Geographical Information Systems and geospatial data are seeing widespread use in various internet and mobile mapping applications. One of the areas where such technologies can be particularly valuable is aeronautical navigation. Pilots use paper charts for navigation, which, in contrast to modern mapping software, have some limitations. This project aims to develop an iOS application for phones and tablets that uses a GeoPackage database containing aeronautical geospatial data, which is rendered on a map to create an offline, feature-based mapping software to be used for navigation. Map features are selected from the database using R-Tree spatial indices. The attributes from each feature within the requested bounds are evaluated to determine the styling for that feature. Each feature, after applying the aforementioned styling, is drawn to an interactive map that supports basic zooming and panning functionalities. The application is written in Swift 3.0 and all features are drawn using iOS Core Graphics

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2014-10-09T12:30:33Z No. of bitstreams: 2 Dissertação - Savio Salvarino Teles de Oliveira - 2013.pdf: 6348358 bytes, checksum: 12e62cd925367772158d94e466de5827 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2014-10-09T14:44:35Z (GMT) No. of bitstreams: 2 Dissertação - Savio Salvarino Teles de Oliveira - 2013.pdf: 6348358 bytes, checksum: 12e62cd925367772158d94e466de5827 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2014-10-09T14:44:35Z (GMT). No. of bitstreams: 2 Dissertação - Savio Salvarino Teles de Oliveira - 2013.pdf: 6348358 bytes, checksum: 12e62cd925367772158d94e466de5827 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2013-06-28 / Fundação de Apoio à Pesquisa - FUNAPE / Geographic Information Systems (GIS) have received increasing attention in research institutes and industry in recent years. A Spatial Database Managament System (SDBMS) is one of the main components of a GIS and spatial join is one of the most important operations in SDBMS. Spatial join involves the relationship between two datasets, combining the geometries according some spatial predicate, such as intersection. Due to the increasing availability of spatial data, the growing number of GIS users, and the high cost of the processing of spatial operations, distributed SGBDEs (SGBDED) have been proposed as a good option to efficiently process spatial join on a cluster. This distributed processing brings some challenges, such as the data distribution and parallel and distributed processing of spatial join. This paper presents a platform for parallel and distributed processing of spatial joins in a cluster using data distribution techniques for dynamic datasets. Studies in the literature have explored data distribution techniques for static datasets, where any update requires data redistribution. This becomes unfeasible when using large datasets with frequent updates. Therefore, this paper proposes two new data distribution techniques for dynamic datasets: Proximity Area and Grid Proximity Area. These techniques have been evaluated to determine which scenarios each technique is more appropriate for. For this purpose, these techniques are evaluated in a real environment using datasets with different characteristics. Therefore, it is possible to evaluate the spatial join operation in real scenarios with each technique. / Os Sistemas de Informação Geográfica (SIG) têm recebido cada vez mais destaque nos institutos de pesquisa e na indústria nos últimos anos. Um Sistema de Gerência de Bancos de Dados Espaciais (SGBDE) é um dos principais componentes de um SIG e a junção espacial uma das operações mais importantes nos SGBDEs. Ela envolve o relacionamento entre duas bases de dados, combinando as geometrias de acordo com algum predicado espacial, como intersecção. Devido à crescente disponibilidade de dados espaciais, ao aumento no número de usuários dos SIGS e ao alto custo de processamento das operações espaciais, os SGBDE distribuídos (SGBDED) surgem com uma boa opção para processar a junção espacial de forma eficiente em um cluster de computadores. Esse processamento distribuído traz consigo alguns desafios, tais como a distribuição dos dados pelo cluster e o processamento paralelo e distribuído da junção espacial. O objetivo deste trabalho é apresentar uma plataforma de geoprocessamento paralelo e distribuído da junção espacial em um cluster de computadores, utilizando técnicas de distribuição de dados para bases de dados dinâmicas. Os trabalhos encontrados na literatura têm explorado técnicas de distribuição de dados indicadas para bases de dados estáticas, onde qualquer atualização da base de dados requer que todos os dados sejam novamente distribuídos pelo cluster. Isto se torna inviável com grandes bases de dados e que sofrem constantes atualizações. Por isso, este trabalho propõe duas novas técnicas de distribuição de dados com bases de dados dinâmicas: Proximity Area e Grid Proximity Area. Estas técnicas foram avaliadas para definir em quais cenários cada uma delas é mais apropriada. Para tal, estas técnicas foram avaliadas em um ambiente real com bases de dados com características diferentes, para que fosse possível experimentar a junção espacial distribuída em cenários diversos com cada técnica de distribuição de dados.

