Performance Of Spatial Queries Research Articles

A Systematic Review of Spatial Approximations in Spatial Database Systems

Many applications rely on spatial information retrieval, which involves costly computational geometric algorithms to process spatial queries. Spatial approximations simplify the geometric shape of complex spatial objects, allowing faster spatial queries at the expense of result accuracy. In this sense, spatial approximations have been employed to efficiently reduce the number of objects under consideration, followed by a refinement step to restore accuracy. For instance, spatial index structures employ spatial approximations to organize spatial objects in hierarchical structures (e.g., the R-tree). It leads to the interest in studying how spatial approximations can be efficiently employed to improve spatial query processing. This article presents a systematic review on this topic. We gather relevant studies by performing a search string on several digital libraries. We further expand the studies under consideration by employing a single iteration of the snowballing approach, where we track the reference list of selected papers. As a result, we provide an overview and comparison of existing approaches that propose, evaluate, or make use of spatial approximations to optimize the performance of spatial queries. The spatial approximations mentioned by the approaches are also summarized. Further, we characterize the approaches and discuss some future trends.

Parallel algorithm for improving the performance of spatial queries in SQL: The use cases of SQLite/SpatiaLite and PostgreSQL/PostGIS databases

This paper proposes an open-source algorithm that performs parallel processing of spatial queries, during which an initial selection of objects to be subjected to spatial relationship tests is done using a spatial index. These data are then further subdivided by the use of the OFFSET and LIMIT clauses into still smaller subgroups, to which spatial relationship tests utilizing complex calculations are assigned, thereby creating multiple processes running in parallel. This algorithm was tested using data from the SQLite/SpatiaLite and PostgreSQL/PostGIS database. In processing spatial relationship queries involving six threads, the algorithm yielded a 3.6X maximum speed-up increase in performance compared to single-thread processing on SQLite/SpatiaLite database and 5.1X maximum speed-up on PostgreSQL/PostGIS database. In single-layer analyses (e.g., area calculation, buffer generation), a 5X speed-up time in query processing was observed.

IDEAL: a Vector-Raster Hybrid Model for Efficient Spatial Queries over Complex Polygons.

Geometric computation can be heavy duty for spatial queries, in particular for complex geometries such as polygons with many edges based on a vector-based representation. While many techniques have been provided for spatial partitioning and indexing, they are mainly built on minimal bounding boxes or other approximation methods, which will not mitigate the high cost of geometric computation. In this paper, we propose a novel vector-raster hybrid approach through rasterization, where pixel-centric rich information is preserved to help not only filtering out more candidates but also reducing geometry computation load. Based on the hybrid model, we develop an efficient rasterization based ray casting method for point-in-polygon queries and a circle buffering method for point-to-polygon distance calculation, which is a common operation for distance based queries. Our experiments demonstrate that the hybrid model can boost the performance of spatial queries on complex polygons by up to one order of magnitude.

FESTIval: A versatile framework for conducting experimental evaluations of spatial indices

The use of a spatial index is a common strategy to improve the performance of spatial queries in spatial database systems and Geographic Information Systems. Choosing the right spatial index to be employed in a given context requires a quantitative method to analyze the performance of spatial indices. This is done through extensive experimental evaluations. However, conducting these evaluations is an expensive, error-prone, and challenging task because (i) spatial objects are complex data to manage, (ii) spatial indices can apply different parameter values and thus assume distinct configurations, and (iii) there are indices specifically developed for different storage systems, such as disks and flash memories. In this article, we propose FESTIval, a versatile framework for conducting experimental evaluations of spatial indices. FESTIval has the following main advantages:•the support for different types of disk-based and flash-aware spatial indices;•the specification and execution of user-defined workloads;•the use of a data schema that stores index configurations and statistical data of executed workloads.Because of its characteristics, FESTIval allows users to reproduce executed experiments. Further, FESTIval provides an extensible environment, where any spatial dataset can be handled by spatial indices. FESTIval has been used to validate new proposals of flash-aware spatial indices, such as eFIND-based indices.

LAZY R-tree: The R-tree with lazy splitting algorithm

The spatial index is a data structure formed according to the position and shape of the spatial object or the relationship between the spatial objects according to certain rules, and the spatial data is managed by an effective spatial data structure. The quality of a spatial index directly affects the performance of spatial queries. The R-tree index structure is a highly efficient spatial index. According to the R-tree query rule, when performing spatial query, most data that is not related to the query condition can be filtered out, and finally, a few leaf nodes can be accessed to query the data satisfying the condition. Its query performance is affected by factors such as non-leaf node overlap and node space utilisation. This article proposes a lazy splitting method to improve the R-tree construction process. The scheme works as follows: (1) When a node overflows, it creates an overflow node for that node and all overflow nodes are saved in a hash table. (2) If the node continues to insert data, the data are added to its overflow node. (3) When an overflow node is saturated, the node and its overflow node are split into two saturated nodes. We use both simulated and actual data to perform experiments. The experimental results show that an R-tree constructed by the lazy algorithm is superior to an R-tree constructed using the original R-tree PM algorithm or the corner-based splitting (CBS) algorithm based on the number of splits created, the node space used and the efficiency of region queries and k-nearest neighbour (kNN) queries.

