The impact of spatial data redundancy on SOLAP query performance

Geographic Data Warehouses (GDW) are one of the main technologies used in decision-making processes and spatial analysis, and the literature proposes several conceptual and logical data models for GDW. However, little effort has been focused on studying how spatial data redundancy affects SOLAP (Spatial On-Line Analytical Processing) query performance over GDW. In this paper, we investigate this issue. Firstly, we compare redundant and non-redundant GDW schemas and conclude that redundancy is related to high performance losses. We also analyze the issue of indexing, aiming at improving SOLAP query performance on a redundant GDW. Comparisons of the SB-index approach, the star-join aided by R-tree and the star-join aided by GiST indicate that the SB-index significantly improves the elapsed time in query processing from 25% up to 99% with regard to SOLAP queries defined over the spatial predicates of intersection, enclosure and containment and applied to roll-up and drill-down operations. We also investigate the impact of the increase in data volume on the performance. The increase did not impair the performance of the SB-index, which highly improved the elapsed time in query processing. Performance tests also show that the SB-index is far more compact than the star-join, requiring only a small fraction of at most 0.20% of the volume. Moreover, we propose a specific enhancement of the SB-index to deal with spatial data redundancy. This enhancement improved performance from 80 to 91% for redundant GDW schemas.FAPESPCNPqCoordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)INEPFINE

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Photography-based taxonomy is inadequate, unnecessary, and potentially harmful for biological sciences

The question whether taxonomic descriptions naming new animal species without type specimen(s) deposited in collections should be accepted for publication by scientific journals and allowed by the Code has already been discussed in Zootaxa (Dubois & Nemésio 2007; Donegan 2008, 2009; Nemésio 2009a–b; Dubois 2009; Gentile & Snell 2009; Minelli 2009; Cianferoni & Bartolozzi 2016; Amorim et al. 2016). This question was again raised in a letter supported by 35 signatories published in the journal Nature (Pape et al. 2016) on 15 September 2016. On 25 September 2016, the following rebuttal (strictly limited to 300 words as per the editorial rules of Nature) was submitted to Nature, which on 18 October 2016 refused to publish it. As we think this problem is a very important one for zoological taxonomy, this text is published here exactly as submitted to Nature, followed by the list of the 493 taxonomists and collection-based researchers who signed it in the short time span from 20 September to 6 October 2016

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

SB-Index : um índice espacial baseado em bitmap para data warehouse geográfico

Geographic Data Warehouses (GDW) became one of the main technologies used in decision-making processes and spatial analysis since they provide the integration of Data Warehouses, On-Line Analytical Processing and Geographic Information Systems. As a result, a GDW enables spatial analyses together with agile and flexible multidimensional analytical queries over huge volumes of data. On the other hand, there is a challenge in a GDW concerning the query performance, which consists of retrieving data related to ad-hoc spatial query windows and avoiding the high cost of star-joins. Clearly, mechanisms to provide efficient query processing, as index structures, are essential. In this master s thesis, a novel index for GDW is introduced, namely the SB-index, which is based on the Bitmap Join Index and the Minimum Bounding Rectangle. The SB-index inherits the Bitmap Index legacy techniques and introduces them in GDW, as well as it enables support for predefined spatial attribute hierarchies. The SB-index validation was performed through experimental performance tests. Comparisons among the SB-index approach, the star-join aided by R-tree and the star-join aided by GiST indicated that the SB-index significantly improves the elapsed time in query processing from 76% up to 96% with regard to queries defined over the spatial predicates of intersection, enclosure and containment and applied to roll-up and drill-down operations. In addition, the impact of the increase in data volume on the performance was analyzed. The increase did not impair the performance of the SB-index, which highly improved the elapsed time in query processing. Moreover, in this master s thesis there is an experimental investigation on how does the spatial data redundancy affect query response time and storage requirements in a GDW? . Redundant and non-redundant GDW schemas were compared, concluding that redundancy is related to high performance losses. Then, aiming at improving query performance, the SB-index performance was evaluated on the redundant GDW schema. The results pointed out that SB-index significantly improves the elapsed time in query processing from 25% up to 99%. Finally, a specific enhancement of the SB-index was developed in order to deal with spatial data redundancy. With this enhancement, the minimum performance gain observed became 80%.Universidade Federal de Minas GeraisO Data Warehouse Geográfico (DWG) tornou-se uma das principais tecnologias de suporte à decisão, pois promove a integração de data warehouses, On-Line Analytical Processing e Sistemas de Informações Geográficas. Por isso, um DWG viabiliza a análise espacial aliada à execução de consultas analíticas multidimensionais envolvendo enormes volumes de dados. Por outro lado, existe um desafio relativo ao desempenho no processamento de consultas, que envolvem janelas de consulta espaciais ad-hoc e várias junções entre tabelas. Claramente, mecanismos para aumentar o desempenho do processamento de consultas, como as estruturas de indexação, são essenciais. Nesta dissertação, propõe-se um novo índice para DWG chamado SB-index, baseado no Índice Bitmap de Junção e no Retângulo Envolvente Mínimo. O SB-index herda todo o legado de técnicas do Índice Bitmap e o introduz no DWG. Além disso, ele provê suporte a hierarquias de atributos espaciais predefinidas. Este índice foi validado por meio de testes experimentais de desempenho. Comparações entre o SB-index, a junção estrela auxiliada pela R-tree e a junção-estrela auxiliada por GiST indicaram que o SB-index diminui significativamente o tempo de resposta do processamento de consultas roll-up e drill-down relacionadas aos predicados espaciais intersecta , está contido e contém , promovendo ganhos de 76% a 96%. Mostrou-se ainda que a variação do volume de dados não prejudica o desempenho do SB-index. Esta dissertação também investiga a seguinte questão: como a redundância de dados espaciais afeta um DWG? . Foram comparados os esquemas de DWG redundante e não-redundante. Observou-se que a redundância de dados espaciais acarreta prejuízos ao tempo de resposta das consultas e aos requisitos de armazenamento do DWG. Então, visando melhorar o desempenho do processamento de consultas, introduziu-se o SB-index no esquema de DWG redundante. Os ganhos de desempenho obtidos a partir desta ação variaram de 25% a 99%. Por fim, foi proposta uma melhoria sobre o SB-index a fim de lidar especificamente com a questão da redundância de dados espaciais. A partir desta melhoria, o ganho mínimo de desempenho tornou-se 80%

Modeling vague spatial data warehouses using the VSCube conceptual model

Although many real world phenomena are vague and characterized by having uncertain location or vague shape, existing spatial data warehouse models do not support spatial vagueness and then cannot properly represent these phenomena. In this paper, we propose the VSCube conceptual model to represent and manipulate shape vagueness in spatial data warehouses, allowing the analysis of business scores related to vague spatial data, and therefore improving the decision-making process. Our VSCube conceptual model is based on the cube metaphor and supports geometric shapes and the corresponding membership values, thus providing more expressiveness to represent vague spatial data. We also define vague spatial aggregation functions (e.g. vague spatial union) and vague spatial predicates to enable vague SOLAP queries (e.g. intersection range queries). Finally, we introduce the concept of vague SOLAP and its operations (e.g. drill-down and roll-up). We demonstrate the applicability of our model by describing an application concerning pest control in agriculture and by discussing the reuse of existing models in the VSCube conceptual model.FAPESP (grant #2011/23904-7)CAPESCNPqINEPFINE