6 Access Methods and Query Processing Techniques

The performance of a database management system (DBMS) is fundamentally dependent on the access methods and query processing techniques available to the system. Traditionally, relational DBMSs have relied on well-known access methods, such as the ubiquitous B +-tree, hashing with chaining, and, in som

Towards a Scalable Dynamic Spatial Database System

With the rise of GPS-enabled smartphones and other similar mobile devices, massive amounts of location data are available. However, no scalable solutions for soft real-time spatial queries on large sets of moving objects have yet emerged. In this paper we explore and measure the limits of actual algorithms and implementations regarding different application scenarios. And finally we propose a novel distributed architecture to solve the scalability issues.Comment: (2012

A rule-based video database system architecture

Cataloged from PDF version of article.We propose a novel architecture for a video database system incorporating both spatio-temporal and semantic (keyword, event/activity and category-based) query facilities. The originality of our approach stems from the fact that we intend to provide full support for spatio-temporal, relative object-motion and similarity-based objecttrajectory queries by a rule-based system utilizing a knowledge-base while using an object-relational database to answer semantic-based queries. Our method of extracting and modeling spatio-temporal relations is also a unique one such that we segment video clips into shots using spatial relationships between objects in video frames rather than applying a traditional scene detection algorithm. The technique we use is simple, yet novel and powerful in terms of effectiveness and user query satisfaction: video clips are segmented into shots whenever the current set of relations between objects changes and the video frames, where these changes occur, are chosen as keyframes. The directional, topological and third-dimension relations used for shots are those of the keyframes selected to represent the shots and this information is kept, along with frame numbers of the keyframes, in a knowledge-base as Prolog facts. The system has a comprehensive set of inference rules to reduce the number of facts stored in the knowledge-base because a considerable number of facts, which otherwise would have to be stored explicitly, can be derived by rules with some extra effort. (C)2002 Elsevier Science Inc. All rights reserved

New data structures and algorithms for the efficient management of large spatial datasets

[Resumen] En esta tesis estudiamos la representación eficiente de matrices multidimensionales, presentando nuevas estructuras de datos compactas para almacenar y procesar grids en distintos ámbitos de aplicación. Proponemos varias estructuras de datos estáticas y dinámicas para la representación de matrices binarias o de enteros y estudiamos aplicaciones a la representación de datos raster en Sistemas de Información Geográfica, bases de datos RDF, etc. En primer lugar proponemos una colección de estructuras de datos estáticas para la representación de matrices binarias y de enteros: 1) una nueva representación de matrices binarias con grandes grupos de valores uniformes, con aplicaciones a la representación de datos raster binarios; 2) una nueva estructura de datos para representar matrices multidimensionales; 3) una nueva estructura de datos para representar matrices de enteros con soporte para consultas top-k de rango. También proponemos una nueva representación dinámica de matrices binarias, una nueva estructura de datos que proporciona las mismas funcionalidades que nuestras propuestas estáticas pero también soporta cambios en la matriz. Nuestras estructuras de datos pueden utilizarse en distintos dominios. Proponemos variantes específicas y combinaciones de nuestras propuestas para representar grafos temporales, bases de datos RDF, datos raster binarios o generales y datos raster temporales. También proponemos un nuevo algoritmo para consultar conjuntamente un conjuto de datos raster (almacenado usando nuestras propuestas) y un conjunto de datos vectorial almacenado en una estructura de datos clásica, mostrando que nuestra propuesta puede ser más rápida y usar menos espacio que otras alternativas. Nuestras representaciones proporcionan interesantes trade-offs y son competitivas en espacio y tiempos de consulta con representaciones habituales en los diferentes dominios.[Resumo] Nesta tese estudiamos a representación eficiente de matrices multidimensionais, presentando novas estruturas de datos compactas para almacenar e procesar grids en distintos ámbitos de aplicación. Propoñemos varias estruturas de datos estáticas e dinámicas para a representación de matrices binarias ou de enteiros e estudiamos aplicacións á representación de datos raster en Sistemas de Información Xeográfica, bases de datos RDF, etc. En primeiro lugar propoñemos unha colección de estruturas de datos estáticas para a representación de matrices binarias e de enteiros: 1) unha nova representación de matrices binarias con grandes grupos de valores uniformes, con aplicacións á representación de datos raster binarios; 2) unha nova estrutura de datos para representar matrices multidimensionais; 3) unha nova estrutura de datos para representar matrices de enteiros con soporte para consultas top-k. Tamén propoñemos unha nova representación dinámica de matrices binarias, unha nova estrutura de datos que proporciona as mesmas funcionalidades que as nosas propostas estáticas pero tamén soporta cambios na matriz. As nosas estruturas de datos poden utilizarse en distintos dominios. Propoñemos variantes específicas e combinacións das nosas propostas para representar grafos temporais, bases de datos RDF, datos raster binarios ou xerais e datos raster temporais. Tamén propoñemos un novo algoritmo para consultar conxuntamente datos raster (almacenados usando as nosas propostas) con datos vectoriais almacenados nunha estrutura de datos clásica, amosando que a nosa proposta pode ser máis rápida e usar menos espazo que outras alternativas. As nosas representacións proporcionan interesantes trade-offs e son competitivas en espazo e tempos de consulta con representacións habituais nos diferentes dominios.[Abstract] In this thesis we study the efficient representation of multidimensional grids, presenting new compact data structures to store and query grids in different application domains. We propose several static and dynamic data structures for the representation of binary grids and grids of integers, and study applications to the representation of raster data in Geographic Information Systems, RDF databases, etc. We first propose a collection of static data structures for the representation of binary grids and grids of integers: 1) a new representation of bi-dimensional binary grids with large clusters of uniform values, with applications to the representation of binary raster data; 2) a new data structure to represent multidimensional binary grids; 3) a new data structure to represent grids of integers with support for top-k range queries. We also propose a new dynamic representation of binary grids, a new data structure that provides the same functionalities that our static representations of binary grids but also supports changes in the grid. Our data structures can be used in several application domains. We propose specific variants and combinations of our generic proposals to represent temporal graphs, RDF databases, OLAP databases, binary or general raster data, and temporal raster data. We also propose a new algorithm to jointly query a raster dataset (stored using our representations) and a vectorial dataset stored in a classic data structure, showing that our proposal can be faster and require less space than the usual alternatives. Our representations provide interesting trade-offs and are competitive in terms of space and query times with usual representations in the different domains

High-Dimensional Spatio-Temporal Indexing

There exist numerous indexing methods which handle either spatio-temporal or high-dimensional data well. However, those indexing methods which handle spatio-temporal data well have certain drawbacks when confronted with high-dimensional data. As the most efficient spatio-temporal indexing methods are based on the R-tree and its variants, they face the well known problems in high-dimensional space. Furthermore, most high-dimensional indexing methods try to reduce the number of dimensions in the data being indexed and compress the information given by all dimensions into few dimensions but are not able to store now - relative data. One of the most efficient high-dimensional indexing methods, the Pyramid Technique, is able to handle high-dimensional point-data only. Nonetheless, we take this technique and extend it such that it is able to handle spatio-temporal data as well. We introduce a technique for querying in this structure with spatio-temporal queries. We compare our technique, the Spatio-Temporal Pyramid Adapter (STPA), to the RST-tree for in-memory and on-disk applications. We show that for high dimensions, the extra query-cost for reducing the dimensionality in the Pyramid Technique is clearly exceeded by the rising query-cost in the RST-tree. Concluding, we address the main drawbacks and advantages of our technique