Enhanced Continuous KNN Queries Using PINE on Road Networks

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 neighbor - NN, K nearest neighbors - KNN, continuous k nearest neighbors - CKNN) 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 neighbor (CKNN) query is an important type of query that finds continuously the K nearest objects to a query point on a given path. We focus on moving queries issued on stationary objects in spatial network database (SNDB) (e.g., continuously report the five nearest gas stations while I am driving.) The result of this type of query is a set of intervals (defined by split points) and their corresponding KNNs. This means that the KNN of an object traveling on one interval of the path remains the same all through that interval, until it reaches a split point where its KNNs change. Existing methods for CKNN are based on Euclidean distances. In this paper we propose a new algorithm for answering CKNN in SNDB w- - here the important measure for the shortest path is network distances rather than Euclidean distances. Our solution addresses a new type of query that is plausible to many applications where the answer to the query not only depends on the distances of the nearest neighbours, but also on the user or application need. By distinguishing between two types of split points, we reduce the number of computations to retrieve the continuous KNN of a moving object.