QRB−tree Indexing: Optimized Spatial Index Expanding upon the QR−tree Index

Support for region queries is crucial in geographic information systems, which process exact queries through spatial indexing to filter features and subsequently refine the selection. Although the filtering step has been extensively studied, the refinement step has received little attention. This research builds upon the QR−tree index, which decomposes space into hierarchical grids, registers features to the grids, and builds an R−tree for each grid, to develop a new QRB−tree index with two levels of optimization. In the first level, a bucket is introduced in every grid in the QR−tree index to accelerate the loading and search steps of a query region for the grids within the query region. In the second level, the number of candidate features to be eliminated is reduced by limiting the features to those registered to the grids covering the corners of the query region. Subsequently, an approach for determining the maximal grid level, which significantly affects the performance of the QR−tree index, is proposed. Direct comparisons of time costs with the QR−tree index and geohash index show that the QRB−tree index outperforms the other two approaches for rough queries in large query regions and exact queries in all cases.

Partial Enclosure Range Searching

A new type of range searching problem, called the partial enclosure range searching problem, is introduced in this paper. Given a set of geometric objects [Formula: see text] and a query region [Formula: see text], our goal is to identify those objects in [Formula: see text] which intersect the query region [Formula: see text] by at least a fixed proportion of their original size. Two variations of this problem are studied. In the first variation, the objects in [Formula: see text] are axis-parallel line segments and the goal is to count the total number of members of [Formula: see text] so that their intersection with [Formula: see text] is at least a given proportion of their size. Here, [Formula: see text] can be an axis-parallel rectangle or a parallelogram of arbitrary orientation. In the second variation, [Formula: see text] is a polygon and [Formula: see text] is an axis-parallel rectangle. The problem is to report the area of the intersection between the polygon [Formula: see text] and a query rectangle [Formula: see text].

gmos: Rapid detection of genome mosaicism over short evolutionary distances

Prokaryotic and viral genomes are often altered by recombination and horizontal gene transfer. The existing methods for detecting recombination are primarily aimed at viral genomes or sets of loci, since the expensive computation of underlying statistical models often hinders the comparison of complete prokaryotic genomes. As an alternative, alignment-free solutions are more efficient, but cannot map (align) a query to subject genomes. To address this problem, we have developed gmos (Genome MOsaic Structure), a new program that determines the mosaic structure of query genomes when compared to a set of closely related subject genomes. The program first computes local alignments between query and subject genomes and then reconstructs the query mosaic structure by choosing the best local alignment for each query region. To accomplish the analysis quickly, the program mostly relies on pairwise alignments and constructs multiple sequence alignments over short overlapping subject regions only when necessary. This fine-tuned implementation achieves an efficiency comparable to an alignment-free tool. The program performs well for simulated and real data sets of closely related genomes and can be used for fast recombination detection; for instance, when a new prokaryotic pathogen is discovered. As an example, gmos was used to detect genome mosaicism in a pathogenic Enterococcus faecium strain compared to seven closely related genomes. The analysis took less than two minutes on a single 2.1 GHz processor. The output is available in fasta format and can be visualized using an accessory program, gmosDraw (freely available with gmos).

Efficient Processing of Movingk-Range Nearest Neighbor Queries in Directed and Dynamic Spatial Networks

Ak-range nearest neighbor (kRNN) query in a spatial network finds thekclosest objects to each point in the query region. The essential nature of thekRNN query is significant in location-based services (LBSs), where location-aware queries with query regions such askRNN queries are frequently used because of the issue of location privacy and the imprecision of the associated positioning techniques. Existing studies focus on reducing computation costs at the server side while processingkRNN queries. They also consider snapshot queries that are evaluated once and terminated, as opposed to moving queries that require constant updating of their results. However, little attention has been paid to evaluating movingkRNN queries in directed and dynamic spatial networks where every edge is directed and its weight changes in accordance with the traffic conditions. In this paper, we propose an efficient algorithm called MORAN that evaluates movingk-range nearest neighbor (MkRNN) queries in directed and dynamic spatial networks. The results of a simulation conducted using real-life roadmaps indicate that MORAN is more effective than a competitive method based on a shared execution approach.