Construction and Implementation of Big Data Framework for Crop Germplasm Resources

2019 ◽  
Author(s):  
Furong Jing ◽  
Yongsheng Cao ◽  
Wei Fang ◽  
Yanqing Chen
Keyword(s):  
Big Data ◽  
Germplasm Resources ◽  
Data Framework ◽  
Crop Germplasm

scholarly journals Development and Evaluation of a Big Data Framework for Performance Management in Mobile Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 226380-226396
Author(s):  
Diana Martinez-Mosquera ◽  
Rosa Navarrete ◽  
Sergio Lujan-Mora
Keyword(s):  
Big Data ◽  
Performance Management ◽  
Mobile Networks ◽  
Data Framework

scholarly journals Precision agriculture and geospatial techniques for sustainable disease control

2021 ◽  
Author(s):  
Daniel P. Roberts ◽  
Nicholas M. Short ◽  
James Sill ◽  
Dilip K. Lakshman ◽  
Xiaojia Hu ◽  
...  
Keyword(s):  
Big Data ◽  
Disease Control ◽  
Crop Production ◽  
Plant Disease ◽  
Production Systems ◽  
Cropping Systems ◽  
Data Driven ◽  
Agricultural Community ◽  
Plant Disease Control ◽  
Crop Germplasm

AbstractThe agricultural community is confronted with dual challenges; increasing production of nutritionally dense food and decreasing the impacts of these crop production systems on the land, water, and climate. Control of plant pathogens will figure prominently in meeting these challenges as plant diseases cause significant yield and economic losses to crops responsible for feeding a large portion of the world population. New approaches and technologies to enhance sustainability of crop production systems and, importantly, plant disease control need to be developed and adopted. By leveraging advanced geoinformatic techniques, advances in computing and sensing infrastructure (e.g., cloud-based, big data-driven applications) will aid in the monitoring and management of pesticides and biologicals, such as cover crops and beneficial microbes, to reduce the impact of plant disease control and cropping systems on the environment. This includes geospatial tools being developed to aid the farmer in managing cropping system and disease management strategies that are more sustainable but increasingly complex. Geoinformatics and cloud-based, big data-driven applications are also being enlisted to speed up crop germplasm improvement; crop germplasm that has enhanced tolerance to pathogens and abiotic stress and is in tune with different cropping systems and environmental conditions is needed. Finally, advanced geoinformatic techniques and advances in computing infrastructure allow a more collaborative framework amongst scientists, policymakers, and the agricultural community to speed the development, transfer, and adoption of these sustainable technologies.

scholarly journals Predictors of outpatients’ no-show: big data analytics using apache spark

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tahani Daghistani ◽  
Huda AlGhamdi ◽  
Riyad Alshammari ◽  
Raed H. AlHazme
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Negative Impact ◽  
Big Data Analytics ◽  
Quality Of Healthcare ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Healthcare Organizations ◽  
Data Framework ◽  
Learning Techniques

AbstractOutpatients who fail to attend their appointments have a negative impact on the healthcare outcome. Thus, healthcare organizations facing new opportunities, one of them is to improve the quality of healthcare. The main challenges is predictive analysis using techniques capable of handle the huge data generated. We propose a big data framework for identifying subject outpatients’ no-show via feature engineering and machine learning (MLlib) in the Spark platform. This study evaluates the performance of five machine learning techniques, using the (2,011,813‬) outpatients’ visits data. Conducting several experiments and using different validation methods, the Gradient Boosting (GB) performed best, resulting in an increase of accuracy and ROC to 79% and 81%, respectively. In addition, we showed that exploring and evaluating the performance of the machine learning models using various evaluation methods is critical as the accuracy of prediction can significantly differ. The aim of this paper is exploring factors that affect no-show rate and can be used to formulate predictions using big data machine learning techniques.

scholarly journals EMPOWERING, a Smart Big Data Framework for Sustainable Electricity Suppliers

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 71132-71142
Author(s):  
Gerard Mor ◽  
Jordi Vilaplana ◽  
Stoyan Danov ◽  
Jordi Cipriano ◽  
Francesc Solsona ◽  
...  
Keyword(s):  
Big Data ◽  
Data Framework ◽  
Sustainable Electricity

scholarly journals SIDELOADING – INGESTION OF LARGE POINT CLOUDS INTO THE APACHE SPARK BIG DATA ENGINE

2016 ◽  
Vol XLI-B2 ◽  
pp. 343-348
Author(s):  
J. Boehm ◽  
K. Liu ◽  
C. Alis
Keyword(s):  
Big Data ◽  
Point Cloud ◽  
Point Clouds ◽  
Geospatial Data ◽  
Apache Spark ◽  
Cloud Data ◽  
Binary File ◽  
Data Framework ◽  
File Formats ◽  
Data Ingestion

In the geospatial domain we have now reached the point where data volumes we handle have clearly grown beyond the capacity of most desktop computers. This is particularly true in the area of point cloud processing. It is therefore naturally lucrative to explore established big data frameworks for big geospatial data. The very first hurdle is the import of geospatial data into big data frameworks, commonly referred to as data ingestion. Geospatial data is typically encoded in specialised binary file formats, which are not naturally supported by the existing big data frameworks. Instead such file formats are supported by software libraries that are restricted to single CPU execution. We present an approach that allows the use of existing point cloud file format libraries on the Apache Spark big data framework. We demonstrate the ingestion of large volumes of point cloud data into a compute cluster. The approach uses a map function to distribute the data ingestion across the nodes of a cluster. We test the capabilities of the proposed method to load billions of points into a commodity hardware compute cluster and we discuss the implications on scalability and performance. The performance is benchmarked against an existing native Apache Spark data import implementation.

The digital traveller: implications for data ethics and data governance in tourism and hospitality

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anca C. Yallop ◽  
Oana A. Gică ◽  
Ovidiu I. Moisescu ◽  
Monica M. Coroș ◽  
Hugues Séraphin
Keyword(s):  
Big Data ◽  
The Body ◽  
Data Governance ◽  
Privacy And Security ◽  
Content Type ◽  
Data Ethics ◽  
Data Framework ◽  
Social License ◽  
Governance Practices ◽  
Tourism And Hospitality

Purpose Big data and analytics are being increasingly used by tourism and hospitality organisations (THOs) to provide insights and to inform critical business decisions. Particularly in times of crisis and uncertainty data analytics supports THOs to acquire the knowledge needed to ensure business continuity and the rebuild of tourism and hospitality sectors. Despite being recognised as an important source of value creation, big data and digital technologies raise ethical, privacy and security concerns. This paper aims to suggest a framework for ethical data management in tourism and hospitality designed to facilitate and promote effective data governance practices. Design/methodology/approach The paper adopts an organisational and stakeholder perspective through a scoping review of the literature to provide an overview of an under-researched topic and to guide further research in data ethics and data governance. Findings The proposed framework integrates an ethical-based approach which expands beyond mere compliance with privacy and protection laws, to include other critical facets regarding privacy and ethics, an equitable exchange of travellers’ data and THOs ability to demonstrate a social license to operate by building trusting relationships with stakeholders. Originality/value This study represents one of the first studies to consider the development of an ethical data framework for THOs, as a platform for further refinements in future conceptual and empirical research of such data governance frameworks. It contributes to the advancement of the body of knowledge in data ethics and data governance in tourism and hospitality and other industries and it is also beneficial to practitioners, as organisations may use it as a guide in data governance practices.

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