Mining method of massive data of mobile library under information asymmetry facing large-scare database

Zhang, Zhiyan

doi:10.1016/j.micpro.2020.103730

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…Zhang et al [20] introduced a massive data miningbased method for mobile libraries. e filtering technique is used here to filter the candidate's available datasets in mobile libraries and produce a feasible set of data for further process.…”

Section: Related Workmentioning

confidence: 99%

Data Management and Service Mode of Library Based on Data Mining Algorithm

Zhang

Chi

2022

Scientific Programming

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Data management for large-scale data library services with mining procedures improves the availability and readiness of heterogeneous sources. The heterogeneous data sources are assimilated as a single entity through mining procedures to meet the data demands. This article introduces connectivity-persistent data mining method (CDMM) to improve the data handling precision with boosting availability. The proposed method relies on federated learning for identifying the service demands, thereby providing data mining. The learning paradigm accumulates information on shared data library existence over various services. Based on the availability, further mining demands are forwarded to the data management system. If the existence verified by the federated learning is adaptable, then sharing-enabled mining is endorsed for the connected users. The data management then augments several heterogeneous shared libraries to meet the mining requirements. This process is reversible based on the service mode and existence. Therefore, the proposed method improves data availability with less mining and access time and fewer failures.

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Section: Related Workmentioning

confidence: 99%

Data Management and Service Mode of Library Based on Data Mining Algorithm

Zhang

Chi

2022

Scientific Programming

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“…en, the obtained knowledge can be put into industrial or commercial applications [10][11][12]. erefore, the two technologies have been widely used in many fields such as disease diagnosis, traffic management, weather forecasting, software testing, and commercial marketing [13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A Vibrational Signal Fault Diagnosis Rule Extraction Method Based on DST‐ACI Discriminant Criterion

Zhang

Guo

2021

Shock and Vibration

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A fault diagnosis rule extraction method oriented to machine foot signal based on dynamic support threshold and association coefficient interestingness (DST-ACI) discriminant criterion is proposed in this paper. The new method includes three main innovations. First, the feature state coding method based on K-means clustering fully takes into account the imbalanced distribution of signal feature values due to the noise interference, and divide the signal feature values into several range intervals to generate the feature state code. Second, the frequent feature pattern mining method based on dynamic support threshold (DST) discriminant criterion can dynamically adjust support threshold according to the frequency of the feature states in each candidate pattern. Third, the fault diagnosis rule extraction method based on the association coefficient interestingness (ACI) discriminant criterion introduces a new metrics called ACI to evaluate the correlation between the pattern and the fault. Four types of fault simulation experiments were carried out, and the performance of the DST-ACI method was tested using the collected vibration signal. The results show that compared with the coding method based on equal-width discretization or equal-density discretization, the accuracy of the transactional dataset generated by the feature state coding method based on K-means clustering is higher. Compared with the frequent feature pattern mining method based on the constant support threshold criterion, the pattern mined by the DST-based criterion has generally higher support. Compared with the existing confidence-lift-based and confidence-improint-based fault diagnosis rule extraction frameworks, the positive correlation between the feature states and the fault type of the rules extracted based on the DST-ACI framework is generally stronger.

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Construction and Application of College Mobile Library Platform Based on Android

Yang¹

2023

Lecture Notes on Data Engineering and Communications Technologies

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Mining method of massive data of mobile library under information asymmetry facing large-scare database

Cited by 4 publications

References 28 publications

Data Management and Service Mode of Library Based on Data Mining Algorithm

Data Management and Service Mode of Library Based on Data Mining Algorithm

A Vibrational Signal Fault Diagnosis Rule Extraction Method Based on DST‐ACI Discriminant Criterion

Construction and Application of College Mobile Library Platform Based on Android

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