Query processing of spatial objects: Complexity versus Redundancy

The management of complex spatial objects in applications, such as geography and cartography, imposes stringent new requirements on spatial database systems, in particular on efficient query processing. As shown before, the performance of spatial query processing can be improved by decomposing complex spatial objects into simple components. Up to now, only decomposition techniques generating a linear number of very simple components, e.g. triangles or trapezoids, have been considered. In this paper, we will investigate the natural trade-off between the complexity of the components and the redundancy, i.e. the number of components, with respect to its effect on efficient query processing. In particular, we present two new decomposition methods generating a better balance between the complexity and the number of components than previously known techniques. We compare these new decomposition methods to the traditional undecomposed representation as well as to the well-known decomposition into convex polygons with respect to their performance in spatial query processing. This comparison points out that for a wide range of query selectivity the new decomposition techniques clearly outperform both the undecomposed representation and the convex decomposition method. More important than the absolute gain in performance by a factor of up to an order of magnitude is the robust performance of our new decomposition techniques over the whole range of query selectivity

A survey of qualitative spatial representations

Representation and reasoning with qualitative spatial relations is an important problem in artificial intelligence and has wide applications in the fields of geographic information system, computer vision, autonomous robot navigation, natural language understanding, spatial databases and so on. The reasons for this interest in using qualitative spatial relations include cognitive comprehensibility, efficiency and computational facility. This paper summarizes progress in qualitative spatial representation by describing key calculi representing different types of spatial relationships. The paper concludes with a discussion of current research and glimpse of future work

Multi-Dimensional Joins

We present three novel algorithms for performing multi-dimensional joins and an in-depth survey and analysis of a low-dimensional spatial join. The first algorithm, the Iterative Spatial Join, performs a spatial join on low-dimensional data and is based on a plane-sweep technique. As we show analytically and experimentally, the Iterative Spatial Join performs well when internal memory is limited, compared to competing methods. This suggests that the Iterative Spatial Join would be useful for very large data sets or in situations where internal memory is a shared resource and is therefore limited, such as with today's database engines which share internal memory amongst several queries. Furthermore, the performance of the Iterative Spatial Join is predictable and has no parameters which need to be tuned, unlike other algorithms. The second algorithm, the Quickjoin algorithm, performs a higher-dimensional similarity join in which pairs of objects that lie within a certain distance epsilon of each other are reported. The Quickjoin algorithm overcomes drawbacks of competing methods, such as requiring embedding methods on the data first or using multi-dimensional indices, which limit the ability to discriminate between objects in each dimension, thereby degrading performance. A formal analysis is provided of the Quickjoin method, and experiments show that the Quickjoin method significantly outperforms competing methods. The third algorithm adapts incremental join techniques to improve the speed of calculating the Hausdorff distance, which is used in applications such as image matching, image analysis, and surface approximations. The nearest neighbor incremental join technique for indices that are based on hierarchical containment use a priority queue of index node pairs and bounds on the distance values between pairs, both of which need to modified in order to calculate the Hausdorff distance. Results of experiments are described that confirm the performance improvement. Finally, a survey is provided which instead of just summarizing the literature and presenting each technique in its entirety, describes distinct components of the different techniques, and each technique is decomposed into an overall framework for performing a spatial join

A new data structure and algorithm for spatial network representation.

by Fung Tze Wa.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 92-96).Abstracts in English and Chinese.Abstract in English --- p.iAbstract in Chinese --- p.iiAcknowledgements --- p.iiiTable of Contents --- p.iv-viList of Figures --- p.vii-ixList of Tables --- p.xChapter Chapter 1 --- IntroductionChapter 1.1 --- Introduction --- p.1Chapter 1.2 --- Motivation --- p.3Chapter 1.3 --- Purposes of this Research --- p.6Chapter 1.4 --- Contribution of this Research --- p.7Chapter 1.5 --- Outline of the Thesis --- p.9Chapter Chapter 2 --- Literature Review And Research IssuesChapter 2.1 --- Introduction --- p.11Chapter 2.2 --- Spatial Access Methods --- p.14Chapter 2.2.1 --- R-Tree --- p.15Chapter 2.2.2 --- R*-Tree --- p.19Chapter 2.2.3 --- R+-Tree --- p.21Chapter 2.3 --- Spatial Network Analysis --- p.22Chapter 2.4 --- Nearest Neighbor Queries --- p.23Chapter 2.5 --- Summary --- p.25Chapter Chapter 3 --- Data PreparationChapter 3.1 --- "Introduction (XML, GML), XML indexing" --- p.26Chapter 3.2 --- Spatial data from Lands Department --- p.31Chapter 3.3 --- Graph representation for Road Network data --- p.32Chapter 3.4 --- Summary --- p.35Chapter Chapter 4 --- XML Indexing for Spatial DataChapter 4.1 --- Introduction --- p.36Chapter 4.2 --- STR Packed R-Tree --- p.38Chapter 4.2.1 --- Implementation --- p.39Chapter 4.2.2 --- Experimental Result --- p.41Chapter 4.3 --- Summary --- p.48Chapter Chapter 5 --- Spatial NetworkChapter 5.1 --- Introduction --- p.50Chapter 5.2 --- CCAM: Connectivity-Clustered Access Method --- p.53Chapter 5.3 --- Shortest Path in Spatial Network --- p.56Chapter 5.4 --- A New Algorithm Specially for Partitioning /Clustering Network --- p.63Chapter 5.5 --- A New Simple heuristic for Shortest Path Problem for Spatial Network --- p.70Chapter 5.6 --- Summary --- p.74Chapter Chapter 6 --- Nearest Neighbor QueriesChapter 6.1 --- Introduction --- p.76Chapter 6.2 --- Modified Algorithm for Nearest Neighbor Queries --- p.78Chapter 6.3 --- Summary --- p.83Chapter Chapter 7 --- Conclusion and Future WorkChapter 7.1 --- Conclusion --- p.84Chapter 7.2 --- Future Work --- p.85Appendix Space Driven AlgorithmChapter A.1 --- Introduction --- p.87Chapter A.2 --- Fixed Grid --- p.88Chapter A.3 --- Z-curve --- p.89Chapter A.4 --- Hilbert curve --- p.90Chapter A.5 --- Conclusion --- p.91Bibliography --- p.9

