Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

QQESPM: A Quantitative and Qualitative Spatial Pattern Matching Algorithm

Carlos Minervino, Claudio Campelo, Maxwell Oliveira, Salatiel Silva

TL;DR

The paper defines QQ-SPM, a generalized POI group search that combines quantitative distance constraints with qualitative connectivity between POIs via topological relations. To answer QQ-SPM queries, the authors adapt ESPM into QQESPM, leveraging the IL-Quadtree index and DE-9IM to support connectivity constraints. The work formalizes the QQ-SPM problem, introduces qq-n-match and qq-e-match notions, and provides an algorithm that performs level-wise pruning and joins to produce matches. An open-source implementation and empirical evaluation against a QQ-simple baseline demonstrate that QQESPM offers superior runtime efficiency and lower memory usage, with statistically significant improvements on real-world POI data. This work enables richer spatial pattern specification for location-based services and geospatial search systems, and suggests avenues for extending predicates and parallelization.

Abstract

The Spatial Pattern Matching (SPM) query allows for the retrieval of Points of Interest (POIs) based on spatial patterns defined by keywords and distance criteria. However, it does not consider the connectivity between POIs. In this study, we introduce the Qualitative and Quantitative Spatial Pattern Matching (QQ-SPM) query, an extension of the SPM query that incorporates qualitative connectivity constraints. To answer the proposed query type, we propose the QQESPM algorithm, which adapts the state-of-the-art ESPM algorithm to handle connectivity constraints. Performance tests comparing QQESPM to a baseline approach demonstrate QQESPM's superiority in addressing the proposed query type.

QQESPM: A Quantitative and Qualitative Spatial Pattern Matching Algorithm

TL;DR

The paper defines QQ-SPM, a generalized POI group search that combines quantitative distance constraints with qualitative connectivity between POIs via topological relations. To answer QQ-SPM queries, the authors adapt ESPM into QQESPM, leveraging the IL-Quadtree index and DE-9IM to support connectivity constraints. The work formalizes the QQ-SPM problem, introduces qq-n-match and qq-e-match notions, and provides an algorithm that performs level-wise pruning and joins to produce matches. An open-source implementation and empirical evaluation against a QQ-simple baseline demonstrate that QQESPM offers superior runtime efficiency and lower memory usage, with statistically significant improvements on real-world POI data. This work enables richer spatial pattern specification for location-based services and geospatial search systems, and suggests avenues for extending predicates and parallelization.

Abstract

The Spatial Pattern Matching (SPM) query allows for the retrieval of Points of Interest (POIs) based on spatial patterns defined by keywords and distance criteria. However, it does not consider the connectivity between POIs. In this study, we introduce the Qualitative and Quantitative Spatial Pattern Matching (QQ-SPM) query, an extension of the SPM query that incorporates qualitative connectivity constraints. To answer the proposed query type, we propose the QQESPM algorithm, which adapts the state-of-the-art ESPM algorithm to handle connectivity constraints. Performance tests comparing QQESPM to a baseline approach demonstrate QQESPM's superiority in addressing the proposed query type.
Paper Structure (11 sections, 1 theorem, 4 figures, 1 table, 1 algorithm)

This paper contains 11 sections, 1 theorem, 4 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Background
  4. IL-Quadtree
  5. DE-9IM
  6. Problem Formalization
  7. QQESPM algorithm
  8. Experiments and Results
  9. Experiments Description
  10. Results
  11. Conclusion

Key Result

Lemma 1

Suppose the node pair $(n_i, n_j)$ is a qq-n-match of the edge $e(v_i, v_j)$. Let $n^f_i$ and $n^f_j$ be the father nodes of $n_i$ and $n_j$ respectively. Then, the node pair $(n^f_i, n^f_j)$ is also a qq-n-match of $(v_i, v_j)$.

Figures (4)

  • Figure 1: Example of a distance-based spatial pattern (A) and a qualitative and quantitative spatial pattern (B)
  • Figure 2: Example of a quadtree space subdivision (A), and its associated tree structure (B)
  • Figure 3: Structure of Search Spatial Patterns espm2019
  • Figure 4: Avg. Execution Time by Dataset Size (A) and by Number of Vertices (B) for Algorithms QQESPM and QQ-simple

Theorems & Definitions (6)

  • Definition 1: spatial pattern
  • Definition 2: qq-e-match
  • Definition 3: match
  • Definition 4: qq-n-match
  • Lemma 1
  • proof