abstract: The spatial databases are used to store geometric objects such as points, lines, polygons. Querying such complex spatial objects becomes a challenging task. Index structures are used to improve the lookup performance of the stored objects in the databases, but traditional index structures cannot perform well in case of spatial databases. A significant amount of research is made to ingest, index and query the spatial objects based on different types of spatial queries, such as range, nearest neighbor, and join queries. Compressed Spatial Bitmap Index (cSHB) structure is one such example of indexing and querying approach that supports spatial range query workloads (set of queries). cSHB indexes and many other approaches lack parallel computation. The massive amount of spatial data requires a lot of computation and traditional methods are insufficient to address these issues. Other existing parallel processing approaches lack in load-balancing of parallel tasks which leads to resource overloading bottlenecks. In this thesis, I propose novel spatial partitioning techniques, Max Containment Clustering and Max Containment Clustering with Separation, to create load-balanced partitions of a range query workload. Each partition takes a similar amount of time to process the spatial queries and reduces the response latency by minimizing the disk access cost and optimizing the bitmap operations. The partitions created are processed in parallel using cSHB indexes. The proposed techniques utilize the block-based organization of bitmaps in the cSHB index and improve the performance of the cSHB index for processing a range query workload. / Dissertation/Thesis / Masters Thesis Computer Science 2018

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-02-14T12:07:18Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação de Economia_Tassia Germano_2015.pdf: 2534403 bytes, checksum: 0da3b9309e247ea148852b161740b42c (MD5) / Made available in DSpace on 2017-02-14T12:07:18Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação de Economia_Tassia Germano_2015.pdf: 2534403 bytes, checksum: 0da3b9309e247ea148852b161740b42c (MD5) Previous issue date: 2015-03-02 / CNPq / Esta dissertação examina a configuração da segregação residencial na cidade do Recife, através do cálculo de índices sintéticos espaciais de segregação. Para este propósito, são empregados dados dos setores censitários dos Censos Demográficos de 2000 e 2010 do IBGE para os grupos populacionais descritos pelas variáveis renda dos responsáveis e raça dos residentes. A partir do cômputo das medidas sintéticas espaciais de segregação e da espacialização dos índices locais, as evidências apontam para padrões de macrossegregação na cidade. Especificamente, para os responsáveis com rendimento superior a 10 salários mínimos há um claro padrão de concentração espacial desses grupos nas regiões que apresentam amenidades locais: Rio Capibaribe, Praia de Boa Viagem e parques da cidade. Além disso, estas áreas são bem localizadas, próximas ao centro, e com forte oferta de serviços públicos (saneamento, por exemplo). Por sua vez, embora o arranjo espacial da população mais pobre e dos negros apresente-se mais disperso no território, é possível verificar alguns padrões de segregação destas populações. Estudos sobre a mensuração da segregação residencial e identificação da sua configuração espacial são bastante escassos na literatura do país, mais ainda para a cidade do Recife. O presente estudo objetiva contribuir para a análise da segregação residencial nas cidades brasileiras. / This master thesis examines the configuration of residential segregation in the city of Recife, by calculating spatial synthetic indices of segregation. For such purpose, data of census layers of the Demographic Census of 2000 and 2010 from IBGE are applied to the population groups described by variables of income of the household head and by race of household’s residents. From the estimation of synthetic spatial segregation measures and spatial distribution of local indices, the evidence points to standards of macro-segregation in the city. In particular, groups of household heads with income higher than 10 minimum wages present a clear pattern of spatial concentration in the regions that have local amenities, such as: Capibaribe River, Boa Viagem Beach and city parks. Moreover, these areas are well located, close to the center and with a strong supply of public services (sanitation, for example). In turn, although the spatial arrangement of poor and black populations is shown to be more disperse in the territory, some segregation patterns of these populations may be seen. Studies on the measurement of residential segregation and identification of its spatial configuration are generally scarce in the country's literature, and even more limited about the city of Recife. This study aims to contribute to the analysis of residential segregation in Brazilian cities.

Location plays an ever increasing role in modern web-based applications. Many of these applications leverage off-the-shelf search engine technology to provide interactive access to large collections of data. Unfortunately, these commodity search engines do not provide special support for location-based indexing and retrieval. Many applications overcome this constraint by applying geographic bounding boxes in conjunction with range queries. We propose an alternative technique based on geographic identifiers and suggest it will yield faster query evaluation and provide higher search precision. Our experiment compared the two approaches by executing thousands of unique queries on a dataset with 1.8 million records. Based on the quantitative results obtained, our technique yielded drastic performance improvements in both query execution time and precision.

With the large number of spatial queries for spatial data objects changing with time in many applications, e.g., the location based services and geographic information systems, spatio-temporal databases have been developed to manipulate them in spatial or temporal databases. We focus on queries for stationary and moving objects in the spatial database in the present. However, there is no total ordering for the large volume and complicated objects which may change their geometries with time. A spatial access method based on the spatial index structure attempts to preserve the spatial proximity as much as possible. Then, the number of disk access which takes the response time is reduced during the query processing. Therefore, in this dissertation, based on the NA-tree, first, we propose the NA-tree join method over the stationary objects. Our NA-tree join simply uses the correlation table to directly obtain candidate leaf nodes based on two NA-trees which have non-empty overlaps. Moreover, our NA-tree join accesses objects once from those candidate leaf nodes and returns pairs of objects which have non-empty overlaps. Second, we propose the NABP method for the continuous range queries over the moving objects. Our NABP method uses the bit-patterns of regions in the NA-tree to check the relation between the range queries and moving objects. Our NABP method searches only one path in the NA-tree for the range query, instead of more than one path in the R*-tree-based method which has the overlapping problem. When the number of range queries increases with time, our NABP method incrementally updates the affected range queries by bit-patterns checking, instead of rebuilding the index like the cell-based method. From the experimental results, we have shown that our NABP method needs less time than the cell-based method for range queries update and less time than the R*-tree-based method for moving objects update. Based on the Hilbert curve with the good clustering property, we propose the ANHC method to answer the all-nearest-neighbors query by our ONHC method. Our ONHC method is used to answer the one-nearest-neighbor query over the stationary objects. We generate direction sequences to store the orientations of the query block in the Hilbert curve of different orders. By using quaternary numbers and direction sequences of the query block, we obtain the relative locations of the neighboring blocks and compute their quaternary numbers. Then, we directly access the neighboring blocks by their sequence numbers which is the transformation of the quaternary numbers from base four to ten. The nearest neighbor can be obtained by distance comparisons in these blocks. From the experimental results, we have shown that our ONHC and ANHC methods need less time than CCSF method for the one-nearest-neighbor query and the method based on R*-trees for the all-nearest-neighbors query, respectively.