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

Automating the Reconstruction of Neuron Morphological Models: the Rivulet Algorithm Suite

The automatic reconstruction of single neuron cells is essential to enable large-scale data-driven investigations in computational neuroscience. The problem remains an open challenge due to various imaging artefacts that are caused by the fundamental limits of light microscopic imaging. Few previous methods were able to generate satisfactory neuron reconstruction models automatically without human intervention. The manual tracing of neuron models is labour heavy and time-consuming, making the collection of large-scale neuron morphology database one of the major bottlenecks in morphological neuroscience. This thesis presents a suite of algorithms that are developed to target the challenge of automatically reconstructing neuron morphological models with minimum human intervention. We first propose the Rivulet algorithm that iteratively backtracks the neuron fibres from the termini points back to the soma centre. By refining many details of the Rivulet algorithm, we later propose the Rivulet2 algorithm which not only eliminates a few hyper-parameters but also improves the robustness against noisy images. A soma surface reconstruction method was also proposed to make the neuron models biologically plausible around the soma body. The tracing algorithms, including Rivulet and Rivulet2, normally need one or more hyper-parameters for segmenting the neuron body out of the noisy background. To make this pipeline fully automatic, we propose to use 2.5D neural network to train a model to enhance the curvilinear structures of the neuron fibres. The trained neural networks can quickly highlight the fibres of interests and suppress the noise points in the background for the neuron tracing algorithms. We evaluated the proposed methods in the data released by both the DIADEM and the BigNeuron challenge. The experimental results show that our proposed tracing algorithms achieve the state-of-the-art results

Formal extension of the relational model for the management of spatial and spatio-temporal data

[Resumen] En los últioms años, se ha realizado un gran esfuerzo investigador en la manipulación de datos especiales y Sistemas de Información Geográfica (SIG). Una clara limitación de las primeras aproximaciones es la falta de integración entre datos geográficos y alfanuméricos. Para resolver esto surge el área de Bases de Datos Espaciales. Los problemas que aparecen en este campo son muchos y complejos. Un primer ejemplo son las peculiaridades de las operaciones espaciales, como el calculo de la intersección espacial de dos superficies. Otro ejemplo es el elegir las estructuras de datos apropiadas (relaciones, capas, etc.) y el conjunto de operaciones adeucado. La combinación con las Bases de Datos Temporales da lugar a las Bases de Datos Espacio-temporales, en las que la inclusión de la dimensión temporal complica más los problemas anteriores. A pesar de la gran cantidad de aproximaciones propuestas, no se ha llegado todavía a una solución satisfactoria. La presente tesis propone una nueva solución que resuelve todos los problemas de modelado de datos espaciales y espacio-temporales resaltados arriba. Parte del trabajo se completó durante el proyecto ""CHOROCRONOS"": A Research Network for Saptiotemporal Database Systems"", financiado por la Unión Europea. El modelo propuesto en la tesis define tres tipos de dato punto, línea y superficie, que encajan perfectamente en la percepción humana. La definición de estos tipos de dato se basa en la definición previa de Quanta Espacial. Las estructuras de datos usadas son las relaciones no anidadas de modelo relacional puro. El conjunto de operaciones relacionales permite alcanzar casi por completo la funcionalidad propuesta en otros modelos. Todas las operaciones han sido definidas en base a un núcleo reducido de operaciones primitvas. Todos los tipos de datos, espaciales, espacio-temporales y convencionales se manipulan de forma uniforme con este conjunto de operaciones

Methodology of evaluation and correction of geometric data topology in QGIS software

Geographical Information Systems (GIS) has revolutionised the process of collecting and processing data, therefore, more and more data recorded in an analogue form are transformed into the digital format. However, the process of generating vector models poses a risk of appearing defects of different types. A methodology of correcting common geometric and topological errors that appear in the manual vectorization of a raster model was presented in the paper. The research material was the vector layer including the digitized version of several dozens of drawings of spatial development plans. The paper also presents a procedure for creating a vector model of spatial data with attention paid to potential sources of errors which could be incurred at the stage of its creation as well as indicates methods for their prevention. The tools and plug-ins for evaluation and revision of geometric and topological correctness of a vector model implemented in QGIS software were mainly used in the survey. Elaborated algorithms are aimed at acceleration of data processing to allow their usage during that process. Indeed, proper conducting of spatial analyses needs to administer a data set which is free of errors. Only then, is it possible to obtain proper results and draw appropriate conclusions