ISQEA Project Open-sourced

Indoor-LBS Project

As a part of Prof. Hua Lu’s DFF project DATA MANAGEMENT FOUNDATIONS FOR INDOOR LBS, the open-sourced project ISQEA (‘Experimental Analysis of Indoor Spatial Queries’) is released at Github https://github.com/indoorLBS/ISQEA. This project contains an in-depth benchmark with datasets, evaluation tasks, and performance metrics. The datasets consist of real and synthetic data characterized by distinctive indoor topology.

Along with this project, an experimental paper entitled ‘‘An Experimental Analysis of Indoor Spatial Queries: Modeling, Indexing, and Processing’’ has been submitted to IEEE Transactions on Knowledge and Data Engineering. This paper refers to an extensive experimental evaluation of five indoor space model/indexes that support four typical indoor spatial queries, namely range query (RQ), k nearest neighbor query (kNNQ), shortest path query (SPQ), and shortest distance query (SDQ). Our evaluation concerns the costs in model/index construction and query processing using a model/index.

Tiantian Liu is the main contributor of this project and I am an important participant. We will continue to maintain this project.