Graph Database Solution for Higher Order Spatial Statistics in the Era
  of Big Data
  We present an algorithm for the fast computation of the general $N$-point
spatial correlation functions of any discrete point set embedded within an
Euclidean space of $\mathbb{R}^n$. Utilizing the concepts of kd-trees and graph
databases, we describe how to count all possible $N$-tuples in binned
configurations within a given length scale, e.g. all pairs of points or all
triplets of points with side lengths $<r_{max}$. Through bench-marking we show
the computational advantage of our new graph based algorithm over more
traditional methods. We show that all 3-point configurations up to and beyond
the Baryon Acoustic Oscillation scale ($\sim$200 Mpc in physical units) can be
performed on current SDSS data in reasonable time. Finally we present the first
measurements of the 4-point correlation function of $\sim$0.5 million SDSS
galaxies over the redshift range $0.43<z<0.7$.
