Incremental Network Expansion Research Articles

Efficient processing of distance–time k th‐order skyline queries in bicriteria networks

With the increasing complexity of traffic conditions in road networks, the nearest destination cannot be necessarily reached in the fastest time. The traditional nearest neighbor (NN) and kNN searches in spatial network databases with single cost criterion are often strongly restrictive. In this paper, the authors consider the problem of kth-order skyline queries in bicriteria networks, where edges represent road segments. Their proposed kth-order skyline queries consider distance, time preferences of each edge, thus having two kinds of skyline queries, named distance optimal kth-order skyline query (DO-kOSQ) and time optimal kth-OSQ (TO-kOSQ). They design algorithms for the two kinds of skyline queries in bicriteria networks based on incremental network expansion method and further develop a maximum distance/time restriction strategy to improve the efficiency of the algorithms. Experimental results show that all of their methods are far below 1000 input–output input/output (IO) accesses and 1 s of central processing unit (CPU) time. For real road networks, their kOSQ+ queries need only 51.6% IO accesses and 59.6% CPU time of those for kOSQ queries, whereas for the larger road network the percentages are 51.8% and 51.2%, respectively. Thus, the results indicate the efficiency and effectiveness of their proposed methods.

Efficient Shortest Path Search in Large Road Network Environment: A Heuristic Approach

The road network now opens a new application area for the classic k-nearestneighbors (k-NN) queries, which retrieve k objects closest to a given query point. However, since most existing schemes are built on top of the Euclidean distance, they just And the k objects, failing in discovering the shortest paths to them and thus possibly bringing the so-called false dismissal problem. Aiming at finding both k objects and the shortest paths at the same time, this paper first selects candidate objects by the k-NN search scheme according to the underlying index structure and then finds the path to each of them by the modified A* algorithm. The path finding step stores the intermediary paths from the query point to all of the scanned nodes and then attempts to match the path segment common between the stored paths and the path to a new scan node instead of repeatedly running A* algorithm for each k point. Experiment results show that, for the road network data of Oldenburg Road Network and California Road Network, the proposed scheme improves the search speed by 1.3–3.0 times, compared with incremental network expansion, post-Dijkstra, and naïve method, also reducing the number of scan nodes by 11.8–66.8%.

Efficient successor retrieval operations for aggregate query processing on clustered road networks

Get-Successors (GS) which retrieves all successors of a junction is a kernel operation used to facilitate aggregate computations in road network queries. Efficient implementation of the GS operation is crucial since the disk access cost of this operation constitutes a considerable portion of the total query processing cost. Firstly, we propose a new successor retrieval operation Get-Unevaluated-Successors (GUS), which retrieves only the unevaluated successors of a given junction. The GUS operation is an efficient implementation of the GS operation, where the candidate successors to be retrieved are pruned according to the properties and state of the algorithm. Secondly, we propose a hypergraph-based model for clustering successively retrieved junctions by the GUS operations to the same pages. The proposed model utilizes query logs to correctly capture the disk access cost of GUS operations. The proposed GUS operation and associated clustering model are evaluated for two different instances of GUS operations which typically arise in Dijkstra's single source shortest path algorithm and incremental network expansion framework. Our simulation results show that the proposed successor retrieval operation together with the proposed clustering hypergraph model is quite effective in reducing the number of disk accesses in query processing.

Voronoi-based reverse nearest neighbor query processing on spatial networks

The use of Voronoi diagram has traditionally been applied to computational geometry and multimedia problems. In this paper, we will show how Voronoi diagram can be applied to spatial query processing, and in particular to Reverse Nearest Neighbor (RNN) queries. Spatial and geographical query processing, in general, and RNN in particular, are becoming more important, as online maps are now widely available. In this paper, using the concept of Voronoi diagram, we classify RNN into four types depending on whether the query point and the interest objects are the generator points of the Voronoi Polygon or not. Our approach is based on manipulating Network Voronoi Diagram properties and applying a progressive incremental network expansion for finding the polygon inner network distances required to solve RNN queries. Our experimentation results show that our approaches have good response times in answering RNN queries.

INCREMENTAL K-NEAREST-NEIGHBOR SEARCH ON ROAD NETWORKS

Most query search on road networks is either to find objects within a certain range (range search) or to find K nearest neighbors (KNN) on the actual road network map. In this paper, we propose a novel query, that is, incremental k nearest neighbor (iKNN). iKNN can be defined as given a set of candidate interest objects, a query point and the number of objects k, find a path which starts at the query point, goes through k interest objects and the distance of this path is the shortest among all possible paths. This is a new type of query, which can be used when we want to visit k interest objects one by one from the query point. This approach is based on expanding the network from the query point, keeping the results in a query set and updating the query set when reaching network intersection or interest objects. The basic theory of this approach is Dijkstra's algorithm and the Incremental Network Expansion (INE) algorithm. Our experiment verified the applicability of the proposed approach to solve the queries, which involve finding incremental k nearest neighbor.

Instance optimal query processing in spatial networks

The performance optimization of query processing in spatial networks focuses on minimizing network data accesses and the cost of network distance calculations. This paper proposes algorithms for network k-NN queries, range queries, closest-pair queries and multi-source skyline queries based on a novel processing framework, namely, incremental lower bound constraint. By giving high processing priority to the query associated data points and utilizing the incremental nature of the lower bound, the performance of our algorithms is better optimized in contrast to the corresponding algorithms based on known framework incremental Euclidean restriction and incremental network expansion. More importantly, the proposed algorithms are proven to be instance optimal among classes of algorithms. Through experiments on real road network datasets, the superiority of the proposed algorithms is demonstrated.

Group K-Nearest Neighbors queries in spatial network databases

This paper addresses the problem of Group K-Nearest Neighbors (GKNN) queries in spatial network databases, and suggests a novel approach based on real network distances. This approach essentially uses network Voronoi diagram properties together with a progressive incremental network expansion for determining the inner network distances that are needed to obtain GKNN queries.

EDAR Algorithm for Continuous KNN Queries Based on Pine

The continuous K nearest neighbor (CKNN) query is an important type of query that continuously finds the KNN to a query point on a given path. In this article we focus on moving queries issued on stationary objects in spatial network database (SNDB). 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 travelling 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 article, we propose a new algorithm for answering CKNN in SNDB where the important measure for the shortest path is network distances rather than Euclidean distances. We propose DAR and eDAR algorithms to address CKNN queries based on the progressive incremental network expansion (PINE) technique. Our experiments show that the eDAR approach has better response time, and requires fewer shortest distance computations and KNN queries than approaches that are based on VN3 using IE.