Fifty-Six Big Data V’s Characteristics and Proposed Strategies to Overcome Security and Privacy Challenges (BD2)

The amount of data that is traveling across the internet today, including very large and complex set of raw facts that are not only large, but also, complex, noisy, heterogeneous, and longitudinal data as well. Companies, institutions, healthcare system, mobile application capturing devices and sensors, traffic management, banking, retail, education etc., use piles of data which are further used for creating reports in order to ensure continuity regarding the services that they have to offer. Recently, Big data is one of the most important topics in IT industry. Managing Big data needs new techniques because traditional security and privacy mechanisms are inadequate and unable to manage complex distributed computing for different types of data. New types of data have different and new challenges also. A lot of researches treat with big data challenges starting from Doug Laney’s landmark paper, during the previous two decades;the big challenge is how to operate a huge volume of data that has to be securely delivered through the internet and reach its destination intact. The present paper highlights important concepts of Fifty-six Big Data V’s characteristics. This paper also highlights the security and privacy Challenges that Big Data faces and solving this problem by proposed technological solutions that help us avoiding these challenging problems.

State Evaluation Based on Feature Identification of Measurement Data:for Resilient Power System

Resilient power systems urgently need real-time evaluation of their operational states.By mining the characteristics of the grid operational data and mapping them to the operational state,this paper proposes a method to evaluate the real-time state and the evolution direction of power systems.First,the state evaluation matrix is constructed using nodal voltages.Then,from the data-driven perspective,the grid state is embodied in the operational data change.Furthermore,four indicators are proposed to characterize the power grid statefrom inherent physical and operating characteristics perspectives.Finally,through the simulations of a real power grid in China,it is shown that the method proposed in this paper can adequately characterize the power grid state,and is robust against bad data.

Prediction of severe preeclampsia in machine learning

This study aimed to find out the blood data characteristics of patients and explore the correlation between severe preeclampsia and blood index value.Provide assistance for the early attention direction of severe preeclampsia diagnosis and treatment.19,653 pregnant women presenting to the West China Second University Hospital,Sichuan University from January 2017 to April 2019.After screening,a total of 248 patients,124 severe preeclampsia cases,and 124 controls were selected for this study.Forty-three blood examination variables were obtained from routine blood work,hepatic,renal and coagulation function examination.Light gradient boosting machine(light GBM),decision tree and random forest were used for date diving.We randomly divided 35%of the original data as a testing set to conduct internal validation of the performance of the prediction model.The area under receiver operating characteristic curve(AUC)was used as the main score to compare the three methods.Finally,a binary classification light GBM model based on aspartate aminotransferase,direct bilirubin and activated partial thromboplastin time ratio can predict severe preeclampsia with sensitivity of 88.37%,specificity of 77.27%,AUC of 89.74%and positive predictive value of 65.96%.We believe relevant quantifiable indicators can establish an effective prediction model,which can provide guidance for early detection and prevention of severe preeclampsia.