DATA MINING AS A MEANS OF QUALITY CONTROL IN PRODUCTION AND ORGANIZATION MANAGEMENT

2020 ◽  
Vol 1 (5) ◽  
pp. 130-138
Author(s):  
L. S. ZVYAGIN ◽  
Keyword(s):  
Data Mining ◽  
Quality Control ◽  
New Technologies ◽  
Data Interpretation ◽  
Organization Management

The article deals with data mining (IAD), which is widely used both in business and in various studies. IAD methods are used to create new ways to solve problems of forecasting, segmentation, data interpretation, etc. The problems to be solved by creating new technologies and methods of IAD are analyzed.

scholarly journals Predicting Academic Course Preference using Hadoop

2019 ◽  
Vol 8 (4) ◽  
pp. 2527-2530
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
New Technologies ◽  
Parallel Applications ◽  
Distributed File System ◽  
Industrial Training ◽  
Effective Learning ◽  
Meaningful Information ◽  
Learning Techniques ◽  
Mining Tools

These days new technologies have been introduced by this new academic trends also have been came into existence into the education system. And this leads to huge amounts of data which makes a big challenge for the students to store the preferred course. For this many data mining tools have been invented to convert the unregulated data into structured format to understand the meaningful information. As we know that Hadoop is a distributed file system which is used to hold huge amounts of data this stores the files in a redundant fashion across multiple machines. Due to this it leads to failure and parallel applications do not work. To avoid this problem we are using Mapreduce for decision making of students in order to choose their preferred course for industrial training purpose for their effective learning techniques to increase their knowledge and capability.

Comparative proteomic analysis of hybrid maize MR2008 and its parental lines

2021 ◽  
Vol 159 (7-8) ◽  
pp. 570-579
Author(s):  
E. E. Osawa-Martínez ◽  
B. Minjarez ◽  
Y. Rodríguez-Yáñez ◽  
E. E. Reza-Zaldivar ◽  
A. A. Canales-Aguirre ◽  
...  
Keyword(s):  
Quality Control ◽  
Metabolic Pathways ◽  
New Technologies ◽  
Bioinformatics Analysis ◽  
Agricultural Practices ◽  
White Variety ◽  
Comparative Proteomic Analysis ◽  
The World ◽  
Parental Lines ◽  
Hybrid Maize

AbstractMaize is one of the three staple foods in the world. The white variety represents 60% of the maize importation with a world consumption of 1125 million tons in 2019/2020. Currently, new technologies could contribute to the analysis of this seed, supporting quality control and improvement. This study aims to carry out the morphological and proteomic comparison between the hybrid MR2008 and its parental lines LUG03 and CML491 through mass spectrometry and bioinformatics analysis. Herein, we identified that 34.8% of the hybrid proteome differs from the parental proteome. Also, ontological and morphological analyses determined that the hybrid exhibits more characteristics related to CML491 than LUG03, for example, metabolic pathways and enzymes, such as anthocyanidin 3-O-glucosyltransferase (UniProt P16166). This analysis allowed the identification of dominant characters, metabolic pathways and confirms the utility of this methodology in agricultural practices, mainly in processes of selection and quality control of a crop.

Data Mining

2011 ◽  
pp. 921-926
Author(s):  
Sherry Y. Chen ◽  
Xiaohui Liu
Keyword(s):  
Data Mining ◽  
Electronic Commerce ◽  
New Technologies ◽  
Research Area ◽  
Future Research ◽  
Data Mining Techniques ◽  
Wide Range ◽  
Degree Of Confidence ◽  
Common Application ◽  
Existing Data

There is an explosion in the amount of data that organizations generate, collect, and store. Organizations are gradually relying more on new technologies to access, analyze, summarize, and interpret information intelligently. Data mining, therefore, has become a research area with increased importance (Amaratunga & Cabrera, 2004). Data mining is the search for valuable information in large volumes of data (Hand, Mannila, & Smyth, 2001). It can discover hidden relationships, patterns, and interdependencies and generate rules to predict the correlations, which can help the organizations make critical decisions faster or with a greater degree of confidence (Gargano & Ragged, 1999). There is a wide range of data mining techniques, which has been successfully used in many applications. This article is an attempt to provide an overview of existing data mining applications. The article begins by explaining the key tasks that data mining can achieve. It then moves to discuss applications domains that data mining can support. The article identifies three common application domains, including bioinformatics, electronic commerce, and search engines. For each domain, how data mining can enhance the functions will be described. Subsequently, the limitations of current research will be addressed, followed by a discussion of directions for future research.

