Compressing Highly Repetitive Binary Trees with an Application to Range Minimum Queries

Abstract

Tree compression is a well-studied area that aims at reducing the size of tree representations by exploiting different forms of repetition. While the underlying theory is well understood, there is still significant room for experimental investigation, particularly in the design of compressed representations that efficiently support navigational queries.
In this work, we address the problem of designing, engineering, and experimentally evaluating a compression technique for unlabeled binary trees based on repeated subtrees, yielding the minimal Directed Acyclic Graph (DAG) of the input tree. We show how this representation can be computed in linear time and space directly from a succinct encoding of the tree, and how it can be augmented with compact auxiliary data structures to support Lowest Common Ancestor (LCA) queries.
When the input tree is the Cartesian tree of an array, LCA queries can be used to answer Range Minimum Queries (RMQs) on the underlying array. This is particularly relevant in the encoding model, where the array is not accessible at query time, and a space lower bound of 2n-O(log n) bits is known. Given the numerous applications of RMQs, we use this problem as a case study for our experimental evaluation, testing our implementation on 11 real-world datasets. Our experiments show that, on almost every dataset, our implementation is the most space-efficient, using as few as 0.11n bits, while still delivering practical query times.