Research on parallel data processing of data mining platform in the background of cloud computing

Bu, Lingrui; Zhang, Hui; Xing, Haiyan; Wu, Lijun

doi:10.1515/jisys-2020-0113

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Load balancing efficiency refers to how better a system can distribute tasks to the available resources to enhance the overall performance [25]. A higher acceleration ratio means the system would perform better in case of more cores or computers [26]. From Table 1, we can see that the evaluation indexes of this method are better than those of the two other methods we studied, and the performance and effectiveness of this method are high.…”

Section: Experimental Analysismentioning

confidence: 98%

Composition and Optimization of Higher Education Management System Based on Data Mining Technology

Hu¹,

Li²

2021

Scientific Programming

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In order to improve the low efficiency of higher education management and the unequal distribution of curriculum resources, facing the actual situation of higher education management, a higher education management system based on data mining technology is constructed and optimized. Using the correlation between MVC components of higher education management platform and the support of data mining technology, this paper constructs the MVC model 2 framework of higher education management platform which is a powerful framework used for developing large-scale projects with ease. We collect the data structure in the database, extract the evaluation indicators of higher education management system, separate the display results and business logic, and improve code reusability. We adopt data input, curriculum management, curriculum scheduling, teacher scheduling, and system maintenance and help to form and optimize the higher education management system. The experimental results show that the constructed and optimized higher education management system can maintain good scheduling performance and provide better teaching services for teachers and students.

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Section: Experimental Analysismentioning

confidence: 98%

Composition and Optimization of Higher Education Management System Based on Data Mining Technology

Hu¹,

Li²

2021

Scientific Programming

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“…In this (9) study, a data mining platform based on the Hadoop distributed file system was developed, and then the K-means algorithm was enhanced with the concept of max-min distance. This parallel processing can be described as a class of techniques that enable the system to attain concurrently data-processing tasks to increase the computational speed of a computer system, but it also increases the cost of computers because more hardware is required.…”

Section: Introductionmentioning

confidence: 99%

Two Level Privacy Preserving Model for Association Rule Mining in Cloud Using Deep-SVM Method

Mangayarkkarasi

2024

IJST

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Background/Objectives: It is becoming more common for data owners to outsource data mining tasks and storage to cloud service providers as a result of the rising costs of maintaining IT infrastructures for large-scale data mining. This trend, however, also raises security concerns about unauthorized breaches of data confidentiality and outcome integrity. Methods: This research considers this scenario in which cloud user can encrypt their data and store it to cloud environment. In order to perform mining operation, the user needs to outsource the task to cloud servers. Then, the cloud server performs the mining task on the encrypted data and share the encrypted association rule to the cloud user. Yet, existing single cloud server systems have privacy leakage issues since their work focuses on either database privacy or item privacy. To remedy this gap in the literature, this study maintains both database privacy and item privacy during the frequent itemset mining process. For item privacy, it first describes a universal safe multiplication protocol with a single cloud server. We build the inner product rules, comparison rules, frequent itemset protocol, and final association rule mining process that is secure against privacy leaking on top of this multiplication protocol. During this Association Rule Mining (ARM) operation, it provides two level of protection to data privacy. This model is designed with distributed Elgamal cryptosystem and sub-protocols for item and database privacy along with Deep learning-based Support Vector Machine (SVM) for secure rule generation. Findings: The proposed method is named as two-level privacy preserving method association with Deep SVM model (2-level:D-SVM), provides guaranteed solutions to the confidentiality of the outsourced cloud data and minimizes the user interaction during association rule mining task. Here, data on breast cancer and heart disease are used, and the effectiveness of the proposed model is demonstrated by comparison to existing models. According to the study, at 25000 transactions, the proposed 2-level:D-SVM model stands for 52% and 50% more efficient than Parallel Processing (PP) and Privacy-preserving Collaborative Model Learning (PCML) techniques in terms of computing cost. Additionally, the proposed model performs 34%, 22%, 6%, and 4% better in terms of execution time https://www.indjst.org/

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Research on parallel data processing of data mining platform in the background of cloud computing

Cited by 2 publications

References 22 publications

Composition and Optimization of Higher Education Management System Based on Data Mining Technology

Composition and Optimization of Higher Education Management System Based on Data Mining Technology

Two Level Privacy Preserving Model for Association Rule Mining in Cloud Using Deep-SVM Method

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