Our brain and nervous system is a vital organ to us, since it is from there our thoughts, personalities, and other mental capacities originate. Within this field of neuroscience a common method of study is to build and run large scale brain simulations where up to hundred thousand neurons are used to produce a model of a brain in three dimensional space. To find all neurites within a specific area is to perform a range query. A vast number of range queries are required when running brain simulations which makes it important that the data structure used to store the simulated neurons is efficient. This study evaluate three common data structures, also called spatial index; the R-tree, Quadtree and R*-tree (Rstar-tree). We test their performance for range queries with regards to execution time, incurred reads, build time, size of data and density of data. The data used is models of a typical neuron so that the characteristics of the data set is preserved. The results show that the R*-tree outperforms the other indices by being significantly more efficient compared to the others, with the R-tree having slightly worse performance than the Quadtree. The time it takes to build the index is to be almost identical for all implementations. / Vår hjärna och nervsystem är ett grundläggande organ för oss. Det är där ifrån våra tankar, personligheter och mentala kapaciteter kommer ifrån. Inom neurovetenskap är en vanlig forskningsmetod att köra storskaliga hjärnsimuleringar där hundratusentals neuroner används för att skapa en modell av hjärnan i 3D. För att hitta alla neuroner inom en viss area används en så kallad intervallfråga. En stor mängd intervallfrågor behövs för hjärnsimuleringar vilket gör det viktigt att datastrukturerna som används för detta är kostnadseffektiva. Denna studie har som mål att jämföra tre stycken vanliga datastrukturer som används för intervallfrågor. Dessa är R-tree, Quadtree och R*-tree. Deras prestanda testas för exekveringstid, antal läsningar, konstruktionstid, samt storlek och densitet på neuroner. För att skapa hjärnsimuleringen används en typisk neuron som standard sådant att dess karakteristiska egenskaper bevaras. Resultaten från studien visar att R*-tree hade den tydligt bästa prestandan för de givna kriterierna, och att Quadtree har en något bättre prestanda än R-tree. Tiden det tar att mata in neuronerna i datastrukturerna är i stort sett densamma.

This thesis deals with indexing of spatio-temporal data. It describes existing approaches to indexing data and support for indexing in Oracle Database 11g. The aim of this work is to design structures of databases for storing spatio-temporal data over Oracle Database 11g to propose experiments for these databases. Ways of spatio-temporal data storage are evaluated according to these experiments in terms of time demands of queries and appropriateness of using available indexing structure and spatial operators.

Submitted by Erika Demachki (erikademachki@gmail.com) on 2014-12-29T15:33:04Z No. of bitstreams: 2 license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Dissertação - Anderson Rogério Cunha - 2014.pdf: 4853685 bytes, checksum: d50cf557f1a067a91c2034443ee62df2 (MD5) / Approved for entry into archive by Erika Demachki (erikademachki@gmail.com) on 2014-12-29T15:39:23Z (GMT) No. of bitstreams: 2 license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Dissertação - Anderson Rogério Cunha - 2014.pdf: 4853685 bytes, checksum: d50cf557f1a067a91c2034443ee62df2 (MD5) / Made available in DSpace on 2014-12-29T15:39:23Z (GMT). No. of bitstreams: 2 license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Dissertação - Anderson Rogério Cunha - 2014.pdf: 4853685 bytes, checksum: d50cf557f1a067a91c2034443ee62df2 (MD5) Previous issue date: 2014-02-19 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Spatial join is one of the spatial operations of higher computational cost. Its complexity increases significantly when it involves multiple databases (multi-way spatial join). Traditional processing strategies of multi-way spatial join apply combinations of binary join algorithms on centralized computing environments. For complex queries, this approach requires much computational power, making it often unfeasible in centralized environments. This work proposes the Distributed Synchronous Traversal algorithm (DST), whose goal is to enable the distributed processing of multi-way spatial joins on a cluster of computers. The DST algorithm is based on Synchronous Traversal algorithm and processes the multiway spatial join in a single synchronous descent upon R-Trees levels of the database entries (the final outcome is built incrementally, without creating temporary databases). To the best of our knowledge, there are no other proposals in the literature that deal with this problem in a distributed fashion and on a peer-to-peer architecture. Many challenges had to be overcome, such as the definition of data structures that enabled the mapping of the semantics of queries of multi-way spatial join and coordination of the required distributed processing. DST proved to be satisfactorily parallelizable and scalable process real datasets in experiments performed in clusters of 1, 2, 4 and 8 servers. / A junção espacial (Spatial Join) é uma das operações espaciais de maior custo computacional. Sua complexidade aumenta significativamente quando envolve múltiplas bases de dados (multi-way spatial join). Estratégias tradicionais de processamento do multi-way spatial join aplicam combinações de algoritmos de junção binária sobre ambientes computacionais centralizados. Em consultas complexas, esse tipo de abordagem exige grande capacidade computacional muitas vezes inviável em ambientes centralizados. Neste trabalho é proposto o algoritmo Distributed Synchronous Traversal (DST), cujo objetivo é tornar viável a execução distribuída do multi-way spatial join em um cluster de computadores. O DST se baseia no algoritmo Synchronous Traversal e processa o multiway spatial join em uma única descida síncrona sobre os níveis das R-Trees das bases de dados de entrada. O resultado final é construído incrementalmente, sem a consolidação de dados intermediários. Até onde conhecemos, não há outras propostas na literatura para multi-way spatial join distribuído sobre uma arquitetura peer-to-peer. Muitos desafios tiveram que ser superados, como a definição de estruturas de dados que possibilitassem o mapeamento da semântica das consultas de multi-way spatial join e a coordenação do processamento distribuído das mesmas. O DST se mostrou satisfatoriamente paralelizável e escalável ao processar bases de dados reais em clusters de até 8 servidores.

