scholarly journals Retraction: Rezig, S. et al. (2018). Using Data-Mining Methods for Predicting Sequential Maintenance Activities. Applied Sciences, 8(11), 2184

2019 ◽  
Vol 9 (4) ◽  
pp. 633
Author(s):  
Sadok Rezig ◽  
Zied Achour ◽  
Nidhal Rezg
Keyword(s):  
Applied Sciences ◽  
Data Mining ◽  
Research Project ◽  
Mining Methods ◽  
Maintenance Activities ◽  
Using Data ◽  
The University ◽  
Sequential Maintenance

After the results from a research project with former students of the university were published in Applied Sciences [...]

scholarly journals Using Data Mining Methods for Predicting Sequential Maintenance Activities

2018 ◽  
Vol 8 (11) ◽  
pp. 2184 ◽  
Author(s):  
Sadok Rezig ◽  
Zied Achour ◽  
Nidhal Rezg
Keyword(s):  
Data Mining ◽  
Spare Parts ◽  
Threshold Values ◽  
Data Mining Approach ◽  
History Of ◽  
Maintenance Activities ◽  
Using Data ◽  
Subsequent Failure ◽  
Sequential Maintenance

A data mining approach is integrated in this work for predictive sequential maintenance along with information on spare parts based on the history of the maintenance data. For most practical problems, the simple failure of one part of a given piece of equipment induces the subsequent failure of the other parts of said equipment. For example, it is frequently observed in mining industries that, like many other industries, the maintenance of conventional equipment is carried out in sequence. Besides, depending on the state of parts of the equipment, many parts can be consumed and replaced. Consequently, with a group of spare parts consumed sequentially in various maintenance activities, it is possible to discover sequential maintenance activities. From maintenance data with predefined support or threshold values and spare parts information, this work determines the sequential patterns of maintenance activities. The proposed method predicts the occurrence of the next maintenance activity with information on the consumed spare parts. An industrial real case study is presented in this paper and it is well-noticed that our experimental results shed new light on the maintenance prediction using data mining.

Data Mining zur Nacharbeitsdauerprognose*/Data Mining-based forecasting of rework duration - Predictive rework control and work process optimization in the automotive assembly

2017 ◽  
Vol 107 (10) ◽  
pp. 773-778
Author(s):  
S. Krzoska ◽  
M. Eickelmann ◽  
J. Schmitt ◽  
J. Prof. Deuse
Keyword(s):  
Data Mining ◽  
Process Optimization ◽  
Quality Data ◽  
Operating Process ◽  
Automotive Assembly ◽  
Related Quality ◽  
Mining Methods ◽  
Product And Process ◽  
Using Data ◽  
Manufacturing Execution

Der Fachbeitrag zeigt am Beispiel der Nacharbeitssteuerung und Arbeitsprozessoptimierung in der Automobilmontage, wie produkt- und prozessbezogene Qualitätsdaten durch den Einsatz von Data Mining-Methoden analysiert sowie effizient genutzt werden können. Dazu wurden Daten aus Manufacturing-Execution-Systemen (MES) mithilfe von Regressionsbäumen zur Entwicklung einer fahrzeugspezifischen Nacharbeitsdauerprognose ausgewertet. Das grundlegende Data Mining-Konzept sowie die Pilotierungsergebnisse werden nachfolgend dargestellt.   The article shows at the example of rework control and operating process optimization in the car assembly how recorded product- and process-related quality data can be analyzed and used efficiently by using Data Mining-methods. With data from MES-systems regression trees were built for a vehicle-specific rework duration forecast. The basic concept and validation results will be presented below.

scholarly journals A Survey of Anomaly Detection Using Data Mining Methods for Hypertext Transfer Protocol Web Services

2015 ◽  
Vol 11 (1) ◽  
pp. 89-97 ◽  
Author(s):  
Mohsen Kakavand ◽  
Norwati Mustapha ◽  
Aida Mustapha ◽  
Mohd Taufik Abdullah ◽  
Hamed Riahi
Keyword(s):  
Data Mining ◽  
Web Services ◽  
Anomaly Detection ◽  
Hypertext Transfer Protocol ◽  
Mining Methods ◽  
Using Data ◽  
Transfer Protocol

scholarly journals Using Data Mining for Improving Education of University Students – A Survey

2018 ◽  
Vol 6 (9) ◽  
pp. 78-83 ◽  
Author(s):  
K. P. S. D. Kumarapathirana
Keyword(s):  
Data Mining ◽  
Academic Success ◽  
Student Retention ◽  
Student Performance ◽  
Poor Performance ◽  
Academic Behavior ◽  
System Logs ◽  
Using Data ◽  
Visualization Techniques ◽  
The University

Data mining combines machine learning, statistical and visualization techniques to discover and extract knowledge. Student retention is an indicator of academic performance and enrolment management of the university. Poor student retention could reflect badly on the university. Universities are facing the immense and quick growth of the volume of educational data stored in different types of databases and system logs. Moreover, the academic success of students is another major issue for the management in all professional institutes. So the early prediction to improve the student performance through counseling and extra coaching will help the management to take timely action for decrease the percentage of poor performance by the students. Data mining can be used to find relationships and patterns that exist but are hidden among the vast amount of educational data. This survey conducts a literature survey to identify data mining technologies to monitor student, analyze student academic behavior and provide a basis for efficient intervention strategies. The results can be used to develop a decision support system and help the authorities to timely actions on weak students.

Observation of Success Status of Employees in E-Learning Courses in Organizations with Data Mining

2017 ◽  
Vol 9 (1) ◽  
pp. 38-49
Author(s):  
Fatma Önay Koçoğlu ◽  
İlkim Ecem Emre ◽  
Çiğdem Selçukcan Erol
Keyword(s):  
Data Mining ◽  
Evaluation Criteria ◽  
Data Set ◽  
Completion Status ◽  
Completion Date ◽  
E Learning ◽  
Pharmaceutical Industries ◽  
Mining Methods ◽  
Using Data ◽  
Performance Results

The aim of this study is to analyze success in e-learning with data mining methods and find out potential patterns. In this context, 374.073 data of 2013-14 period taken from an institution serving in e-learning field in Turkey are used. Data set, which is collected from information technology, banking and pharmaceutical industries, includes success and industry of employees', trainings which they complete, whether the trainings are completed, first login and last logout dates, training completion date and duration of experience in training. Using this data set, success status of participants is observed by using data mining methods (C5.0, Random Forest and Gini). By observing using accuracy, error rate, specificity and f- score from performance evaluation criteria, C5.0 has chosen the algorithm which gives the best performance results. According to the results of the study, it has been determined that the sectors of the employees are not important, on the contrary the ones that are important are the completion status, the duration of experience and training.

Sign in / Sign up

Export Citation Format

Share Document