Applying big data to planning food production in conditions of uncertainty

2021 ◽  
Vol 14 (2) ◽  
pp. 13-20
Author(s):  
Yaroslav Ivano ◽  
Petr Asalhanov ◽  
Nadezhda Bendik
Keyword(s):  
Big Data ◽  
Food Production ◽  
Food Products ◽  
Data Sources ◽  
Conceptual Scheme ◽  
Agricultural Producers ◽  
Irkutsk Region ◽  
Big Data Technology

The article considers the use of Big Data technology for planning food production in conditions of uncertainty. The use of a large amount of diverse information allows us to solve various classes of problems of forecasting and planning the production and sale of food products. The conceptual scheme of using of big data technology by agricultural producers is given on the example of the Irkutsk region and groups of solved extreme problems with examples are considered. Data sources and users are described. The current Big Data platforms are presented.

scholarly journals SISTEM PENGELOLAAN KEARSIPAN Google MELALUI BIG DATA

2020 ◽  
Vol 15 (2) ◽  
pp. 21-20
Author(s):  
Aldha Shafrielda Sihab ◽  
Anugerah Pagiyan Nurfajar
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Secondary Data ◽  
Data Sources ◽  
The World ◽  
Library Studies ◽  
Secondary Data Sources ◽  
Learning Machine ◽  
Crucial Part ◽  
Big Data Technology

Big data is an newest trend that embraces the world of technology and business. It's a data collection so large and complex that it no longer allows it to be managed with traditional software tools. One of the world's companies moving in big data technology is Google.Inc. This company maintains and districts data for various purposes, so its presence is urgently needed. As the development of data in Google has been a crucial part of the digital age, resulting in several breakthroughs. First Google data can hold files effectively as well as easily be accessed by people. Both big data can easily call back specifically through Google's learning machine. The study was conducted using qualitative diskirtive methods with secondary data sources through library studies.

A Specification Framework for Big Data Initiatives

2015 ◽  
pp. 257-274 ◽  
Author(s):  
Anh D. Ta ◽  
Marcus Tanque ◽  
Montressa Washington
Keyword(s):  
Big Data ◽  
Return On Investment ◽  
Data Sources ◽  
Top Down ◽  
Raw Data ◽  
Avant Garde ◽  
High Level ◽  
Data Requirements ◽  
Big Data Technology ◽  
Three Components

Given the emergence of big data technology and its rising popularity, it is important to ensure that the use of this avant-garde technology directly addresses the enterprise goals which are required to maximize the Return-On-Investment (ROI). This chapter aims to address a specification framework for the process of transforming enterprise data into wisdom or actionable information through the use of big data technology. The framework is based on proven methodologies, which consist of three components: Specify, Design, and Refine. The recommended framework provides a systematic, top-down process to extrapolate big data requirements from high-level technical and enterprise goals. The framework also provides a process for managing the quality and relationship between raw data sources and big data products.

A Specification Framework for Big Data Initiatives

Web Services ◽  
2019 ◽  
pp. 639-656
Author(s):  
Anh D. Ta ◽  
Marcus Tanque ◽  
Montressa Washington
Keyword(s):  
Big Data ◽  
Return On Investment ◽  
Data Sources ◽  
Top Down ◽  
Raw Data ◽  
Avant Garde ◽  
High Level ◽  
Data Requirements ◽  
Big Data Technology ◽  
Three Components

Given the emergence of big data technology and its rising popularity, it is important to ensure that the use of this avant-garde technology directly addresses the enterprise goals which are required to maximize the Return-On-Investment (ROI). This chapter aims to address a specification framework for the process of transforming enterprise data into wisdom or actionable information through the use of big data technology. The framework is based on proven methodologies, which consist of three components: Specify, Design, and Refine. The recommended framework provides a systematic, top-down process to extrapolate big data requirements from high-level technical and enterprise goals. The framework also provides a process for managing the quality and relationship between raw data sources and big data products.

Enhancement of food production quality: the truffle case

2020 ◽  
pp. 587-611 ◽  
Author(s):  
Elena Viganò ◽  
Federico Gori ◽  
Antonella Amicucci
Keyword(s):  
Food Production ◽  
Food Products ◽  
Production Quality ◽  
Regulatory Framework ◽  
Marketing Literature

The central role of quality agri-food production in the promotion of a given territory is actually widely recognized by both the economic and marketing literature and the stakeholders involved in the enhancement process of rural systems. On this basis, this work analyzes one of the finest Italian agri-food products: the truffle. This work tries to point out the main problems characterizing the current regulatory framework, the trade and the production of the Italian truffle sector, emphasizing their causes, consequences and possible solutions.