Biological Data Mining

2008 ◽  
pp. 1696-1705
Author(s):  
George Tzanis ◽  
Christos Berberidis ◽  
Ioannis Vlahavas
Keyword(s):  
Data Mining ◽  
New Technologies ◽  
Noisy Data ◽  
Large Data ◽  
Biological Data ◽  
Customer Relationship ◽  
Mass Storage ◽  
Stock Management ◽  
Management Fraud ◽  
Storage Media

At the end of the 1980s, a new discipline named data mining emerged. The introduction of new technologies such as computers, satellites, new mass storage media, and many others have lead to an exponential growth of collected data. Traditional data analysis techniques often fail to process large amounts of, often noisy, data efficiently in an exploratory fashion. The scope of data mining is the knowledge extraction from large data amounts with the help of computers. It is an interdisciplinary area of research that has its roots in databases, machine learning, and statistics and has contributions from many other areas such as information retrieval, pattern recognition, visualization, parallel and distributed computing. There are many applications of data mining in the real world. Customer relationship management, fraud detection, market and industry characterization, stock management, medicine, pharmacology, and biology are some examples (Two Crows Corporation, 1999).

Biological Data Mining

2005 ◽  
pp. 35-41 ◽  
Author(s):  
George Tzanis ◽  
Christos Berberidis ◽  
Ioannis Vlahavas
Keyword(s):  
Data Mining ◽  
New Technologies ◽  
Noisy Data ◽  
Large Data ◽  
Biological Data ◽  
Customer Relationship ◽  
Mass Storage ◽  
Stock Management ◽  
Management Fraud ◽  
Storage Media

At the end of the 1980s, a new discipline named data mining emerged. The introduction of new technologies such as computers, satellites, new mass storage media, and many others have lead to an exponential growth of collected data. Traditional data analysis techniques often fail to process large amounts of, often noisy, data efficiently in an exploratory fashion. The scope of data mining is the knowledge extraction from large data amounts with the help of computers. It is an interdisciplinary area of research that has its roots in databases, machine learning, and statistics and has contributions from many other areas such as information retrieval, pattern recognition, visualization, parallel and distributed computing. There are many applications of data mining in the real world. Customer relationship management, fraud detection, market and industry characterization, stock management, medicine, pharmacology, and biology are some examples (Two Crows Corporation, 1999).

scholarly journals Quality Control of Biomarkers: From the Samples to Data Interpretation

10.5772/51555 ◽  
2012 ◽  
Author(s):  
F. G. ◽  
D. M. ◽  
A. L. C. Faria ◽  
S. B. ◽  
D. N. ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Interpretation

scholarly journals A Case Study on the Particularities and Sustainability of the Concepts of TQM, Quality Control, and Risk Management in the Corporate Insurance Industry: Loss and the Incidence of Catastrophic Risks

Proceedings ◽  
2020 ◽  
Vol 63 (1) ◽  
pp. 3
Author(s):  
Narcis Sebastian Păvălașcu ◽  
Manuela Rozalia Gabor
Keyword(s):  
Risk Management ◽  
Quality Control ◽  
New Technologies ◽  
Insurance Industry ◽  
Press Release ◽  
Management Systems ◽  
Catastrophic Risks ◽  
Trade Wars ◽  
Corporate Insurance

The development of quality control and risk management systems is a priority for any industry and especially for the corporate insurance industry. Defective product and work incidents represent 14% of the total number of insurance claims, serving as the main loss of liability for businesses. According to a Allianz Global Corporate and Specialty press release, the cyber risks and impact of new technologies will have an increasing influence on the landscape of corporate losses in the coming years. Our results from this study conclude that the emerging business risks for the next 3–4 years are as follows: cyber incidents, 48%; new technologies, 30%; and changes in legislations/regulations, 28% (i.e., the present pandemic cause by COVID-19, the Brexit, trade wars, and tariffs etc.).

scholarly journals Best Paper Selection

2017 ◽  
Vol 26 (01) ◽  
pp. 70-71
Keyword(s):  
Public Health ◽  
Data Mining ◽  
Quality Control ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Systematic Approach ◽  
Biomedical Data ◽  
Control Methods ◽  
Data Quality Control ◽  
Health Records

Chen J, Podchiyska T, Altman R. OrderRex: clinical order decision support and outcome predictions by data-mining electronic medical records. J Am Med Inform Assoc 2016;23:339-48 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5009921/ Miotto R, Li L, Kidd BA, Dudley JT. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records. Sci Rep 2016;6:26094 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4869115/ Prasser F, Kohlmayer F, Kuhn KA. The Importance of Context: Risk-based De-identification of Biomedical Data. Methods Inf Med 2016;55:347-55 https://methods.schattauer.de/en/contents/archivestandard/issue/2382/manuscript/25994.ht Saez C, Zurriaga O, Perez-Panades J, Melchor I, Robles M, Garcia-Gomez JM. Applying probabilistic temporal and multisite data quality control methods to a public health mortality registry in Spain: a systematic approach to quality control of repositories. J Am Med Inform Assoc 2016;23:1085-95 https://academic.oup.com/jamia/article-lookup/doi/10.1093/jamia/ocw010

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