Toward intelligent data warehouse mining: An ontology-integrated approach for multi-dimensional association mining

Lin, Wen-Yang; Jiang, Chang-Long; Wu, Chuan-Chun

doi:10.1016/j.eswa.2011.02.144

Cited by 12 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With major breakthroughs in new generation information technologies such as 5G communication, big data, and Internet of Things, the upgrading and transformation of the coal mining mode from automation to intelligence has received technical support. The concepts of smart coal mines and intelligent mines are gradually becoming clear, and the standards and specifications are gradually being completed, which lays a foundation for theor large-scale popularization and application [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Research on an Intelligent Mining Complete System of a Fully Mechanized Mining Face in Thin Coal Seam

Ren,

Ding,

Wang

et al. 2023

Sensors

View full text Add to dashboard Cite

The mining environment of thin coal seam working faces is generally harsh, the labor intensity is high, and the production efficiency is low. Previous studies have shown that thin coal seam mining finds it difficult to follow machines, does not have complete sets of equipment, has a low degree of automation, and has difficult system co-control, which easily causes production safety accidents. In order to effectively solve the problems existing in thin coal seam mining, Binhu Coal Mine has established intelligent fully mechanized mining and actively explored automatic coal cutting, automatic support following, and intelligent control. The combination of an SAC electro-hydraulic control system and SAP pumping station control system has been applied in 16,108 intelligent fully mechanized coal mining faces, which realizes the automatic following of underground support and the control of adjacent support, partition support, and group operation; the automatic coal cutting of the shearer is realized by editing the automatic coal-cutting state of the shearer and adjusting the automatic parameters. A centralized control center is set up, which realizes the remote control and one-button start–stop of working face equipment. Through a comparative analysis of 16,108 intelligent fully mechanized mining faces and traditional fully mechanized mining faces, it is found that intelligent fully mechanized mining faces have obvious advantages in terms of equipment maintenance, equipment operation mode, and working face efficiency, which improve the equipment and technical mining level of thin coal seam. The application of intelligent mining in Binhu Coal Mine has a great and far-reaching impact on the development of thin coal seam mining technology in China.

show abstract

Section: Introductionmentioning

confidence: 99%

Research on an Intelligent Mining Complete System of a Fully Mechanized Mining Face in Thin Coal Seam

Ren,

Ding,

Wang

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Further studies have indicated the applications of BIDW architectures that can be utilized at the second level of the above four-level concurrent decision-making model (Chan et al, 2011; Cuzzocrea, 2011; Ramakrishnan et al, 2012). Furthermore, a number of studies have focused on DM and ontology-based applications in DW architectures that can be used at the third and fourth levels of the above four-level concurrent decision-making model (Aggarwal et al, 2012; Jukic and Nestorov, 2006; Pardillo and Mazón, 2011; Wu et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

A concurrent group decision-making framework for selection of business intelligence–based data warehouse architecture

Zandi

2013

Concurrent Engineering

View full text Add to dashboard Cite

The selection of an appropriate data warehouse architecture from a business intelligence-based perspective is an important decision to achieve organizational strategies. The aim of this article is to develop a concurrent group decisionmaking framework for the selection of a data warehouse business intelligence-based architecture. In this framework, in order to translate higher level needs into lower level requirements, we develop a four-level concurrent decision-making model to turn (1) critical success factors of business intelligence implementation into key data mining factors at the first level, (2) the key data mining factors into key ontology factors at the second level, and (3) the key ontology factors into key data resource management factors at the third level. Then, these factors are utilized to select the business intelligence data warehouse architecture at the fourth level of the four-level concurrent decision-making model. A case study demonstrates the applicability of the concurrent group decision-making framework.

show abstract

Assessing Teacher’s Performance Evaluation and Prediction Model Using Cloud Computing Over Multi-dimensional Dataset

Kavitha

2021

Wireless Pers Commun

View full text Add to dashboard Cite

Toward intelligent data warehouse mining: An ontology-integrated approach for multi-dimensional association mining

Abstract: A data warehouse is an important decision support system with cleaned and integrated

Cited by 12 publications

References 33 publications

Research on an Intelligent Mining Complete System of a Fully Mechanized Mining Face in Thin Coal Seam

Research on an Intelligent Mining Complete System of a Fully Mechanized Mining Face in Thin Coal Seam

A concurrent group decision-making framework for selection of business intelligence–based data warehouse architecture

Assessing Teacher’s Performance Evaluation and Prediction Model Using Cloud Computing Over Multi-dimensional Dataset

Contact Info

Product

Resources

About