Optimizing Spatial Databases

This paper describes the best way to improve the optimization of spatial databases: through spatial indexes. The most commune and utilized spatial indexes are R-tree and Quadtree and they are presented, analyzed and compared in this paper. Also there are given a few examples of queries that run in Oracle Spatial and are being supported by an R-tree spatial index. Spatial databases offer special features that can be very helpful when needing to represent such data. But in terms of storage and time costs, spatial data can require a lot of resources. This is why optimizing the database is one of the most important aspects when working with large volumes of data.Spatial Database, Spatial Index, R-tree, Quadtree, Optimization

Extending General Compact Querieable Representations to GIS Applications

The raster model is commonly used for the representation of images in many domains, and is especially useful in Geographic Information Systems (GIS) to store information about continuous variables of the space (elevation, temperature, etc.). Current representations of raster data are usually designed for external memory or, when stored in main memory, lack efficient query capabilities. In this paper we propose compact representations to efficiently store and query raster datasets in main memory. We present different representations for binary raster data, general raster data and time-evolving raster data. We experimentally compare our proposals with traditional storage mechanisms such as linear quadtrees or compressed GeoTIFF files. Results show that our structures are up to 10 times smaller than classical linear quadtrees, and even comparable in space to non-querieable representations of raster data, while efficiently answering a number of typical queries.Comment: This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Actions H2020-MSCA-RISE-2015 BIRDS GA No. 690941

A storage and access architecture for efficient query processing in spatial database systems

Due to the high complexity of objects and queries and also due to extremely large data volumes, geographic database systems impose stringent requirements on their storage and access architecture with respect to efficient query processing. Performance improving concepts such as spatial storage and access structures, approximations, object decompositions and multi-phase query processing have been suggested and analyzed as single building blocks. In this paper, we describe a storage and access architecture which is composed from the above building blocks in a modular fashion. Additionally, we incorporate into our architecture a new ingredient, the scene organization, for efficiently supporting set-oriented access of large-area region queries. An experimental performance comparison demonstrates that the concept of scene organization leads to considerable performance improvements for large-area region queries by a factor of up to 150

Efficient Processing of Spatial Joins Using R-Trees

Abstract: In this paper, we show that spatial joins are very suitable to be processed on a parallel hardware platform. The parallel system is equipped with a so-called shared virtual memory which is well-suited for the design and implementation of parallel spatial join algorithms. We start with an algorithm that consists of three phases: task creation, task assignment and parallel task execu-tion. In order to reduce CPU- and I/O-cost, the three phases are processed in a fashion that pre-serves spatial locality. Dynamic load balancing is achieved by splitting tasks into smaller ones and reassigning some of the smaller tasks to idle processors. In an experimental performance compar-ison, we identify the advantages and disadvantages of several variants of our algorithm. The most efficient one shows an almost optimal speed-up under the assumption that the number of disks is sufficiently large. Topics: spatial database systems, parallel database systems

Efficient geographic information systems: Data structures, Boolean operations and concurrency control

Geographic Information Systems (GIS) are crucial to the ability of govern mental agencies and business to record, manage and analyze geographic data efficiently. They provide methods of analysis and simulation on geographic data that were previously infeasible using traditional hardcopy maps. Creation of realistic 3-D sceneries by overlaying satellite imagery over digital elevation models (DEM) was not possible using paper maps. Determination of suitable areas for construction that would have the fewest environmental impacts once required manual tracing of different map sets on mylar sheets; now it can be done in real time by GIS. Geographic information processing has significant space and time require ments. This thesis concentrates on techniques which can make existing GIS more efficient by considering these issues: Data Structure, Boolean Operations on Geographic Data, Concurrency Control. Geographic data span multiple dimensions and consist of geometric shapes such as points, lines, and areas, which cannot be efficiently handled using a traditional one-dimensional data structure. We therefore first survey spatial data structures for geographic data and then show how a spatial data structure called an R-tree can be used to augment the performance of many existing GIS. Boolean operations on geographic data are fundamental to the spatial anal ysis common in geographic data processing. They allow the user to analyze geographic data by using operators such as AND, OR, NOT on geographic ob jects. An example of a boolean operation query would be, Find all regions that have low elevation AND soil type clay. Boolean operations require signif icant time to process. We present a generalized solution that could significantly improve the time performance of evaluating complex boolean operation queries. Concurrency control on spatial data structures for geographic data processing is becoming more critical as the size and resolution of geographic databases increase. We present algorithms to enable concurrent access to R-tree spatial data structures so that efficient sharing of geographic data can occur in a multi user GIS environment

