A review of data mining in knowledge management: applications/findings for transportation of small and medium enterprises

Selamat, Siti Aishah Mohd; Prakoonwit, Simant; Khan, Wajid

doi:10.1007/s42452-020-2589-3

Cited by 7 publications

(5 citation statements)

References 37 publications

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“…Based on the above estimation, the validations (8), (9), and (10) are performed using the federated learning model.…”

Section: Proposed Connectivity-persistent Data Mining Methodmentioning

confidence: 99%

“…is is depicted in Figure 3. e learning induces multiple ψ as defined in ( 8), (9), and (10) for differentω r (t). Based on the sharing output, user service mining and allocations are performed.…”

Section: Proposed Connectivity-persistent Data Mining Methodmentioning

confidence: 99%

“…In the available allocation intervals, χ o (t) is the deciding factor for preventing increasing mining time for multiple resources. If the service and user concentration increase, then the ψ factor as in( 8), (9), and ( 10) is assessed for different ϕ conditions. ese conditions are based on the time factor for preventing additional delay, and therefore, the allocation consecutively aids existence.…”

Section: Mining Time Comparisonmentioning

confidence: 99%

“…Various demands and requests are demanded by an organization for the data mining process. Companies and industries demand a certain set of services that improve the accuracy rate in the data mining process [9,10].…”

Section: Introductionmentioning

confidence: 99%

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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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“…Based on the above estimation, the validations (8), (9), and (10) are performed using the federated learning model.…”

Section: Proposed Connectivity-persistent Data Mining Methodmentioning

confidence: 99%

Section: Proposed Connectivity-persistent Data Mining Methodmentioning

confidence: 99%

Section: Mining Time Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhang

Chi

2022

Scientific Programming

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“…ere are a lot of matters involved in enterprise lean management quality management, such as courses and evaluation index, etc. Association rules can extract valuable knowledge hidden in data, assist educational administrators to make effective decisions, and improve the management level [19][20][21].…”

Section: Quality Evaluation Of Enterprise Lean Managementmentioning

confidence: 99%

Application of Data Mining in Effect Evaluation of Lean Management

Ding¹,

Li²,

2022

Scientific Programming

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Quantitative evaluation is an important part of enterprise diagnosis, which promotes the scientific and modern management of enterprises. At present, the existing enterprise management evaluation methods cannot complete the mining of enterprise index data, which leads to large error and low significance coefficient in enterprise management evaluation. Therefore, the application of data mining in enterprise lean management effect evaluation is put forward. The process and main functions of data mining are analyzed; data mining algorithm is used to establish the evaluation index system of lean management effect and calculate the index weight. Using the association rules method in data mining, according to the parameters of enterprise lean management level evaluation index and weight value, through the fuzzy set transformation idea, the fuzzy boundary of each index and factor is described by the membership degree, the fuzzy judgment matrix is constructed, and the final evaluation result is obtained by multilayer compound calculation. Experimental results show that this study has a high significance coefficient, and the proposed evaluation method of enterprise lean management effect has ideal accuracy and short time consumption. In practical application, the cumulative contribution rate is higher and has higher stability.

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