Interactive analysis of product development experiments using On-line Analytical Mining

Do, Namchul; Bae, Sung-Moon; Park, Chulsoon

doi:10.1016/j.compind.2014.09.003

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…To do this, the output of the data extracted from the data cube is given as input to the Apriori algorithm and, finally, the patterns are intended to improve the quality of the garment. In [7], Namchul Do and his collaborators have been analyzed and evaluated by presenting an OLAM-based approach to product development processes. In this regard, using the data extracted from database of PDM (Product Data Management), firstly the defect Factors and functions' index are identified and after fulfilling the ETL process and creating OLAP cubes, the data is provided to enter the data mining and knowledge mining models.…”

Section: On-line Analytical Mining (Olam)mentioning

confidence: 99%

Knowledge Extraction and Analysis to Evaluate the Financial Performance of an Organization Using OLAM

Ebrahimi¹

2018

IJMECS

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Data mining or the discovery of knowledge out of databases extracts patterns, and useful and nonsubstantial, implicit, and unknown information from large databases. Searching for associative rules is a data mining method where relationships and dependencies Interactions between a large set of data items are shown. In large organizations, most data are created at the passage of time. Relational tables in a variety of business or scientific domains have rich information, with high quantative and nominal data types. Therefore, in order to gather information, measure performance and increase business efficiency, it is necessary to isolate relational database and store data in a data warehouse. Using the multidimensional data mining that integrates online analytical processing with data mining, knowledge can be found in multidimensional databases. In this article, to avoid inappropriate rules in exploring associative rules, the online analytical processing technique is combined with the Apriori algorithm and explores associative rules on multi-dimensional and multilevel data using the data cubic and Apriori algorithm. In addition, the criteria of accuracy, tool performance, and runtime are defined for comparing techniques for exploring associative rules. The metrics mentioned in the scope of this paper have been compared, and data from the Central Insurance Agency have been analyzed using data cubes and the Apriori algorithm, and useful association rules have been generated. Results of the research displays improvement of the response time compared to the other methods.

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Section: On-line Analytical Mining (Olam)mentioning

confidence: 99%

Knowledge Extraction and Analysis to Evaluate the Financial Performance of an Organization Using OLAM

Ebrahimi¹

2018

IJMECS

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“…Different proposed solutions are compared for each step of the data mining procedure, and while many high quality results are obtained for the modelling phase, the conclusion is that most of them lack data handling and data preprocessing solutions. The product development process was analysed based on collected data in Do, Bae, and Park (2015), but with the focus on gathering and transforming the data, while the exploitation is left to commercial systems. Product exploitation has been analysed in Liao, Ho, and Yang (2009) for the beverage market.…”

Section: Decision Support In Plmmentioning

confidence: 99%

An autonomous system for PLM domain data exploitation

Milicic¹,

Kadiri²,

Clobes

et al. 2015

International Journal of Computer Integrated Manufacturing

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In this article, a system for automated data analysis and data modelling is presented. It is a well-known fact that a fully automated, data independent data mining system cannot be designed. It is proposed here that data from the product lifecycle management (PLM) domain can be successfully modelled mathematically by exploiting the advantages of having a semantic model of data, relaxed time constraints and by allowing sub-optimal accuracy. A data mining procedure is followed through in detail and, for each step, it is argued how the proposed assumptions tackle the challenges of automation. Finally, an industrial use case is presented to demonstrate the value gained from such a system.

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“…The synergy of CCCC supports the increasingly demanding performance specifications of these applications and helps to face special situations like unexpected condition adaptations, human interaction challenges, and goal conflicts. Practical industrial applications of the synergy of CCCC are cyber-physical systems [1][2][3][4][5], networked control systems [6][7][8][9][10], mechatronics systems [11][12][13][14][15], online quality control of production items [16][17][18][19][20], supervision and failure analysis of dynamically changing machine states [21][22][23][24], decision support systems [25][26][27][28][29], prediction and control in dynamic production processes [30][31][32], welding processes [33,34], user profiling [35,36], process monitoring [37][38][39], web based control of information management flows [40,41], and resilient control architectures [42][43][44]…”

mentioning

confidence: 99%

Synergy of computers, cognition, communication and control with industrial applications

Precup

Hellendoorn

Angelov

2015

Computers in Industry

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Interactive analysis of product development experiments using On-line Analytical Mining

Cited by 7 publications

References 9 publications

Knowledge Extraction and Analysis to Evaluate the Financial Performance of an Organization Using OLAM

Knowledge Extraction and Analysis to Evaluate the Financial Performance of an Organization Using OLAM

An autonomous system for PLM domain data exploitation

Synergy of computers, cognition, communication and control with industrial applications

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