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Seeing the Trees for the Forest: Leveraging Tree-Shaped Substructures in Property Graphs

Daniel Aarao Reis Arturi, Christoph Köhnen, George Fletcher, Bettina Kemme, Stefanie Scherzinger

Abstract

Property graphs often contain tree-shaped substructures, yet they are not captured by existing proposals for graph schemas; likewise, query languages and query engines offer little-to-no native support for managing them systematically. As a first contribution, we report on a micro experiment that demonstrates the optimization potential of treating tree-shaped substructures as first class citizens in graph database systems. In particular, we show that in systems backed by relational engines, we can achieve substantial speedups by leveraging structural indexes, as originally developed for XML databases, to accelerate path queries. Based on our findings, we put forward a vision in which tree-shaped substructures are systematically managed throughout the graph query lifecycle, from modeling and schema design to indexing and query processing, and outline arising research questions.

Seeing the Trees for the Forest: Leveraging Tree-Shaped Substructures in Property Graphs

Abstract

Property graphs often contain tree-shaped substructures, yet they are not captured by existing proposals for graph schemas; likewise, query languages and query engines offer little-to-no native support for managing them systematically. As a first contribution, we report on a micro experiment that demonstrates the optimization potential of treating tree-shaped substructures as first class citizens in graph database systems. In particular, we show that in systems backed by relational engines, we can achieve substantial speedups by leveraging structural indexes, as originally developed for XML databases, to accelerate path queries. Based on our findings, we put forward a vision in which tree-shaped substructures are systematically managed throughout the graph query lifecycle, from modeling and schema design to indexing and query processing, and outline arising research questions.
Paper Structure (16 sections, 6 figures, 1 table)

This paper contains 16 sections, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Background
  4. Property-based graphs and query languages
  5. Indexing and query evaluation on tree-based data
  6. Current Indexing Techniques on Graphs
  7. Initial Evaluation
  8. Research Challenges
  9. Example 4.1.
  10. Example 4.2.
  11. Data definition languages
  12. Index implementation.
  13. Query Rewriting and Optimization.
  14. Query language and interaction design.
  15. Cost Estimation.
  16. ...and 1 more sections

Figures (6)

  • Figure 1: Excerpt of LDBC SNB graph. Tree-shaped substructures framed, with thicker edges, edge labels omitted.
  • Figure 2: Time series, managing Accounts. Edges drawn horizontally impose order among siblings. Edge labels omitted.
  • Figure 3: (a) Structural index example and (b) graph data stats
  • Figure 4: Speedups in GDBMS Kuzu and Apache AGE, using Dewey and PrePost indexes on wide trees (WT 1/2/3), a deep tree (DT), a tiny forest (TF), and forests from LDBC SNB SF1.
  • Figure 5: Place schema.
  • ...and 1 more figures