Approximate Evaluation of Label-Constrained Reachability Queries
Stefania Dumbrava, Angela Bonifati, Amaia Nazabal Ruiz Diaz, and, Romain Vuillemot

TL;DR
This paper introduces GRASP, a human-explainable summarization method for approximate evaluation of complex label-constrained reachability queries in graphs, providing scalable, accurate, and visualizable query answers with controllable error estimates.
Contribution
It presents the first visualization-driven system for approximate graph analytics of complex label-constrained reachability queries, including a novel summarization algorithm and error estimation.
Findings
GRASP summaries are small and scalable for large graphs.
The node group minimization problem is NP-complete.
The system effectively estimates query errors and scales well in experiments.
Abstract
The current surge of interest in graph-based data models mirrors the usage of increasingly complex reachability queries, as witnessed by recent analytical studies on real-world graph query logs. Despite the maturity of graph DBMS capabilities, complex label-constrained reachability queries, along with their corresponding aggregate versions, remain difficult to evaluate. In this paper, we focus on the approximate evaluation of counting label-constrained reachability queries. We offer a human-explainable solution to graph Approximate Query Processing (AQP). This consists of a summarization algorithm (GRASP), as well as of a custom visualization plug-in, which allows users to explore the obtained summaries. We prove that the problem of node group minimization, associated to the creation of GRASP summaries, is NP-complete. Nonetheless, our GRASP summaries are reasonably small in practice,…
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Taxonomy
TopicsData Management and Algorithms · Advanced Database Systems and Queries · Graph Theory and Algorithms
