scholarly journals Big Data Optimization Techniques: A Survey

2018 ◽  
Vol 10 (4) ◽  
pp. 41-48 ◽  
Author(s):  
Chandrima Roy ◽  
◽  
Siddharth Swarup Rautaray ◽  
Manjusha Pandey
Keyword(s):  
Big Data ◽  
Optimization Techniques ◽  
Data Optimization

scholarly journals Data Optimization Techniques using Bloom Filter in Big Data

2016 ◽  
Vol 142 (3) ◽  
pp. 23-27
Author(s):  
Ritu Jain ◽  
Mukesh Rawat ◽  
Swati Jain
Keyword(s):  
Big Data ◽  
Bloom Filter ◽  
Optimization Techniques ◽  
Data Optimization

An Artificial Bee Colony-Guided Approach for Electro-Encephalography Signal Decomposition-Based Big Data Optimization

2020 ◽  
Vol 19 (02) ◽  
pp. 561-600
Author(s):  
Selcuk Aslan
Keyword(s):  
Big Data ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Experimental Studies ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Abc Algorithm ◽  
Bee Colony ◽  
De Algorithm ◽  
Data Optimization

The digital age has added a new term to the literature of information and computer sciences called as the big data in recent years. Because of the individual properties of the newly introduced term, the definitions of the data-intensive problems including optimization problems have been substantially changed and investigations about the solving capabilities of the existing techniques and then developing their specialized variants for big data optimizations have become important research topic. Artificial Bee Colony (ABC) algorithm inspired by the clever foraging characteristics of the real honey bees is one of the most successful swarm intelligence-based metaheuristics. In this study, a new ABC algorithm-based technique that is named source-linked ABC (slinkABC) was proposed by considering the properties of the optimization problems related with the big data. The slinkABC algorithm was tested on the big data optimization problems presented at the Congress on Evolutionary Computation (CEC) 2015 Big Data Optimization Competition. The results obtained from the experimental studies were compared with the different variants of the ABC algorithm including gbest-guided ABC (GABC), ABC/best/1, ABC/best/2, crossover ABC (CABC), converge-onlookers ABC (COABC), quick ABC (qABC) and modified gbest-guided ABC (MGABC) algorithms. In addition to these, the results of the proposed ABC algorithm were also compared with the results of the Differential Evolution (DE) algorithm, Genetic algorithm (GA), Firefly algorithm (FA), Phase-Based Optimization (PBO) algorithm and Particle Swarm Optimization (PSO) algorithm-based approaches. From the experimental studies, it was understood that the ABC algorithm modified by considering the unique properties of the big data optimization problems as in the slinkABC produces better solutions for most of the tested instances compared to the mentioned optimization techniques.

Big Data Processing and Mining for Next Generation Intelligent Transportation Systems

2013 ◽  
Vol 63 (3) ◽  
Author(s):  
Jelena Fiosina ◽  
Maxims Fiosins, Jörg P. Müller
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real World ◽  
Intelligent Transportation Systems ◽  
Social Impact ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Optimization Techniques ◽  
Next Generation ◽  
Big Data Processing

The deployment of future Internet and communication technologies (ICT) provide intelligent transportation systems (ITS) with huge volumes of real-time data (Big Data) that need to be managed, communicated, interpreted, aggregated and analysed. These technologies considerably enhance the effectiveness and user friendliness of ITS, providing considerable economic and social impact. Real-world application scenarios are needed to derive requirements for software architecture and novel features of ITS in the context of the Internet of Things (IoT) and cloud technologies. In this study, we contend that future service- and cloud-based ITS can largely benefit from sophisticated data processing capabilities. Therefore, new Big Data processing and mining (BDPM) as well as optimization techniques need to be developed and applied to support decision-making capabilities. This study presents real-world scenarios of ITS applications, and demonstrates the need for next-generation Big Data analysis and optimization strategies. Decentralised cooperative BDPM methods are reviewed and their effectiveness is evaluated using real-world data models of the city of Hannover, Germany. We point out and discuss future work directions and opportunities in the area of the development of BDPM methods in ITS.