A New Rockburst Experiment Data Compression Storage Algorithm based on Big Data Technology

2019 ◽  
pp. 561-573
Author(s):  
Yu Zhang ◽  
Yan-Ge Wang ◽  
Yan-Ping Bai ◽  
Yong-Zhen Li ◽  
Zhao-Yong Lv ◽  
...  
Keyword(s):  
Big Data ◽  
Data Compression ◽  
Experiment Data ◽  
Big Data Technology

Big Data Driven Clinical Informatics & Surveillance (BDD_CIS) – A Multimodal Database Focused Clinical, Community, and Multi-Omics Surveillance Plan for COVID-19: A study Protocol (Preprint)

2020 ◽  
Author(s):  
Bankole Olatosi ◽  
Jiajia Zhang ◽  
Sharon Weissman ◽  
Zhenlong Li ◽  
Jianjun Hu ◽  
...  
Keyword(s):  
Big Data ◽  
South Carolina ◽  
Data Science ◽  
Age Groups ◽  
The Elderly ◽  
The United States ◽  
Data Sources ◽  
Patient Registries ◽  
Multiple Partner ◽  
Multimodal Data

BACKGROUND The Coronavirus Disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) remains a serious global pandemic. Currently, all age groups are at risk for infection but the elderly and persons with underlying health conditions are at higher risk of severe complications. In the United States (US), the pandemic curve is rapidly changing with over 6,786,352 cases and 199,024 deaths reported. South Carolina (SC) as of 9/21/2020 reported 138,624 cases and 3,212 deaths across the state. OBJECTIVE The growing availability of COVID-19 data provides a basis for deploying Big Data science to leverage multitudinal and multimodal data sources for incremental learning. Doing this requires the acquisition and collation of multiple data sources at the individual and county level. METHODS The population for the comprehensive database comes from statewide COVID-19 testing surveillance data (March 2020- till present) for all SC COVID-19 patients (N≈140,000). This project will 1) connect multiple partner data sources for prediction and intelligence gathering, 2) build a REDCap database that links de-identified multitudinal and multimodal data sources useful for machine learning and deep learning algorithms to enable further studies. Additional data will include hospital based COVID-19 patient registries, Health Sciences South Carolina (HSSC) data, data from the office of Revenue and Fiscal Affairs (RFA), and Area Health Resource Files (AHRF). RESULTS The project was funded as of June 2020 by the National Institutes for Health. CONCLUSIONS The development of such a linked and integrated database will allow for the identification of important predictors of short- and long-term clinical outcomes for SC COVID-19 patients using data science.

scholarly journals Validity of the “Big Data Tendency in Education” Scale as a Tool Helping to Reach Inclusive Social Development

2020 ◽  
Vol 12 (13) ◽  
pp. 5470 ◽  
Author(s):  
Antonio Matas-Terrón ◽  
Juan José Leiva-Olivencia ◽  
Pablo Daniel Franco-Caballero ◽  
Francisco José García-Aguilera
Keyword(s):  
Big Data ◽  
Inclusive Education ◽  
Sustainable Development Goals ◽  
Sustainable Society ◽  
Education Students ◽  
The Sustainable Development ◽  
Research Goal ◽  
Development Goals ◽  
Nonprobability Sample ◽  
Big Data Technology

Big Data technology can be a great resource for achieving the Sustainable Development Goals in a fair and inclusive manner; however, only recently have we begun to analyse its impact on education. This research goal was to analyse the psychometric characteristics of a scale to assess opinions that educators in training have about Big Data besides their related emotions. This is important, as it will be the educators of the future who will have to manage with Big Data at school. A nonprobability sample of 337 education students from Peru and Spain was counted. Internal consistency, as well as validity, were analysed through exploratory and confirmatory factorial analysis. The results show good psychometric values, highlighting as relevant a latent structure of six factors that includes emotional and cognitive dimensions. As a result, the profile defining the participants in relation to Big Data was identified. Finally, the implications of the Big Data for Inclusive Education in a sustainable society are discussed.

scholarly journals Tuberculosis in Epidemic Prevention and Control Based on Big Data Technology

2021 ◽  
Vol 1881 (4) ◽  
pp. 042036
Author(s):  
Jiao Tan ◽  
Yonghong Ma ◽  
Ke Men ◽  
Jing Lei ◽  
Hairui Zhang ◽  
...  
Keyword(s):  
Big Data ◽  
Prevention And Control ◽  
And Control ◽  
Big Data Technology
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