Big Data Analytics in Weather Forecasting: A Systematic Review

2021 ◽  
Author(s):  
Marzieh Fathi ◽  
Mostafa Haghi Kashani ◽  
Seyed Mahdi Jameii ◽  
Ebrahim Mahdipour
Keyword(s):  
Systematic Review ◽  
Big Data ◽  
Data Analytics ◽  
Weather Forecasting ◽  
Big Data Analytics

Managing supply chain resources with Big Data Analytics: a systematic review

2017 ◽  
Vol 21 (3) ◽  
pp. 177-200 ◽  
Author(s):  
Marcelo Werneck Barbosa ◽  
Alberto de la Calle Vicente ◽  
Marcelo Bronzo Ladeira ◽  
Marcos Paulo Valadares de Oliveira
Keyword(s):  
Systematic Review ◽  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics

scholarly journals Big Data analytics for Advanced Viticulture

2021 ◽  
Vol 22 (3) ◽  
pp. 303-312
Author(s):  
Jitali Patel ◽  
Ruhi Patel ◽  
Saumya Shah ◽  
Jigna Ashish Patel
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Weather Forecasting ◽  
Big Data Analytics ◽  
Research Area ◽  
Disease Detection ◽  
Agricultural Practice ◽  
Main Research ◽  
Grape Leaf ◽  
The Right

Big data analytics involve systematic approach to find hidden patterns to help the organization grow from large volume and variety of data. In recent years big data analytics is widely used in the agricultural domain to improve yield. Viticulture (the cultivation of grapes) is one of the most lucrative farming in India. It is a subdivision of horticulture and is the study of wine growing. The demand for Indian Wine is increasing at about 27% each year since the 21st century and thus more and more ways are being developed to improve the quality and quantity of the wine products. In this paper, we focus on a specific agricultural practice as viticulture. Weather forecasting and disease detection are the two main research areas in precision viticulture. Leaf disease detection as a part of plant pathology is the key research area in this paper. It can be applied on vineyards of India where farmers are bereft of the latest technologies. Proposed system architecture comprises four modules: Data collection, data preprocessing, classification and visualization. Database module involve grape leaf dataset, consists of healthy images combined with disease leaves such as Black measles, Black rot, and Leaf blight. Models have been implemented on Apache Hadoop using map reduce programming framework. It apply feature extraction to extract various features of the live images and classification algorithm with reduced computational complexity. Gray Level Co-occurrence Matrix (GLCM) followed by K-Nearest Neighborhood (KNN) algorithm. System also recommends the necessary steps and remedies that the viticulturists can take to assure that the grapes can be salvaged at the right time and in the right manner based on classification results. Overall system will help Indian viticulturists to improve the harvesting process. Accuracy of the model is 72% and it can be increased as a future work by including deep learning with time series grape leaf images.  

scholarly journals Big Data Analytics and Firm Performance: A Systematic Review

Information ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 226 ◽  
Author(s):  
Parisa Maroufkhani ◽  
Ralf Wagner ◽  
Wan Khairuzzaman Wan Ismail ◽  
Mas Bambang Baroto ◽  
Mohammad Nourani
Keyword(s):  
Systematic Review ◽  
Big Data ◽  
Firm Performance ◽  
Empirical Research ◽  
Data Analytics ◽  
Web Of Science ◽  
Big Data Analytics ◽  
Future Research ◽  
Science Index ◽  
The Web

The literature on big data analytics and firm performance is still fragmented and lacking in attempts to integrate the current studies’ results. This study aims to provide a systematic review of contributions related to big data analytics and firm performance. The authors assess papers listed in the Web of Science index. This study identifies the factors that may influence the adoption of big data analytics in various parts of an organization and categorizes the diverse types of performance that big data analytics can address. Directions for future research are developed from the results. This systematic review proposes to create avenues for both conceptual and empirical research streams by emphasizing the importance of big data analytics in improving firm performance. In addition, this review offers both scholars and practitioners an increased understanding of the link between big data analytics and firm performance.

scholarly journals Big data analytics in E-commerce: a systematic review and agenda for future research

2016 ◽  
Vol 26 (2) ◽  
pp. 173-194 ◽  
Author(s):  
Shahriar Akter ◽  
Samuel Fosso Wamba
Keyword(s):  
Systematic Review ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research

The emerging big data analytics and IoT in supply chain management: a systematic review

2018 ◽  
Vol 25 (2) ◽  
pp. 141-156 ◽  
Author(s):  
Arun Aryal ◽  
Ying Liao ◽  
Prasnna Nattuthurai ◽  
Bo Li
Keyword(s):  
Systematic Review ◽  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Research Approach ◽  
Network Systems ◽  
Content Type ◽  
And Performance ◽  
Over Time

Purpose The purpose of this study is to provide insights into the way in which understanding and implementation of disruptive technology, specifically big data analytics and the Internet of Things (IoT), have changed over time. The study also examines the ways in which research in supply chain and related fields differ when responding to and managing disruptive change. Design/methodology/approach This study follows a four-step systematic review process, consisting of literature collection, descriptive analysis, category selection and material evaluation. For the last stage of evaluating relevant issues and trends in the literature, the latent semantic analysis method was adopted using Leximancer, which allows more rapid, reliable and consistent content analysis. Findings The empirical analysis identified key research trends in big data analytics and IoT divided over two time-periods, in which research demonstrated steady growth by 2015 and the rapid growth was shown afterwards. The key finding of this review is that the main interest in recent big data is toward overlapping customer service, support and supply chain network, systems and performance. Major research themes in IoT moved from general supply chain and business information management to more specific context including supply chain design, model and performance. Originality/value In addition to providing more awareness of this research approach, the authors seek to identify important trends in disruptive technologies research over time.

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