Spatial Queries in Road Networks Based on PINE
Maytham H. Safar · Zenodo (CERN European Organization for Nuclear Research) · 2020
Abstract: Over the last decade, due to the rapid developments in information technology (IT), a new breed of information systems has appeared such as geographic information systems that introduced new challenges for researchers, developers and users. One of its applications is the car navigation system, which allows drivers to receive navigation instructions without taking their eyes off the road. Using a Global Positioning System (GPS) in the car navigation system enables the driver to perform a wide range of queries, from locating the car position, to finding a route from a source to a destination, or dynamically selecting the best route in real time. Several types of spatial queries (e.g., nearest neighbour- NN, K nearest neighbours – KNN, continuous k nearest neighbours – CKNN, reverse nearest neighbour – RNN) have been proposed and studied in the context of spatial databases. With spatial network databases (SNDB), objects are restricted to move on pre-defined paths (e.g., roads) that are specified by an underlying network. In our previous work, we proposed a novel approach, termed Progressive Incremental Network Expansion (PINE), to efficiently support NN and KNN queries. In this work, we utilize our developed PINE system to efficiently support other spatial queries such as CKNN. The continuous K nearest neighbour (CKNN) query is an important