scholarly journals Hierarchially Distributed Data Matrix Scheme for Big Data Processing

2019 ◽  
Vol 8 (12) ◽  
pp. 4161-4165
Keyword(s):  
Big Data ◽  
Data Stream ◽  
Data Matrix ◽  
Data Sets ◽  
Distributed Data ◽  
Data Dependency ◽  
The Past ◽  
Current State ◽  
Matrix Scheme ◽  
Consequent Improvement

MapReduce is a programming paradigm and an affiliated Design for processing and making substantial data sets. It operates on a large cluster of specialty machines and is extremely scalable Across the past years, MapReduce and Spark have been offered to facilitate the job of generating big data programs and utilization. However, the tasks in these structures are roughly described and packaged as executable jars externally any functionality being presented or represented. This means that extended roles are not natively composable and reusable for consequent improvement. Moreover, it also impedes the capacity for employing optimizations on the data stream of job orders and pipelines. In this article, we offer the Hierarchically Distributed Data Matrix (HDM), which is a practical, strongly-typed data description for writing composable big data appeals. Along with HDM, a runtime composition is presented to verify the performance of HDM applications on dispersed infrastructures. Based on the practical data dependency graph of HDM, various optimizations are employed to develop the appearance of performing HDM jobs. The empirical outcomes show that our optimizations can deliver increases of between 10% to 60% of the Job-Completion-Time for various types of applications when associated with the current state of the art, Apache Spark.

scholarly journals Review of Automatic Microexpression Recognition in the Past Decade

2021 ◽  
Vol 3 (2) ◽  
pp. 414-434
Author(s):  
Liangfei Zhang ◽  
Ognjen Arandjelović
Keyword(s):  
Facial Expressions ◽  
Emotional State ◽  
Small Range ◽  
Regular Expressions ◽  
Data Sets ◽  
Comprehensive Overview ◽  
Psychological States ◽  
Facial Movements ◽  
The Past ◽  
Current State

Facial expressions provide important information concerning one’s emotional state. Unlike regular facial expressions, microexpressions are particular kinds of small quick facial movements, which generally last only 0.05 to 0.2 s. They reflect individuals’ subjective emotions and real psychological states more accurately than regular expressions which can be acted. However, the small range and short duration of facial movements when microexpressions happen make them challenging to recognize both by humans and machines alike. In the past decade, automatic microexpression recognition has attracted the attention of researchers in psychology, computer science, and security, amongst others. In addition, a number of specialized microexpression databases have been collected and made publicly available. The purpose of this article is to provide a comprehensive overview of the current state of the art automatic facial microexpression recognition work. To be specific, the features and learning methods used in automatic microexpression recognition, the existing microexpression data sets, the major outstanding challenges, and possible future development directions are all discussed.

scholarly journals Study of Big Data in Healthcare: Analysis, Management, Techniques and Applications

2021 ◽  
pp. 61-66
Author(s):  
Ragini Munnaprasad Gupta
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Data Sets ◽  
Healthcare Industry ◽  
Medical Field ◽  
The Past ◽  
Management Techniques ◽  
Future Direction ◽  
Different Sources ◽  
The Internet Of Things

Clinical, suppliers-providers of healthcare, policymakers, and patients are encounter exciting opportunities in big data sets. Big data capability that emerged in the past decades. Due to the rapid growth of communication in the healthcare industry big data play an important role. I have explained how healthcare uses big data analytics due to its great potential that. In the healthcare industry, different sources of big data include clinic records, medical records of patients, consequences of medical assessments, and devices that are a piece of the internet of things. Firstly, I am beginning with the core concept of big data and healthcare. Secondly, discuss the Process of Big data Analysis and Management. Thirdly talk about the techniques of big data that uses in the medical field. Finally, the Application of the healthcare industry and future direction are discussed.

scholarly journals Using big data to map the network organization of the brain

2014 ◽  
Vol 37 (1) ◽  
pp. 101-102 ◽  
Author(s):  
James E. Swain ◽  
Chandra Sripada ◽  
John D. Swain
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Network Theory ◽  
Social Network Theory ◽  
Data Sets ◽  
Brain Organization ◽  
Neuronal Ensembles ◽  
The Past ◽  
The Social ◽  
The Brain

AbstractThe past few years have shown a major rise in network analysis of “big data” sets in the social sciences, revealing non-obvious patterns of organization and dynamic principles. We speculate that the dependency dimension – individuality versus sociality – might offer important insights into the dynamics of neurons and neuronal ensembles. Connectomic neural analyses, informed by social network theory, may be helpful in understanding underlying fundamental principles of brain organization.

scholarly journals Implications of Big Data for cell biology

2015 ◽  
Vol 26 (14) ◽  
pp. 2575-2578 ◽  
Author(s):  
Kara Dolinski ◽  
Olga G. Troyanskaya
Keyword(s):  
Biological Sciences ◽  
Big Data ◽  
Systems Biology ◽  
Cell Biology ◽  
Heterogeneous Data ◽  
Data Sets ◽  
Individual Data ◽  
High Throughput Data ◽  
The Past ◽  
The Many

“Big Data” has surpassed “systems biology” and “omics” as the hottest buzzword in the biological sciences, but is there any substance behind the hype? Certainly, we have learned about various aspects of cell and molecular biology from the many individual high-throughput data sets that have been published in the past 15–20 years. These data, although useful as individual data sets, can provide much more knowledge when interrogated with Big Data approaches, such as applying integrative methods that leverage the heterogeneous data compendia in their entirety. Here we discuss the benefits and challenges of such Big Data approaches in biology and how cell and molecular biologists can best take advantage of them.

Artistic Challenges within Control Societies: Big Data and Democratic Resistance

MedienJournal ◽  
2017 ◽  
Vol 38 (4) ◽  
pp. 50-61 ◽  
Author(s):  
Jan Jagodzinski
Keyword(s):  
Big Data ◽  
Gilles Deleuze ◽  
Data Archive ◽  
Current State ◽  
The Future ◽  
The Way

This paper will first briefly map out the shift from disciplinary to control societies (what I call designer capitalism, the idea of control comes from Gilles Deleuze) in relation to surveillance and mediation of life through screen cultures. The paper then shifts to the issues of digitalization in relation to big data that have the danger of continuing to close off life as zoë, that is life that is creative rather than captured via attention technologies through marketing techniques and surveillance. The last part of this paper then develops the way artists are able to resist the big data archive by turning the data in on itself to offer viewers and participants a glimpse of the current state of manipulating desire and maintaining copy right in order to keep the future closed rather than being potentially open.

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