The class of spatial query over the networks has been extensively studied in the past decade. However, most of the current research focuses on the single objective solution, which is based on the optimization of one objective function. In this paper, we propose a framework to address one type of the multi-objective spatial query problems (i.e., the range query) on the road networks. Towards this end, we utilize a filtering and refinement paradigm to significantly prune the search space and localize the expensive refinement operation within a small candidate space. We experimentally verified the applicability and efficiency of the proposed method with real world datasets.

The growing popularity of online Earth visualization tools and geo-realistic games and the availability of high resolution terrain data have motivated a new class of queries to the interests of the GIS and spatial database community: spatial queries (e.g., kNN) over land surface. However, the fundamental challenges that restrict the applicability of these studies to real world applications are the prohibitive time complexity and storage overhead to precompute the shortest surface paths. In this paper, for the first time, we propose an approximate solution to address both challenges and allow browsing the shortest surface paths in O () time, where N is the size of the terrain. With this method, the time and space requirements for an exhaustive all-pair pre-computation have been reduced from O(N 3) to O(N 1.5) and O(N) respectively. The substantial savings in both time and storage are gained by taking advantage of the fact that the O(N 2) surface paths only deviate from approximate straight lines at O ( ) points, termed rough vertices. As a result, we propose a linear time shortest surface path computation algorithm between two arbitrary vertices and a linear size storage structure, which captures all the shortest surface paths between any pair of vertices. We experimentally verified the applicability and scalability of the proposed methods with large real world and synthetic data sets and showed that accuracy higher than 97 % can be obtained in most cases.

Harnessing the potential of today's ever growing and dynamic geospatial data requires the development of novel visual analysis interfaces, tools and technologies. In this paper, we present GeoDec, a generic framework capable of supporting queries and visualizations of real-world spatio-temporal data sets. We show, for various locations and applications, how our innovative Query Driven Design enhances the visual analysis of geospatial data through the interactive manipulation of queries and the temporal navigation of these query results.

As geo-realistic rendering of land surfaces is becoming commonplace in geographical information systems (GIS), games and online Earth visualization platforms, a new type of k Nearest Neighbor (kNN) queries, "surface" k Nearest Neighbor (skNN) queries, has emerged and been investigated recently, which extends the traditional kNN queries to a constrained third dimension (i.e., land surface). All existing techniques, however, assume a static environment, limiting their utility in emerging applications (e.g., Location-based Services) where objects move. In this paper, for the first time, we propose two exact methods that can continuously answer skNN queries in a highly dynamic environment which allows for arbitrary movements of data objects. The first method, inspired by the existing techniques in monitoring kNN in road networks [7] maintains an analogous counterpart of the Dijkstra Expansion Tree on land surface, called Surface Expansion Tree (SE-Tree). However, we show the concept of expansion tree for land surface does not work as SE-tree suffers from intrinsic defects: it is fat and short, and hence does not improve the query efficiency. Therefore, we propose a superior approach that partitions SE-Tree into hierarchical chunks of pre-computed surface distances, called Angular Surface Index Tree (ASI-Tree). Unlike SE-tree, ASI-Tree is a well balanced thin and tall tree. With ASI-Tree, we can continuously monitor skNN queries efficiently with low CPU and I/O overheads by both speeding up the surface shortest path computations and localizing the searches. We experimentally verify the applicability and evaluate the efficiency of the proposed methods with both real world and synthetic data sets. ASI-Tree consistently and significantly outperforms SE-Tree in all cases.

The class of k Nearest Neighbor (kNN) queries is frequently used in geospatial applications. Many studies focus on processing kNN in Euclidean and road network spaces. Meanwhile, with the recent advances in remote sensory devices that can acquire detailed elevation data, the new geospatial applications heavily operate on this third dimension, i.e., land surface. Hence, for the field of databases to stay relevant, it should be able to efficiently process spatial queries given this constrained third dimension. However, online processing of the surface k Nearest Neighbor (skNN) queries is quite challenging due to the huge size of land surface models which renders any accurate distance computation on the surface extremely slow. In this paper, for the first time, we propose an index structure on land surface that enables exact and fast responses to skNN queries. Two complementary indexing schemes, namely Tight Surface Index (TSI) and Loose Surface Index (LSI), are constructed and stored collectively on a single novel data structure called Surface Index R-tree (SIR-tree). With those indexes, we can process skNN query efficiently by localizing the search and minimizing the invocation of the costly surface distance computation and hence incurring low I/O and computation costs. Our algorithm does not need to know the value of k a priori and can incrementally expand the search region using SIR-tree and report the query result progressively. It also reports the exact shortest surface paths to the query results. We show through experiments with real world data sets that our algorithm has better performance than the competitors in both efficiency and accuracy.