A survey of tools and applications in big data

Menon, Sindhu P.; Hegde, Nagaratna P.

doi:10.1109/isco.2015.7282364

Cited by 23 publications

(8 citation statements)

References 5 publications

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“…These Software platforms allow writing and running applications easily, which makes easier to process large amount of data. [3] 4. ARCHITECTURE OF HADOOP …”

Section: Traditional Enterprise Approachmentioning

confidence: 99%

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Security Survey on Big Data and Efficient Encryption Methods

Nandargi¹

2017

IJRET

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In the last few years, the growth in the data is enormous. Data is growing in the form of terabytes to Exabyte's, such data is difficult to manage and process it through traditional systems. One of the popular methods for processing large amount of data is Hadoop. Originally Hadoop system was developed without security models so security is major concern of Hadoop development. This paper concentrates on security issues in Hadoop and available solutions.

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“…These Software platforms allow writing and running applications easily, which makes easier to process large amount of data. [3] 4. ARCHITECTURE OF HADOOP …”

Section: Traditional Enterprise Approachmentioning

confidence: 99%

“…The digitization makes new types of large and real time data over the years, So many organizations, industries have to process terabytes to pet bytes of data, usually it takes long time to analyse it. [3] 6. HADOOP SECURITY ISSUES…”

Section: Relevancementioning

confidence: 99%

Security Survey on Big Data and Efficient Encryption Methods

Nandargi¹

2017

IJRET

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“…Data mining approaches can be classified into two major models [11]: The descriptive data mining and the predictive data mining models. The descriptive models are unsupervised learning that describe the historical events, and the presumed or real relationship between elements that created them.…”

Section: A Association Rulesmentioning

confidence: 99%

“…These models uses a summarization analysis tools including multiple techniques such as, association rule for discovering and extracting relevant data. This model is commonly used in marketing analysis [11].…”

Section: A Association Rulesmentioning

confidence: 99%

A Novel Mapreduce Lift Association Rule Mining Algorithm (Mrlar) for Big Data

Oweis¹,

Fouad²,

Oweis³

et al. 2016

ijacsa

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Abstract-Big Data mining is an analytic process used to discover the hidden knowledge and patterns from a massive, complex, and multi-dimensional dataset. Single-processor's memory and CPU resources are very limited, which makes the algorithm performance ineffective. Recently, there has been renewed interest in using association rule mining (ARM) in Big Data to uncover relationships between what seems to be unrelated. However, the traditional discovery ARM techniques are unable to handle this huge amount of data. Therefore, there is a vital need to scalable and parallel strategies for ARM based on Big Data approaches. This paper develops a novel MapReduce framework for an association rule algorithm based on Lift interestingness measurement (MRLAR) which can handle massive datasets with a large number of nodes. The experimental result shows the efficiency of the proposed algorithm to measure the correlations between itemsets through integrating the uses of MapReduce and LIM instead of depending on confidence.

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“…Our prior work [6][7][8][9] have discussed about the existing technqiues pertaining to significance of classification approach, tools of big data analytics, and applicability of big data analytics over healthcare sector. Chen et al [10] have presented architecture for supportability of agile methodologies over big data.…”

Section: Introductionmentioning

confidence: 99%

A Novel Integrated Framework to Ensure Better Data Quality in Big Data Analytics over Cloud Environment

Sindhu¹,

Hegde²

2017

IJECE

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With advent of Big Data Analytics, the healthcare system is increasingly adopting the analytical services that is ultimately found to generate massive load of highly unstructured data. We reviewed the existing system to find that there are lesser number of solutions towards addressing the problems of data variety, data uncertainty, and data speed. It is important that an errorfree data should arrive in analytics. Existing system offers single-hand solution towards single platform. Therefore, we introduced an integrated framework that has the capability to address all these three problems in one execution time. Considering the synthetic big data of healthcare, we carried out the investigation to find that our proposed system using deep learning architecture offers better optimization of computational resources. The study outcome is found to offer comparatively better response time and higher accuracy rate as compared to existing optimization technqiues that is found and practiced widely in literature.

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A survey of tools and applications in big data

Cited by 23 publications

References 5 publications

Security Survey on Big Data and Efficient Encryption Methods

Security Survey on Big Data and Efficient Encryption Methods

A Novel Mapreduce Lift Association Rule Mining Algorithm (Mrlar) for Big Data

A Novel Integrated Framework to Ensure Better Data Quality in Big Data Analytics over Cloud Environment

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