An overview of big data opportunities, applications and tools

Benjelloun, Fatima-Zahra; Lahcen, Ayoub Ait; Belfkih, Samir

doi:10.1109/isacv.2015.7105553

Cited by 40 publications

(21 citation statements)

References 23 publications

(20 reference statements)

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“…Mariyah () state that big data contribute values to organisations. Benjelloun, Lahcen, and Belfkih () pointed out that the insights organisations gain from big data can help decision makers to enhance their strategies and optimise their plans.…”

Section: Literature Reviewmentioning

confidence: 99%

The use of mobile systems to access health care big data in the Namibian environment

Shaanika

Iyamu

2019

E J Info Sys Dev Countries

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As the need for health care services increases, some health facilities in many developing countries such as Namibia are challenged with accessing its big data. Despite the success of mobile systems, its use to access health care big data is challenging, which have impact on manageability and improvement of service delivery. The main problem is that the factors that influence interaction between mobile systems and health care big data are not empirically known within the Namibian context. The Namibian Ministry of Health (MoH) was therefore used as a case in this study. Qualitative methods and an interpretivist approach were employed, and structuration theory was used to underpin the study, which means the theory guided the analysis of the data. From the analysis, five factors were found to influence interaction in the use of mobile systems to access health care big data. On the basis of these factors, a model is proposed, which is a contribution to improving health care service delivery in the country through its practical implications: guiding interactions among health care stakeholders and developing policy for accessing patients' big data.

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Section: Literature Reviewmentioning

confidence: 99%

The use of mobile systems to access health care big data in the Namibian environment

Shaanika

Iyamu

2019

E J Info Sys Dev Countries

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“…Any government entity or organization that uses the information and communication technologies to operate their business and services is known as e-government [2]. Egovernance is the collection of services of the government provided to the people [2].…”

Section: Figure 1 Different Data Sourcesmentioning

confidence: 99%

Section: Figure 1 Different Data Sourcesmentioning

confidence: 99%

“…Egovernance is the collection of services of the government provided to the people [2]. In India national e-governance plan NeGP is implemented by Department of Electronics and Information Technology (DeitY) and Department of Administrative Reforms and Public Grievances (DARPG) [2]. The vision of NeGP is "Make all Public Services accessible to the common man in his locality, through common service delivery outlets and ensure efficiency, transparency and reliability of such services at affordable costs to realize the basic needs of the common man" [3].…”

Section: Figure 1 Different Data Sourcesmentioning

confidence: 99%

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Necessity of Big Data and Analytics for Good e-governance

Patel¹,

Roy²,

Bhattacharyya³

et al. 2017

IJGDC

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“…Besides, storing large scale of instances can result in a great memory cost and a slow execution speed for instance-based learning algorithms. One alternative for tackling the problem is to perform data reduction as a preprocessing step of machine learning [3]. Data reduction is to remove the unimportant instances, e.g., noisy, redundant or less related ones, from the original datasets without greatly deteriorating the learners' performance.…”

Section: Introductionmentioning

confidence: 99%

Fast Data Reduction With Granulation-Based Instances Importance Labeling

2019

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Data reduction has become greatly significant prior to applying instance-based machine learning algorithms in the Big Data era. Data reduction is used to reduce the size of data sets while retaining representative data. Existing algorithms, however, suffer from heavy computational cost and in having tradeoff in size reduction rate and learning accuracy. In this paper, we propose a fast data reduction approach by using granular computing to label important instances, i.e., instances with higher contributions to the learning task. The original data set is first granulated into K granules by applying K-means to a mapped lower-dimension space. Then, the importance of each instance in every granule is labeled based on its Hausdorff distance. Those instances whose importance values are lower than an experimentally tuned threshold are eliminated. The presented algorithm is applied to kNN classification tasks with eighteen different sizes of data sets from the UCI repository, and its outstanding performance in classification accuracy, size reduction rate, and runtime is illustrated by comparing with seven data reduction methods. The experimental results demonstrate that the proposed algorithm can greatly reduce the computational cost and achieve a higher classification accuracy when the reduction size is the same for all the compared algorithms. INDEX TERMS Data reduction, granular computing, data importance label, kNN.

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An overview of big data opportunities, applications and tools

Cited by 40 publications

References 23 publications

The use of mobile systems to access health care big data in the Namibian environment

The use of mobile systems to access health care big data in the Namibian environment

Necessity of Big Data and Analytics for Good e-governance

Fast Data Reduction With Granulation-Based Instances Importance Labeling

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