The similarity-aware relational division database operator with case studies in agriculture and genetics

Gonzaga, André dos Santos; Cordeiro, Robson L. F.

doi:10.1016/j.is.2019.01.002

Cited by 4 publications

(4 citation statements)

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“…Em [Gonzaga and Cordeiro 2019] é proposto um operador de divisão por similaridade, variando-se a característica dos dados, pois estes não seguem uma relac ¸ão de igualdade/precedência entre si, mas estão dispostos em espac ¸os de distância. O emprego de Divisão Relacional sobre dados em espac ¸os de distância e espac ¸os métricos também é encontrado em [Vasconcelos et al 2018], onde a operac ¸ão é implementada no SGBD comercial Oracle, juntamente com uma extensão da linguagem SQL, para possibilitar a execuc ¸ão da Divisão Relacional por similaridade.…”

Section: Referencial Teóricounclassified

“…Este conjunto de operadores de consulta, como originalmente definidos [Codd 1972], inclui o operador de Divisão Relacional. A Divisão Relacional é uma operac ¸ão binária que permite responder consultas do tipo "para todos os/as" ou então "nenhum dos/das" [Vasconcelos et al 2018, Gonzaga andCordeiro 2019] seja uma relac ¸ão Pessoa contendo o nome e os idiomas que uma pessoa domina. A consulta "Quais pessoas falam, conjuntamente, inglês e espanhol?"…”

Section: Introduc ¸ãOunclassified

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Planos físicos de consulta para Divisão Relacional

Lima,

Santos,

Bedo

et al. 2023

Anais Do XXXVIII Simpósio Brasileiro De Banco De Dados (SBBD 2023)

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O operador de Divisão Relacional não possui um comando específico na linguagem SQL. Como consequência, uma mesma consulta pode ser escrita de diversas maneiras, mas não necessariamente com o mesmo desempenho de execução. A partir das formas de representação da Divisão Relacional em SQL, este trabalho investiga o uso de duas estratégias para otimização: uso de índice composto e uso do operador Hash Join diretamente no plano físico de consulta, durante o processo de junção de tuplas. O experimentos foram realizados com o SGBD PostregeSQL e os resultados obtidos mostram que as estratégias propostas melhoram o desempenho em 47% em relação ao obtido pelo planejador de consultas do banco de dados.

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Section: Referencial Teóricounclassified

Section: Introduc ¸ãOunclassified

Planos físicos de consulta para Divisão Relacional

Lima,

Santos,

Bedo

et al. 2023

Anais Do XXXVIII Simpósio Brasileiro De Banco De Dados (SBBD 2023)

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“…Support vector machine (SVM) is a powerful machine learning algorithm [33]. SVM can solve classification and regression problems at the same time.…”

Section: Svr Machine Model Creationmentioning

confidence: 99%

The Prediction of Carbon Emission Information in Yangtze River Economic Zone by Deep Learning

Huafang

Cheng

2021

Land

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This study aimed to respond to the national “carbon peak” mid-and long-term policy plan, comprehensively promote energy conservation and emission reduction, and accurately manage and predict carbon emissions. Firstly, the proposed method analyzes the Yangtze River Economic Belt as well as its “carbon peak” and carbon emissions. Secondly, a support vector regression (SVR) machine prediction model is proposed for the carbon emission information prediction of the Yangtze River Economic Zone. This experiment uses a long short-term memory neural network (LSTM) to train the model and realize the experiment’s prediction of carbon emissions. Finally, this study obtained the fitting results of the prediction model and the training model, as well as the prediction results of the prediction model. Information indicators such as the scale of industry investment, labor efficiency output, and carbon emission intensity that affect carbon emissions in the “Yangtze River Economic Belt” basin can be used to accurately predict the carbon emissions information under this model. Therefore, the experiment shows that the SVR model for solving complex nonlinear problems can achieve a relatively excellent prediction effect under the training of LSTM. The deep learning model adopted herein realized the accurate prediction of carbon emission information in the Yangtze River Economic Zone and expanded the application space of deep learning. It provides a reference for the model in related fields of carbon emission information prediction, which has certain reference significance.

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“…Realizing the strategy of rural revitalization requires improving the output efficiency and output quality of agricultural factors, continuously innovating agricultural technology, strengthening the leadership of talents, and improving the efficiency of agricultural resource allocation, which requires further promoting the modernization of agriculture and rural areas [5][6]. The Strategic Plan for Rural Revitalization (2018-2022) clearly points out that the powerful hand that promotes urban-rural integration and high-quality development is to accelerate the modernization of agriculture and rural areas, and the report of the 19th National Congress of the CPC also explicitly proposes to accelerate the modernization of agriculture, which is a re-examination of the development of China's "Three Rural Areas" in the new period [7].…”

Section: Introductionmentioning

confidence: 99%

Integration of Rural Revitalization Strategy and Modernized Agricultural Governance Based on Intelligent Big Data Analysis

Yin

2023

Applied Mathematics and Nonlinear Sciences

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This paper explores the integration mechanism of rural revitalization strategy and agricultural modernization governance. The relationship between the two is complementary and closely linked, and rural revitalization can bring new impetus to agricultural modernization. The logistic development model is proposed to carry out a stage-by-stage study on the integration development of agricultural modernization governance and rural revitalization, and the subsystem orderliness and composite system synergy models are established by using the geometric mean method and the linear weighting method, respectively, and the degree of integration development is judged by measuring the integration status. The model constructed in this paper, as well as the governance level of rural revitalization strategies and agricultural modernization in place B, are measured through empirical research. The experiment shows that the two variables in this paper and their logarithmic means are 100, and the means are 0.3436, 0.3463, -1.3217, and -0.9874, respectively. In the stability test, the number of scatters in the region is 92, and the truthfulness and predictive intervals are in [-1,1], which makes the model stability and credibility good. After the implementation of the rural revitalization strategy for 11 years, the level of the comprehensive development index of agricultural modernization in the rural areas of place B in 2020 is 0.3189, 0.2473, 0.2434, 0.2645, respectively, which is in the stage of steady growth.

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The similarity-aware relational division database operator with case studies in agriculture and genetics

Cited by 4 publications

References 35 publications

Planos físicos de consulta para Divisão Relacional

Planos físicos de consulta para Divisão Relacional

The Prediction of Carbon Emission Information in Yangtze River Economic Zone by Deep Learning

Integration of Rural Revitalization Strategy and Modernized Agricultural Governance Based on Intelligent Big Data Analysis

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