Analytical queries on road networks

Abstract

Spatial analytical queries on road networks typically perform hundreds of thousands to several millions of shortest distance computations in the process of producing results. These queries require architectures that can compute a large number of network distances. Two architectures are evaluated on a variety of spatial analytical queries on road networks. The first architecture is a widely used hybrid architecture that uses a database to store spatial datasets, a road network distance computing module, and an analysis tool to tie them together into a single query processing pipeline. The second architecture uses of a distance oracle representation of a road network. This architecture stores the spatial datasets and the distance oracle inside the database, and the query processing is completely handled by the database. A detailed evaluation of the two architectures for a variety of analytical query processing tasks such as region, KNN, distance matrix and trajectory queries is presented and the lessons learned are discussed.

In-Path Oracles for Road Networks

“…Peng and Samet [42] introduced City Distance Oracles (CDO) for local area road networks spreading over several cities to achieve fast spatial analytic queries on a city road network. Peng et al [43] also propose SPark and Distance Oracles (SPDO), which is an approximate distance oracle for spatial analysis queries on continental road networks on Apache Spark which results in high throughput.…”

“…To illustrate how one can use the in-path oracle effectively, we consider a relational database [37,73,82] containing a few sample tables using which we can create a few scenarios. The oracle is stored as a table ORACLE (Z AB , poi_id).…”