Evolutionary optimization: A big data perspective

Bhattacharya, Maumita; Islam, Rafiqul; Abawajy, Jemal

doi:10.1016/j.jnca.2014.07.032

Cited by 56 publications

(21 citation statements)

References 26 publications

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“…Process capability enhancement:This enhancement technique is used in application level optimization [9] to improve the performance of data transmission over pipelining and also supports parallelism and concurrency control. Another one application is evolutionary optimization [10], which proposes genetic operators and focuses on high dimensional optimization problems which even works on complex solution space. Memory management enhancement: This enhancement technique is used in In-memory big data optimization, which shows a detailed examination of the required technology related to memory management [11] along with the related works and also supports all the memory operations to be more effective in planning and execution.…”

Section: Methodsmentioning

confidence: 99%

Indexing and Optimization Techniques in Biomedical Industry

N.R*¹,

Prem²

2019

IJEAT

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The unpredictable amount of data generated everyday by smart phones, social networks, health care systems etc. is really mind blowing. Smart phones alone generate 335exabytes of data ineveryyear that is really big data.Thus, the storage industry is facing several challenges in providing high magnitude of storage and retrieval devices at lowest costs which help to fulfill the requirements of big data and even technologies like de-duplication on storage devices are also becoming very important. Similarly, in recent days storing and retrieving the health care information in biomedical area is also becoming a great challenge in providing the best optimum data because of its huge amount of biomedical datasets. In order to achieve efficiency in providing highest quality health care information, an optimized index scheme is needed for big data which is based on accuracy and timelines. The existing indexing and optimization solutions are not enough to meet the emerging grow of index size and seek time. The objective of this paper is to identify better indexing solutions by investigating the basic big data requirements on indexing and optimization. This also includes a comparative study of various indexing and optimization techniques along with a taxonomy which contains Artificial Intelligence (AI) and Non Artificial Intelligence (NAI) based indexing techniques, optimization enhancement techniques which improves the performance efficiency of big data health care informatics.

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Section: Methodsmentioning

confidence: 99%

Indexing and Optimization Techniques in Biomedical Industry

N.R*¹,

Prem²

2019

IJEAT

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“…Evolutionary algorithms (EA) are another CI technique, which can meet the requirements and challenges of Big Data analytics. EA are very good explorers of the search space which makes them very good candidates of Big Data analysis, since Big Data are subjects of a very high degree of dimensionality and sparseness (Bhattacharya et al, 2016). This problem was investigated in the paper by Bhattacharya et al, where the researchers have developed an EA with the ability to deal with both issues.…”

Section: Computational Intelligence For Big Data Analyticsmentioning

confidence: 99%

Big data analytics: Computational intelligence techniques and application areas

Iqbal

Doctor

et al. 2020

Technological Forecasting and Social Change

176

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Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment.

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“…The research also introduces an algorithm to deal with the high dimensionality problem. The POPULATION_EA [2] algorithm introduce in this study mainly aim to manage advanced dimensional problem field. The POPULATION_EA prototype is capable in handling high dimensional optimization problems including complex multi model solution space.…”

Section: A Process Capability Enhancementmentioning

confidence: 99%

Big Data Optimization Techniques: A Survey

Roy¹,

Rautaray²,

Pandey³

2018

IJIEEB

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As the world is getting digitized the speed in which the amount of data is over owing from different sources in different format, it is not possible for the traditional system to compute and analysis this kind of big data for which big data tool like Hadoop is used which is an open source software. It stores and computes data in a distributed environment. In the last few years developing Big Data Applications has become increasingly important. In fact many organizations are depending upon knowledge extracted from huge amount of data. However traditional data technique shows a reduced performance, accuracy, slow responsiveness and lack of scalability. To solve the complicated Big Data problem, lots of work has been carried out. As a result various types of technologies have been developed. As the world is getting digitized the speed in which the amount of data is over owing from different sources in different format, it is not possible for the traditional system to compute and analysis this kind of big data for which big data tool like Hadoop is used which is an open source software. This research work is a survey about the survey of recent optimization technologies and their applications developed for Big Data. It aims to help to choose the right collaboration of various Big Data technologies according to requirements.

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Evolutionary optimization: A big data perspective

Cited by 56 publications

References 26 publications

Indexing and Optimization Techniques in Biomedical Industry

Indexing and Optimization Techniques in Biomedical Industry

Big data analytics: Computational intelligence techniques and application areas

Big Data Optimization Techniques: A Survey

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