<p>The project has been carried out at, and in association with, Micronic Laser Systems AB in Täby, Sweden. Micronic Laser Systems, manufacture laser pattern generators for the semiconductor and display markets. Laser pattern generators are used to create photomasks, which are a key component in the microlithographic process of manufacturing microchips and displays. </p><p>An essential problem to all modern semiconductor manufacturing is the constantly decreasing sizes of features and increasing use of resolution enhancement techniques (RET), leading to ever growing sizes of datasets describing the semiconductors. When sizes of datasets reach magnitudes of hundreds of gigabytes, visualisation, navigation and editing of any such dataset becomes extremely difficult. As of today this problem has no satisfying solution. </p><p>The project aims at the proposal of a geometry engine that effectively can deal with the evergrowing sizes of modern semiconductor lithography. This involves a new approach to handling data, a new format for spatial description of the datasets, hardware accelerated rendering and support for multiprocessor and distributed systems. The project has been executed without implying changes to existing data formats and the resulting application is executable on Micronics currently existing hardware platforms. </p><p>The performance of the new viewer system surpasses any old implementation by a varying factor. If rendering speed is the comparative factor, the new system is about 10-20 times faster than its old counterparts. In some cases, when hard disk access speed is the limiting factor, the new implementation is only slightly faster or as fast. And finally, spatial indexing allow some operations that previously lasted several hours, to complete in a few seconds, by eliminating all unnecessary disk-reading operations.</p>

Im Folgenden wird ein Promotionsprojekt zur Implementierung und Optimierung von hybriden Indexstrukturen beschrieben. Die erhöhte Suchperformance wird bei hybriden Indexstrukturen durch einen höheren Aufwand an Vorberechnungen bei Einfügeoperationen erreicht. Dadurch ergibt sich, im Gegensatz zu Ansätzen, welche mehrere Indexstrukturen miteinander verbinden oder getrennte Suchanfragen ausführen eine Effizienz der Reorganisation hybrider Indexstrukturen, die prohibitiv für den Einsatz in den meisten Anwendungen ist. Diese sollen innerhalb des Promotionsprojekts optimiert werden, um eine Einsatzfähigkeit in realistischen Szenarien gewährleisten zu können.

