An Improvement for Spatial-Temporal Queries of ATMGRAPH

Abstract

As a knowledge graph for the field of ATM (Air Traffic Management), ATMGRAPH integrates aviation information from various sources, and provides a new way to comprehensively analyze ATM data, but the storage schema of ATMGRAPH is inefficient for trajectory-related queries which have typical spatial-temporal characteristics, thus cannot meet the application requirements. This paper presents an improved storage model of ATMGRAPH, specifically, we design a cluster structure to connect trajectory points and spatial-temporal information to speed up trajectory-related queries, and we link flights, airports, and weather information in an effective way to speed up weather-related queries. We create a dataset of about 10,000 real domestic flights, and build a knowledge graph of it which contains about 11.66 million triplets. Experimental results show that ATM knowledge graph constructed by this storage model can significantly improve the efficiency of spatial-temporal related queries.