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.

Spatial database systems widely employ spatial indexing structures to speed up the processing of spatial queries. Many of the proposed spatial indices in the literature, such as the R-tree, assume magnetic disks (i.e., HDDs) as the underlying storage device. They are termed as disk-based spatial indices. On the other hand, several spatial database applications are increasingly using flash-based Solid State Drives (SSDs) and thus, designing spatial indices for these storage devices has gained increasing attention. This is due the fact that, compared to HDDs, SSDs offer smaller size, lighter weight, lower power consumption, better shock resistance, and faster reads and writes. Hence, specific indices for SSDs, termed flash-aware spatial indices, have been proposed in the literature to deal with the intrinsic characteristics of SSDs, such as the asymmetric costs of reads and writes. However, the research to date has not been able to establish a flash-aware spatial index that actually exploits all the benefits of SSDs. This PhD thesis advances on the literature as follows. We firstly define a methodology to create spatial datasets for experimental evaluations. We also propose FESTIval, a versatile framework that provides a common and unique environment to execute experimental evaluations. Such contributions served as a foundation to conduct performance analysis along this PhD work. By using this foundation, we analyze the performance behavior of spatial indices on different storage devices, such as HDDs and SSDs. Further, we discuss the applicability of employing flash simulators on the evaluation of spatial indices. The findings of these experiments contributed to the proposal of eFIND, a generic and efficient framework for flash-aware spatial indexing. eFIND is generic because it can port a wide range of disk-based spatial indices to SSDs. eFIND is also efficient because it is based on a set of design goals that exploits SSD performance. Performance tests showed that, compared to the state of the art, eFIND improved the construction of ported disk-based spatial indices and the execution of spatial queries. For porting the R-tree (i.e., the eFIND R-tree), eFIND showed performance reductions from 43% to 77% to build spatial indices, and from 4% to 23% to execute spatial queries. For porting the xBR+-tree (i.e., the eFIND xBR+-tree), eFIND showed reductions from 28% to 83% to build spatial indices and up to 35% in the spatial query processing. / Sistemas de banco de dados espaciais empregam estruturas de indexação espaciais para acelerar o processamento de consultas espaciais. Muitos dos índices espaciais propostos na literatura, como a R-tree, assumem que os dispositivos de armazenamentos são os discos magnéticos (i.e., HDDs) e são denominados índices espaciais baseados em disco. Por outro lado, várias aplicações de banco de dados espaciais estão cada vez mais usando Solid State Drives (SSDs) baseados em memória flash e, assim, projetar índices espaciais para esses dispositivos tem ganhado cada vez mais atenção. Isso se deve ao fato de que, em comparação com os HDDs, os SSDs oferecem menor tamanho, menor peso, menor consumo de energia, melhor resistência a choques além de leituras e escritas mais rápidas. Assim, índices espaciais para memória flash têm sido propostos na literatura para lidar com as características intrínsecas dos SSDs, como os custos assimétricos de leituras e escritas. No entanto, a pesquisa até o momento não conseguiu estabelecer um índice espacial que realmente explora todos os benefícios dos SSDs. Esta tese de doutorado avança na literatura da seguinte forma. Primeiramente, é definida uma metodologia para criar conjuntos de dados espaciais para avaliações experimentais. Também é proposto FESTIval, um arcabouço versátil que fornece um ambiente comum e único para executar avaliações experimentais. Tais contribuições serviram como base para conduzir análises de desempenho ao longo deste trabalho de doutorado. Usando essa base, o comportamento de desempenho de índices espaciais em diferentes dispositivos de armazenamento, como HDDs e SSDs, é analisado. Além disso, discutese a aplicabilidade de simuladores flash na avaliação experimental de índices espaciais. Os resultados desses experimentos contribuíram para a proposta de eFIND, uma estrutura genérica e eficiente para indexação espacial em memórias flash. eFIND é genérico porque pode portar uma ampla gama de índices espaciais baseados em disco para SSDs. eFIND também é eficiente porque é baseado em um conjunto de objetivos de projeto que exploram o desempenho do SSD. Os testes de desempenho mostraram que, em comparação com o estado da arte, eFIND melhorou a construção de índices espaciais portados e a execução de consultas espaciais. Para portar a R-tree (ou seja, a eFIND R-tree), eFIND mostrou melhorias de desempenho de 43% a 77% para construir índices espaciais e de 4% a 23% para executar consultas espaciais. Para portar a xBR+-tree (ou seja, a eFIND xBR+-tree), eFIND mostrou melhorias de 28% a 83% para construir índices espaciais e de até 35% no processamento de consultas espaciais.