Data Mining Methodology for Engineering Applications (DMME)—A Holistic Extension to the CRISP-DM Model

Wiemer, Hajo; Drowatzky, Lucas; Ihlenfeldt, Steffen

doi:10.3390/app9122407

Cited by 42 publications

(31 citation statements)

References 8 publications

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“…DMME is also an extension of CRISP-DM by adding the step of technical understanding & conceptualization and technical realization & testing after business understanding step and before data understanding step. After the deployment step, DMME adds one additional step which is technical implementation [34].…”

Section: Data Mining Methodologymentioning

confidence: 99%

A Study Review of Common Big Data Architecture for Small-medium Enterprise

Septian¹,

Abdillah²,

Aliyudin³

2020

Proceedings of the Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 20

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This paper will be composed by conducting a document review and collect papers that related to big data solution and infrastructure. The survey is trying to cover the phases for building a common big data pipelines by following these structures: enterprise vision and mission that affect the big data architecture, defining the data sources, building the backend services to collect the datapoint from the data sources, utilize the data pipeline to prevent data loss, consuming the data pipeline message and deliver it to the data storage, build the raw data storage, define business-driven data visualization and analytics, build the ETL, build business-oriented data storage, applying statistical and machine learning, perform the business-driven visualization and implementing the monitoring for the big data pipelines. The survey will emphasize that many big data components could help the small-medium enterprise to tackle their big data operational issues.

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Section: Data Mining Methodologymentioning

confidence: 99%

A Study Review of Common Big Data Architecture for Small-medium Enterprise

Septian¹,

Abdillah²,

Aliyudin³

2020

Proceedings of the Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 20

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“…At the same time, the mass of UGC data overwhelms the means to extract timely, high-quality information-based insights that are meaningful, useful, efficient or applicable to managerial interventions [3][4][5][6]. Nevertheless, the desire to benefit from such data is driving researchers to seek new tools and analysis techniques that focus on identifying knowledge and insight generation [1,7].…”

Section: Introductionmentioning

confidence: 99%

How to Extract Meaningful Insights from UGC: A Knowledge-Based Method Applied to Education

2019

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New analysis and visualization techniques are required to glean useful insights from the vast amounts of data generated by new technologies and data sharing platforms. The aim of this article is to lay a foundation for such techniques so that the age of big data may also be the age of knowledge, visualization, and understanding. Education is the keystone area used in this study because it is deeply affected by digital platforms as an educational medium and also because it deals mostly with digital natives who use information and communication technology (ICT) for all manner of purposes. Students and teachers are therefore a rich source of user generated content (UGC) on social networks and digital platforms. This article shows how useful knowledge can be extracted and visualized from samples of readily available UGC, in this case the text published in tweets from the social network Twitter. The first stage employs topic-modeling using LDA (latent dirichlet allocation) to identify topics, which are then subjected to sentiment analysis (SA) using machine-learning (developed in Python). The results take on meaning through an application of data mining techniques and a data visualization algorithm for complex networks. The results obtained show insights related to innovative educational trends that practitioners can use to improve strategies and interventions in the education sector in a short-term future.

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“…The characteristics of multi-objective problems increase the computational difficulty of traditional single-objective scheduling, while data mining [11] could extract rules from a large dataset without 2 of 21 too much professional scheduling knowledge, which has more research space in solving MOJSSP and obtaining the optimal values of the objectives.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Machine Learning and Population Knowledge Mining Method to Minimize Makespan and Total Tardiness of Multi-Variety Products

Qiu

Zhang

2019

Applied Sciences

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Nowadays, the production model of many enterprises is multi-variety customized production, and the makespan and total tardiness are the main metrics for enterprises to make production plans. This requires us to develop a more effective production plan promptly with limited resources. Previous research focuses on dispatching rules and algorithms, but the application of the knowledge mining method for multi-variety products is limited. In this paper, a hybrid machine learning and population knowledge mining method to minimize makespan and total tardiness for multi-variety products is proposed. First, through offline machine learning and data mining, attributes of operations are selected to mine the initial population knowledge. Second, an addition–deletion sorting method (ADSM) is proposed to reprioritize operations and then form the rule-based initial population. Finally, the nondominated sorting genetic algorithm II (NSGA-II) hybrid with simulated annealing is used to obtain the Pareto solutions. To evaluate the effectiveness of the proposed method, three other types of initial populations were considered under different iterations and population sizes. The experimental results demonstrate that the new approach has a good performance in solving the multi-variety production planning problems, whether it is the function value or the performance metric of the acquired Pareto solutions.

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Data Mining Methodology for Engineering Applications (DMME)—A Holistic Extension to the CRISP-DM Model

Cited by 42 publications

References 8 publications

A Study Review of Common Big Data Architecture for Small-medium Enterprise

A Study Review of Common Big Data Architecture for Small-medium Enterprise

How to Extract Meaningful Insights from UGC: A Knowledge-Based Method Applied to Education

A Hybrid Machine Learning and Population Knowledge Mining Method to Minimize Makespan and Total Tardiness of Multi-Variety Products

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