Towards a Common Data Framework for Analytical Environments

2016 ◽  
pp. 271-287
Author(s):  
Maribel Yasmina Santos ◽  
Jorge Oliveira e Sá ◽  
Carina Andrade
Keyword(s):  
Data Framework

A Walk through Various Data Hiding Techniques

2017 ◽  
Vol 7 (8) ◽  
pp. 52
Author(s):  
Manoj Kumar ◽  
Rohit Tanwar
Keyword(s):  
Data Hiding ◽  
Data Framework ◽  
Web Information ◽  
Fundamental Part ◽  
Secure Information ◽  
Numerous Data ◽  
Security Information ◽  
Security Procedures

In the computerized age as a result of the broad utilization of web, information covering up in advanced symbolism assumes a fundamental part to guarantee copyright assurance and power from pernicious assaults. Today the exponential development in web clients request secure information correspondence, for that it is required to send the information as encoded or shrouded shape. Numerous data framework security procedures are accessible.Information transmission needs security. Information covering up can be accomplished through numerous techniques. Distinctive information concealing procedures are talked about in this paper which incorporates watermarking, steganography, fingerprinting, cryptography and advanced mark.

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 A database framework for rapid screening of structure-function relationships in PFAS chemistry

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
An Su ◽  
Krishna Rajan
Keyword(s):  
Molecular Structure ◽  
Structure Function ◽  
Classification Criteria ◽  
Rapid Screening ◽  
High Dimensional ◽  
Visualization Tool ◽  
Classification Schemes ◽  
3 Dimensional ◽  
Data Framework ◽  
Fundamental Physical

AbstractThis paper describes a database framework that enables one to rapidly explore systematics in structure-function relationships associated with new and emerging PFAS chemistries. The data framework maps high dimensional information associated with the SMILES approach of encoding molecular structure with functionality data including bioactivity and physicochemical property. This ‘PFAS-Map’ is a 3-dimensional unsupervised visualization tool that can automatically classify new PFAS chemistries based on current PFAS classification criteria. We provide examples on how the PFAS-Map can be utilized, including the prediction and estimation of yet unmeasured fundamental physical properties of PFAS chemistries, uncovering hierarchical characteristics in existing classification schemes, and the fusion of data from diverse sources.

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.

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