios.

The solution is parallelizable and computationally efficient.

The approach is demonstrated to be novel and practically applicable.

Abstract

Assume we have two bijective functions $U(x)$ and $M(x)$ with $M(x)\neq U(x)$ for all $x$ and $M,N: \N \rightarrow \N$ . Every day and in different locations, we see the different results of $U$ and $M$ without seeing $x$. We are not assured about the time stamp nor the order within the day but at least the location is fully defined. We want to find the matching between $U(x)$ and $M(x)$ (i.e., we will not know $x$). We formulate this problem as an adaptive graph mining: we develop the theory, the solution, and the implementation. This work stems from a practical problem thus our definitions. The solution is simple, clear, and the implementation parallel and efficient. In our experience, the problem and the solution are novel and we want to share our finding.