The Random Buffer Tree : A Randomized Technique for I/O-efficient Algorithms

TL;DR

This paper introduces the random buffer tree, a probabilistic self-balancing data structure that achieves expected amortized I/O-optimal bounds for dictionary operations and priority queue usage on large data sets.

Contribution

It presents a novel randomized data structure combining properties of treaps and buffer trees, with proven expected I/O bounds for efficient large-scale data management.

Findings

Expected amortized I/O bounds match those of buffer trees.

The structure is applicable as an I/O-efficient priority queue.

Proves probabilistic bounds for massive data set operations.

Abstract

In this paper, we present a probabilistic self-balancing dictionary data structure for massive data sets, and prove expected amortized I/O-optimal bounds on the dictionary operations. We show how to use the structure as an I/O-optimal priority queue. The data structure, which we call as the random buffer tree, abstracts the properties of the random treap and the buffer tree and has the same expected I/O-bounds as the buffer tree.