Made available in DSpace on 2014-07-29T14:57:47Z (GMT). No. of bitstreams: 1 dissertacao thiago b de oliveira 2010.pdf: 575961 bytes, checksum: 7a5a7e195780fa853d33c7629520df2a (MD5) Previous issue date: 2010-12-15 / The demand for spatial data processing systems that support the creation of massive applications has steadily grown in the increasingly ubiquitous computing world. These demands aims to explore the large amount of existing data to assist people s daily lives and provide new tools for business and government. Most of the current solutions to process spatial data do not meet the scalability needed, and thus new solutions that efficiently use distributed computing resources are needed. This work presents a distributed and scalable system called DSI-RTree, which implements a distributed index to process spatial data in a cluster of computers. We also have done a review of details related to the construction of the distributed spatial index, by addressing issues such as the size of data partitions, how that partitions are distributed and the impact of these definitions in the message flow on the cluster. An equation to calculate the size of the partitions based on the size of data sets is proposed, to ensure efficiently query processing on the proposed architecture. We have done some experiments running window queries in spatial data sets of 33,000 and 158,000 polygons and the results showed a scalability greater than linear. / Em face de um mundo computacional ubíquo cada vez mais possível, tem crescido constantemente a necessidade de sistemas de processamento de dados espaciais que suportem a criação de aplicações massivas para explorar a grande quantidade de dados existente, a fim de auxiliar a vida cotidiana das pessoas e prover novas ferramentas para empresas e governo. Soluções atuais de processamento, em sua maioria, não possuem a escalabilidade necessária para atender esta demanda e novas soluções distribuídas que usam eficientemente os recursos computacionais são necessárias. Este trabalho apresenta o DSIRTree, um sistema distribuído e escalável, que implementa a indexação e processamento distribuído de dados espaciais em um cluster de computadores. Uma avaliação de parâmetros da construção do índice espacial distribuído é realizada, abordando aspectos como o tamanho das partições criadas, a forma de distribuição destas partições e o impacto destas definições na troca de mensagens entre as máquinas do cluster. Uma fórmula para cálculo do tamanho das partições conforme o tamanho dos datasets é proposta, a fim de garantir eficiência no processamento de consultas na arquitetura projetada. Testes práticos do sistema mostraram uma escalabilidade maior que linear no processamento de consultas de janela em datasets espaciais de 32 e 158 mil polígonos.

The project has been carried out at, and in association with, Micronic Laser Systems AB in Täby, Sweden. Micronic Laser Systems, manufacture laser pattern generators for the semiconductor and display markets. Laser pattern generators are used to create photomasks, which are a key component in the microlithographic process of manufacturing microchips and displays. An essential problem to all modern semiconductor manufacturing is the constantly decreasing sizes of features and increasing use of resolution enhancement techniques (RET), leading to ever growing sizes of datasets describing the semiconductors. When sizes of datasets reach magnitudes of hundreds of gigabytes, visualisation, navigation and editing of any such dataset becomes extremely difficult. As of today this problem has no satisfying solution. The project aims at the proposal of a geometry engine that effectively can deal with the evergrowing sizes of modern semiconductor lithography. This involves a new approach to handling data, a new format for spatial description of the datasets, hardware accelerated rendering and support for multiprocessor and distributed systems. The project has been executed without implying changes to existing data formats and the resulting application is executable on Micronics currently existing hardware platforms. The performance of the new viewer system surpasses any old implementation by a varying factor. If rendering speed is the comparative factor, the new system is about 10-20 times faster than its old counterparts. In some cases, when hard disk access speed is the limiting factor, the new implementation is only slightly faster or as fast. And finally, spatial indexing allow some operations that previously lasted several hours, to complete in a few seconds, by eliminating all unnecessary disk-reading operations.

Time series data are sequences of data points collected at certain time intervals. The advance in mobile and sensor technologies has led to rapid growth in the available amount of time series data. The ability to search large time series data sets can be extremely useful in many applications. In healthcare, a system monitoring vital signals can perform a search against the past data and identify possible health threatening conditions. In engineering, a system can analyze performances of complicated equipment and identify possible failure situations or needs of maintenance based on historical data. Existing search methods for time series data are limited in many ways. Systems utilizing memory-bound or disk-bound indexes are restricted by the resources of a single machine or hard drive. Systems that do not use indexes must search through the entire database whenever a search is requested. The proposed system uses multidimensional index in the distributed storage environment to break the bound of one physical machine and allow for high data scalability. Utilizing an index allows the system to locate the patterns similar to the query without having to examine the entire dataset, which can significantly reduce the amount of computing resources required. The system uses an Apache HBase distributed key-value database to store the index and time series data across a cluster of machines. Evaluations were conducted to examine the system’s performance using synthesized data up to 30 million data points. The evaluation results showed that, despite some drawbacks inherited from an R-tree data structure, the system can efficiently search and retrieve patterns in large time series datasets.