The big data system, components, tools, and technologies: a survey

Rao, T. Ramalingeswara; Mitra, Pabitra; Bhatt, Ravindara; Goswami, Adrijit

doi:10.1007/s10115-018-1248-0

Cited by 109 publications

(72 citation statements)

References 124 publications

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“…(4), and & is used to generate the set of keys & , which determine the distance of image rotation for each column in accordance with Eq. (5). In the third step, the original image is divided into m sets of columns, where each set consists of elements referred to as !…”

Section: A a New Image Encryption Methods Using A Pseudoset Based On mentioning

confidence: 99%

“…As predicted by Ericsson [3], in 2022, 18 billion of 29 billion devices connected to the internet will be devices related to the IoT, while the Statista Research Department [4] predicts that by 2025, the total number of devices connected to the IoT around the world will reach 75.44 billion. The number of interconnected devices has caused the corresponding data to rapidly increase in abundance and diversity, so big data technology plays an important role in each stage of data processing [5]. Other important concerns related to a large amount of data are security and privacy [6].…”

Section: Introductionmentioning

confidence: 99%

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Novel Image Encryption Using a Pseudoset Generated by Chaotic Permutation Multicircular Shrinking With a Gradual Deletion of the Input Set

et al. 2020

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High-level security with a large keyspace and a short processing time is needed in digital image encryption. Generally, an encryption method that produces a large keyspace is characterized by a relatively slow encryption process. In this paper, we propose a new image encryption method that uses two chaotic pseudosets. A gradual deletion of the input set (GDIS) is introduced to enhance the process of chaotic permutation multicircular shrinking (CPMCS), herein referred to as GDIS CPMCS, to diffuse the image pixels and control the shift distance of the row and column rotations of an image. The proposed encryption scheme offers some advantages: it has a larger keyspace than the referenced image encryption schemes and a shorter processing time than CPMCS. The processing time of the proposed image encryption method with the GDIS CPMCS algorithm is 16.7 times faster for a gray image and 43 times faster for a color image than that in our previous work. Based on histogram and entropy analyses, the proposed scheme is also robust to statistical analysis. Moreover, the ciphered image has a very high degree of randomness according to the National Institute of Standards and Technology (NIST) randomness test results. In terms of differential analysis, a one-bit change in the original image leads to a substantial change in the ciphered image, as indicated by the unified average change intensity (UACI) and number of pixel change rate (NPCR) scores of 33.45% and 99.61%, respectively. Furthermore, GDIS CPMCS is robust to salt-andpepper, Poisson, Gaussian, and speckle noise, with peak signal-to-noise ratios (PSNRs) higher than 14. The scheme is also robust to data loss since a reconstructed image with 50% data loss can be recognized, as indicated by a PSNR of 11.4 INDEX TERMS cryptography; fast image encryption; very large keyspace; chaotic permutation; robust to noise; multiple circular shrinking

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Section: A a New Image Encryption Methods Using A Pseudoset Based On mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel Image Encryption Using a Pseudoset Generated by Chaotic Permutation Multicircular Shrinking With a Gradual Deletion of the Input Set

et al. 2020

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“…The recent developments in data availability, under the journal categories of psychology, management, and technology, are known by various terms: big data, digital ecosystems, advanced informatics, predictive analytics, integrated digital systems, and data analytics [ 13 ]. These emerging, multidisciplinary sources of data promise to supplement or replace traditional historical incident data, and also to transform enterprise thinking about the value added by advanced health and safety programs [ 14 , 15 ].…”

Section: Background For Shifts In Safety Thinkingmentioning

confidence: 99%

Finding Pluto: An Analytics-Based Approach to Safety Data Ecosystems

Barker

2021

Safety and Health at Work

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This review article addresses the role of safety professionals in the diffusion strategies for predictive analytics for safety performance. The article explores the models, definitions, roles, and relationships of safety professionals in knowledge application, access, management, and leadership in safety analytics. The article addresses challenges safety professionals face when integrating safety analytics in organizational settings in four operations areas: application, technology, management, and strategy. A review of existing conventional safety data sources (safety data, internal data, external data, and context data) is briefly summarized as a baseline. For each of these data sources, the article points out how emerging analytic data sources (such as Industry 4.0 and the Internet of Things) broaden and challenge the scope of work and operational roles throughout an organization. In doing so, the article defines four perspectives on the integration of predictive analytics into organizational safety practice: the programmatic perspective, the technological perspective, the sociocultural perspective, and knowledge-organization perspective. The article posits a four-level, organizational knowledge-skills-abilities matrix for analytics integration, indicating key organizational capacities needed for each area. The work shows the benefits of organizational alignment, clear stakeholder categorization, and the ability to predict future safety performance.

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“…Normally, these components are very complex, and need to work together if valuable insights are to be obtained [2]. These components can range from relational and non-relational databases to machine learning algorithms, virtual machines or containers, or even the underlying hardware that supports their services [3].…”

Section: Introductionmentioning

confidence: 99%

Improving Incident Response in Big Data Ecosystems by Using Blockchain Technologies

et al. 2020

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Big data ecosystems are increasingly important for the daily activities of any type of company. They are decisive elements in the organization, so any malfunction of this environment can have a great impact on the normal functioning of the company; security is therefore a crucial aspect of this type of ecosystem. When approaching security in big data as an issue, it must be considered not only during the creation and implementation of the big data ecosystem, but also throughout its entire lifecycle, including operation, and especially when managing and responding to incidents that occur. To this end, this paper proposes an incident response process supported by a private blockchain network that allows the recording of the different events and incidents that occur in the big data ecosystem. The use of blockchain enables the security of the stored data to be improved, increasing its immutability and traceability. In addition, the stored records can help manage incidents and anticipate them, thereby minimizing the costs of investigating their causes; that facilitates forensic readiness. This proposal integrates with previous research work, seeking to improve the security of big data by creating a process of secure analysis, design, and implementation, supported by a security reference architecture that serves as a guide in defining the different elements of this type of ecosystem. Moreover, this paper presents a case study in which the proposal is being implemented by using big data and blockchain technologies, such as Apache Spark or Hyperledger Fabric.

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The big data system, components, tools, and technologies: a survey

Cited by 109 publications

References 124 publications

Novel Image Encryption Using a Pseudoset Generated by Chaotic Permutation Multicircular Shrinking With a Gradual Deletion of the Input Set

Novel Image Encryption Using a Pseudoset Generated by Chaotic Permutation Multicircular Shrinking With a Gradual Deletion of the Input Set

Finding Pluto: An Analytics-Based Approach to Safety Data Ecosystems

Improving Incident Response in Big Data Ecosystems by Using Blockchain Technologies

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