Algorithm and Data Optimization Techniques for Scaling to Massively Threaded Systems

Computer ◽  
2012 ◽  
Vol 45 (8) ◽  
pp. 26-32 ◽  
Author(s):  
John A. Stratton ◽  
Christopher Rodrigues ◽  
I-Jui Sung ◽  
Li-Wen Chang ◽  
Nasser Anssari ◽  
...  
Keyword(s):  
Optimization Techniques ◽  
Data Optimization

An Investigation Into the Efficacy of Deep Learning Tools for Big Data Analysis in Health Care

2020 ◽  
pp. 1826-1838
Author(s):  
Rojalina Priyadarshini ◽  
Rabindra K. Barik ◽  
Chhabi Panigrahi ◽  
Harishchandra Dubey ◽  
Brojo Kishore Mishra
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Large Data ◽  
Big Data Analysis ◽  
Optimization Techniques ◽  
Data Sets ◽  
Learning Tools ◽  
Healthcare Applications ◽  
Proper Training ◽  
Future Prediction

This article describes how machine learning (ML) algorithms are very useful for analysis of data and finding some meaningful information out of them, which could be used in various other applications. In the last few years, an explosive growth has been seen in the dimension and structure of data. There are several difficulties faced by conventional ML algorithms while dealing with such highly voluminous and unstructured big data. The modern ML tools are designed and used to deal with all sorts of complexities of data. Deep learning (DL) is one of the modern ML tools which are commonly used to find the hidden structure and cohesion among these large data sets by giving proper training in parallel platforms with intelligent optimization techniques to further analyze and interpret the data for future prediction and classification. This article focuses on the use of DL tools and software which are used in past couple of years in various areas and especially in the area of healthcare applications.

scholarly journals A Survey on Securing and Optimizing Health Care Bigdata

2018 ◽  
Vol 7 (4.10) ◽  
pp. 504
Author(s):  
K. Kavitha ◽  
D. Anuradha ◽  
P. Pandian
Keyword(s):  
Health Care ◽  
Big Data ◽  
Access Control ◽  
Mathematical Optimization ◽  
Optimization Techniques ◽  
Role Based Access Control ◽  
Attribute Based Access Control ◽  
Optimization And Simulation ◽  
Role Based ◽  
The Cost

Huge amount of health care data are available online to improve the overall performance of health care system. Since this huge health care Big-data is valuable and sensitive, it requires safety. In this paper we analyze numerous ways in which the health care Big-data can be protected. In recent days many augmented security algorithm that are suitable for Big-data have emerged like, El-Gamal, Triple-DES, and Homomorphic algorithms. Also authentication and access control can be implemented over Big-data using Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) schemes.Along with security to Big-data we try to evolve the ways in which the valuable Big-data can be optimized to improve the Big-data analysis. Mathematical optimization techniques such as simple and multi-purpose optimization and simulation are employed in Big-data to maximize the patient satisfaction and usage of doctor’s consulting facility. And also, to minimize the cost spent by patient and energy wasted.  

scholarly journals Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data

2015 ◽  
Vol 32 (03) ◽  
pp. 1550019 ◽  
Author(s):  
Jie Xu ◽  
Edward Huang ◽  
Chun-Hung Chen ◽  
Loo Hay Lee
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
High Performance ◽  
Manufacturing Systems ◽  
Stochastic Systems ◽  
Simulation Optimization ◽  
Big Data Analytics ◽  
Optimization Techniques ◽  
New Era ◽  
Recent Developments

Recent advances in simulation optimization research and explosive growth in computing power have made it possible to optimize complex stochastic systems that are otherwise intractable. In the first part of this paper, we classify simulation optimization techniques into four categories based on how the search is conducted. We provide tutorial expositions on representative methods from each category, with a focus in recent developments, and compare the strengths and limitations of each category. In the second part of this paper, we review applications of simulation optimization in various contexts, with detailed discussions on health care, logistics, and manufacturing systems. Finally, we explore the potential of simulation optimization in the new era. Specifically, we discuss how simulation optimization can benefit from cloud computing and high-performance computing, its integration with big data analytics, and the value of simulation optimization to help address challenges in engineering design of complex systems.

scholarly journals Parallel coordinate descent methods for big data optimization

2015 ◽  
Vol 156 (1-2) ◽  
pp. 433-484 ◽  
Author(s):  
Peter Richtárik ◽  
Martin Takáč
Keyword(s):  
Big Data ◽  
Coordinate Descent ◽  
Descent Methods ◽  
Data Optimization
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