Application of Big Data Technology in Scientific Research Data Management of Military Enterprises

Wang, Kun; Liu, Tong; Xie, Xiang

doi:10.1016/j.procs.2019.01.221

Cited by 11 publications

(4 citation statements)

References 1 publication

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“…Asset and process management research argue that data should be specifically structured for the intended use within the work flow [27,57]. Analytics research concur and note that insight is more obtainable when the data has been preprocessed for a specific domain of analysis [32,37,42,52,68]. To this effect, and to address the previous highlighted shortcomings, we propose an extended CRISP-DM process model.…”

Section: An Enhanced Process Modelmentioning

confidence: 96%

Exploring the Relationship Between Data Science and Circular Economy: An Enhanced CRISP-DM Process Model

Kristoffersen

Aremu

Blomsma

et al. 2019

Lecture Notes in Computer Science

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To date, data science and analytics have received much attention from organizations seeking to explore how to use their massive volumes of data to create value and accelerate the adoption of Circular Economy (CE) concepts. The correct utilization of analytics with circular strategies may enable a step change that goes beyond incremental efficiency gains towards a more sustainable and circular economy. However, the adoption of such smart circular strategies by the industry is lagging, and few studies have detailed how to operationalize this potential at scale. Motivated by this, this study seeks to address how organizations can better structure their data understanding and preparation to align with overall business and CE goals. Therefore, based on the literature and a case study the relationship between data science and the CE is explored, and a generic process model is proposed. The proposed process model extends the Cross Industry Standard Process for Data Mining (CRISP-DM) with an additional phase of data validation and integrates the concept of analytic profiles. We demonstrate its application for the case study of a manufacturing company seeking to implement the smart circular strategy -predictive maintenance.

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Section: An Enhanced Process Modelmentioning

confidence: 96%

Exploring the Relationship Between Data Science and Circular Economy: An Enhanced CRISP-DM Process Model

Kristoffersen

Aremu

Blomsma

et al. 2019

Lecture Notes in Computer Science

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“…The shared data stored by data owners in the cloud storage server usually has hierarchical structures [4,5], as shown in Figure 1. However, CP-ABE did not consider this characteristic.…”

Section: Introductionmentioning

confidence: 99%

A Data Hierarchical Encryption Scheme Based on Attribute Hiding under Multiple Authorization Centers

Wang,

Pan,

et al. 2023

Electronics

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The data hierarchical Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme implements multiple hierarchical data encryption of a single access policy, which reduces the computation and storage overhead. However, existing data hierarchical CP-ABE schemes have some problems, such as the leakage of personal privacy information through access policies or user attributes in plaintext form, and these schemes grant enough privileges to a single authorization center. If the authorization center is untrusted or attacked, keys can be used to illegally access data, which is the key escrow problem. To solve these problems, we propose an Attribute Hiding and Multiple Authorization Centers-based Data Hierarchical Encryption Scheme (AH-MAC-DHE). Firstly, we propose an Attribute Convergence Hiding Mechanism (ACHM). This mechanism solves the problem of personal privacy information leakage by hiding access policies and user attributes. Secondly, we design Privilege-Dispersed Multiple Authorization Centers (PD-MAC). PD-MAC solves the problem of key escrow by dispersing the privileges of the single authorization center to the user authorization center and attribute authorization center. Finally, we prove that AH-MAC-DHE is secure under the decisional q-parallel Bilinear Diffie-Hellman Exponent (BDHE) assumption, which also satisfies anti-collusion and privacy security. The experimental results indicate that compared with existing schemes, AH-MAC-DHE performs well.

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“…The BDA tools provide valuable benefits to the organization to deal with the three types of data, such as structured, semi-structured and unstructured data [9]. However, the conventional database techniques unable to process these challenging data and its only focus on the structured data [10]. To handle the semi-structured and unstructured data, there is no proper methods developed in BD context [11].…”

Section: Introductionmentioning

confidence: 99%

A Prediction of Different Technologies for the Development of Unstructured Big Data

B M*,

Devi K A

2020

IJITEE

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Nowadays, a huge volume of terabytes of data is generated from digital technologies and modern information systems, namely Internet of Things and cloud computing. The extraction of knowledge for making decisions from the analysis of these massive data, leads to requires a huge effort at multiple levels. Hence, the researchers focused on Big Data Analysis (BDA) for better development. Traditional platforms and data techniques are very less efficient in Big Data (BD) context, which shows the lack of accuracy, performance, scalability and slow responsiveness. Several works are carried out to address the complex BD challenges by developing new technologies and different types of distributions. In this research work, various technologies which are developed for BD are described and the impact of open research issues, challenges and tools for processing the BD are discussed. Then, the impacts on key business performances for BD are evaluated. At last, this work presented the four major technical and managerial challenges, which provides a platform for exploring BD at numerous stages

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Application of Big Data Technology in Scientific Research Data Management of Military Enterprises

Cited by 11 publications

References 1 publication

Exploring the Relationship Between Data Science and Circular Economy: An Enhanced CRISP-DM Process Model

Exploring the Relationship Between Data Science and Circular Economy: An Enhanced CRISP-DM Process Model

A Data Hierarchical Encryption Scheme Based on Attribute Hiding under Multiple Authorization Centers

A Prediction of Different Technologies for the Development of Unstructured Big Data

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