PSVDAG: Compact Voxelized Representation of 3D Scenes Using Pointerless Sparse Voxel Directed Acyclic Graphs

This paper deals with the issue of geometry representation of voxelized three-dimensional scenes using hierarchical data structures. These include pointerless Sparse Voxel Octrees that have no pointers on child nodes and allow a compact binary representation. However, if necessary, there is a possibility to reconstruct these pointers for rapid traversing. Sparse Voxel Directed Acyclic Graphs added 32-bit pointers to child nodes and merging of common subtrees, which can be considered lossless compression. By merging common subtrees, no decompression overhead occurs at the time of traversing. The hierarchical data structure proposed herein - the Pointerless Sparse Voxel Directed Acyclic Graph - incorporates the benefits of both - pointerless Sparse Voxel Octrees (by avoiding storing pointers on child nodes) and Sparse Voxel Directed Acyclic Graphs (by allowing the merging of common subtrees due the introduction of labels and callers). The proposed data structure supports the quick and easy reconstruction of pointers by introducing the Active Child Node Count. It also potentially allows Child Node Mask compression of its nodes. This paper presents the proposed data structure and its binary-level encoding in detail. It compares the effectiveness of the representation of voxelized three-dimensional scenes (originally represented in OBJ format) in the proposed data structure with the data structures mentioned above. It also summarizes statistical data providing a more detailed description of the various parameters of the data structure for different scenes stored in multiple resolutions

A Data Structure for Spatio-Temporal Databases

The advantages and applications of spatial mechanisms are well documented; however, there are very few being designed. The principal hinderance to the design of spatial mechanisms is the great difficulty involved in specifying spatial problems and in interpreting spatial solutions. Similarly, the development of spatial codes to implement these techniques is held back by the lack of means to easily visualize and verify solutions, particularly in the realm of relational databases. If spatial mechanisms are to be successful, the designer must be able to synthesize, analyse and evaluate, as well as load and extract information, using a single code representing a spatial structure. This entails the implementation of spatial relationships involving spatial data structures. It is with this in mind that the Canadian Hydrographic Service database group embarked on the development of a new type of spatial database structure based on the quadtree concept

On the Practice and Application of Context-Free Language Reachability

The Context-Free Language Reachability (CFL-R) formalism relates to some of the most important computational problems facing researchers and industry practitioners. CFL-R is a generalisation of graph reachability and language recognition, such that pairs in a labelled graph are reachable if and only if there is a path between them whose labels, joined together in the order they were encountered, spell a word in a given context-free language. The formalism finds particular use as a vehicle for phrasing and reasoning about program analysis, since complex relationships within the data, logic or structure of computer programs are easily expressed and discovered in CFL-R. Unfortunately, The potential of CFL-R can not be met by state of the art solvers. Current algorithms have scalability and expressibility issues that prevent them from being used on large graph instances or complex grammars. This work outlines our efforts in understanding the practical concerns surrounding CFL-R, and applying this knowledge to improve the performance of CFL-R applications. We examine the major difficulties with solving CFL-R-based analyses at-scale, via a case-study of points-to analysis as a CFL-R problem. Points-to analysis is fundamentally important to many modern research and industry efforts, and is relevant to optimisation, bug-checking and security technologies. Our understanding of the scalability challenge motivates work in developing practical CFL-R techniques. We present improved evaluation algorithms and declarative optimisation techniques for CFL-R, capitalising on the simplicity of CFL-R to creating fully automatic methodologies. The culmination of our work is a general-purpose and high-performance tool called Cauliflower, a solver-generator for CFL-R problems. We describe Cauliflower and evaluate its performance experimentally, showing significant improvement over alternative general techniques