BWT Indexes for Optimal Joins in Graph Databases

Diego Arroyuelo and Gonzalo Navarro

Graph databases represent data as a labeled directed graph, where the labels refer to properties that connect the entities represented by their source and target vertices. Queries feature, most prominently, sets of edges where source, target, and/or label can be variables; each instantiation of the variables where all the edges occur in the graph is a solution to the query. Worst-case-optimal algorithms to solve those queries have been devised, but they pose significant space requirements. This overhead has hindered the adoption of worst-case-optimal algorithms in real systems. We show that a representation of the graph based on the extended BWT (eBWT), where each edge is seen as an independent string of length 3 (source, label, target) supports worst-case-optimal algorithms while using almost no extra space on top of the raw data. We then show how the idea is generalized to the relational model, where the strings can be longer than 3 and several eBWTs are needed to obtain worst-case optimality. The aim to minimize the amount of space in that case leads to consider novel eBWT variants, where columns other than the last can be chosen. Finally, we show how the same graph representation can be used to solve other typical queries, like finding graph paths that match regular expressions.