2DPR-Tree: Two-Dimensional Priority R-Tree Algorithm for Spatial Partitioning in SpatialHadoop

Among spatial information applications, SpatialHadoop is one of the most important systems for researchers. Broad analyses prove that SpatialHadoop outperforms the traditional Hadoop in managing distinctive spatial information operations. This paper presents a Two Dimensional Priority R-Tree (2DPR-Tree) as a new partitioning technique in SpatialHadoop. The 2DPR-Tree employs a top-down approach that effectively reduces the number of partitions accessed to answer the query, which in turn improves the query performance. The results were evaluated in different scenarios using synthetic and real datasets. This paper aims to study the quality of the generated index and the spatial query performance. Compared to other state-of-the-art methods, the proposed 2DPR-Tree improves the quality of the generated index and the query execution time.

공간 데이터 스트림 질의 정확도 향상을 위한 다단계 부하제한 기법

In spatial data stream management systems, it is needed appropriate load shedding algorithm because real-time input spatial data streams could exceed the limitation of main memory. However previous researches, lack regard for input ratio and spatial utilization rates of spatial data streams, or the characteristics of data source which generates data streams with spatial information efficiently, can lead to decrease the performance and accuracy of spatial data stream query. Therefore, multi-level load shedding scheme for spatial data stream management systems is proposed to increase the spatial query performance and accuracy. This proposed scheme limits overloads in relation to the input rate and the characteristics of data source first, and then, if needed, query data representing low query participation probability based on spatial utilizations are dropped relatively. Our experiments show that the proposed method could decrease load shedding frequency for previous researches by more than 11% despite query results accuracy and query performance are superior at 0.04% and 3%.

Research on the Spatial Query Technology of Geospatial Database

The application requirement of Geospatial data is increasing and complex as it is getting numerous as a result of furthering study on geosciences. Based on a deeply research on Oracle Spatial storage management mechanism, this paper proposed a method that applies the graph theory to domain of optimizing spatial query of massive geographical data, and established a geospatial data query model in order to settle a problem of lower spatial query efficiency in geospatial database. Combining with the practical applications, this paper did a conventional spatial query test and a spatial query based on geospatial data model respectively. The result is that the spatial query based on geospatial data query model has a better efficiency than that on conventional method. Besides, this model can greatly improve the spatial query performance and this improvement will be increasingly apparent as the data volume increases.

Современные методы поиска и индексации многомерных данных в приложениях моделирования больших динамических сцен

This paper is dedicated to review of recent methods for indexing of multidimensional data and their use for modeling of large-scale dynamic scenes. The problem arises in many application domains such as computer graphics systems, virtual and augmented reality systems, CAD systems, robotics, geographical information systems, project management systems, etc. Two fundamental approaches to the indexing of multidimensional data, namely object aggregation and spatial decomposition, have been outlined. In the context of the former approach balanced search trees, also referred as object pyramids, have been discussed. In particular, generalizations of B-trees such as R-trees, R*-trees, R+-trees have been discussed in conformity to indexing of large data located in external memory and accessible by pages. The conducted analysis shows that object aggregation methods are well suited for static scenes but very limited for dynamic environments. The latter approach assumes the recursive decomposition of scene volume in accordance with the cutting rules. Space decomposition methods are octrees, A-trees, bintrees, K-D-trees, X-Y-trees, treemaps and puzzle-trees. They support more reasonable compromise between performance of spatial queries and expenses required to update indexing structures and to keep their in concordant state under permanent changes in the scenes. Compared to object pyramids, these methods look more promising for dramatically changed environments. It is concluded that regular dynamic octrees are most effective for the considered applications of modeling of large-scale dynamic scenes.

A new enhancement to the R-tree node splitting

The performance of spatial queries depends mainly on the underlying index structure used to handle them. R-tree, a well-known spatial index structure, suffers largely from high overlap and high coverage resulting mainly from splitting the overflowed nodes. Assigning the remaining entries to the underflow node in order to meet the R-tree minimum fill constraint ( Remaining Entries problem) may induce high overlap or high coverage. This is done without considering the geometric features of the remaining entries and this may cause a very non-optimized expansion of that particular node. This paper presents a solution to the above problem. The proposed solution to this problem distributes rectangles as follows: (1) assign m entries to the first node, which are nearest to the first seed; (2) assign other m entries to the second node, which are nearest to the second seed; (3) assign the remaining entries one by one to the nearest seed. Several experiments on real data, as well as synthetic data, show that the proposed splitting algorithm outperforms the efficient version of the original R-tree in terms of query performance.

Information-associated join indices for spatial range search

Abstract Spatial join indices are join indices constructed for spatial objects. Similar to join indices in relational database systems, spatial join indices improve efficiency of spatial join operations. In this paper, a spatial-information-associated join indexing mechanism is developed to speed up spatial queries, especially, spatial range queries. Three distance-associated join index structures: basic, ring-structured and hierarchical, are developed and studied. Such join indexing structures can be further extended to include orientation information for flexible applications, which leads to zone-structured and other spatial-information-associated join indices. Our performance study and analysis show that spatial-information-associated join indices substantially improve the performance of spatial queries and that different structures are best suited for different applications.