An Experimental Analysis of Indoor Spatial Queries: Modeling, Indexing, and Processing

TL;DR

This paper provides an extensive experimental comparison of various models and indexes for indoor spatial queries, offering insights into their performance, complexities, and suitability for different indoor LBS scenarios.

Contribution

It conducts a comprehensive experimental study, comparing five indoor spatial query models and indexes using a new benchmark with real and synthetic data.

Findings

Identifies the most efficient models for specific indoor query types

Provides detailed performance metrics and complexity analysis

Offers recommendations for selecting models based on scenario requirements

Abstract

Indoor location-based services (LBS), such as POI search and routing, are often built on top of typical indoor spatial queries. To support such queries and indoor LBS, multiple techniques including model/indexes and search algorithms have been proposed. In this work, we conduct an extensive experimental study on existing proposals for indoor spatial queries. We survey five model/indexes, compare their algorithmic characteristics, and analyze their space and time complexities. We also design an in-depth benchmark with real and synthetic datasets, evaluation tasks and performance metrics. Enabled by the benchmark, we obtain and report the performance results of all model/indexes under investigation. By analyzing the results, we summarize the pros and cons of all techniques and suggest the best choice for typical scenarios.