scholarly journals USAGE OF ADVANCED DATA ANALYSIS IN AUSTRIAN INDUSTRIAL COMPANIES

2021 ◽  
Vol 2 ◽  
pp. 125-131
Author(s):  
Martin Wallner ◽  
Tomáš Peráček
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Data ◽  
Complex Data ◽  
Small Companies ◽  
High Turnover ◽  
Industrial Companies ◽  
Analysis Methods ◽  
It Department ◽  
Data Analysis Methods

Data has become one of the most valuable resources for companies. The large data volumes of Big Data projects allow institutions the application of various data analysis methods. Compared to older analysis methods, which mostly have an informative function, predictive and prescriptive analysis methods allow foresight and the prevention of future problems and errors. This paper evaluates the current state of advanced data analysis in Austrian industrial companies. Furthermore, it investigates if the advantages of complex data analyses can be monetarized and if cooperate figures such as the turnover or company size influence the answers of the survey. For that reason, a survey among industrial companies in Austria was performed to assess the usage of complex data analysis methods and Big Data. It is shown that small companies use descriptive and diagnostic analysis methods, while big companies use more advanced analytical methods. Companies with a high turnover are also more likely to perform Big Data projects. On an international comparison for most Austrian industrial companies, Big Data is not the main focus of their IT department. Also, modern data architectures are not as extensively implemented as in other countries of the DACH region. However, there is a clear perception by Austrian industrial companies that forward-looking data analysis methods will be predominant in five years.

scholarly journals Use of Data Analysis Methods in Dental Publications: Is There Evidence of a Methodological Change?

Publications ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 9 ◽  
Author(s):  
Pentti Nieminen ◽  
Hannu Vähänikkilä
Keyword(s):  
Data Analysis ◽  
Clinical Care ◽  
Statistical Significance ◽  
Mining Machine ◽  
Complex Data ◽  
Dental Research ◽  
Analysis Methods ◽  
Study Characteristics ◽  
Conducting Research ◽  
Data Analysis Methods

Objectives: To evaluate how data analysis methods in dental studies have changed in recent years. Methods: A total of 400 articles published in 2010 and 2017 in five dental journals, Journal of Dental Research, Caries Research, Community Dentistry and Oral Epidemiology, Journal of Dentistry, and Acta Odontologica Scandinavica, were analyzed. The study characteristics and the reporting of data analysis techniques were systematically identified. Results: The statistical intensity of the dental journals did not change from 2010 to 2017. Dental researchers did not adopt the data mining, machine learning, or Bayesian approaches advocated in the computer-oriented methodological literature. The determination of statistical significance was the most generally used method for conducting research in both 2010 and 2017. Observational study designs were more common in 2017. Insufficient and incomplete descriptions of statistical methods were still a serious problem. Conclusion: The stabilization of statistical intensity in the literature suggests that papers applying highly computationally complex data analysis methods have not meaningfully contributed to dental research or clinical care. Greater rigor is required in reporting the methods in dental research articles, given the current pervasiveness of failure to describe the basic techniques used.

Analysis of digital technologies in the agri-food sector based on big data analysis methods

2021 ◽  
pp. 334-344
Author(s):  
Yulia Sergeevna Otmakhova ◽  
Dmitry Alexeevich Devyatkin ◽  
Natalia Ivanovna Usenko
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Digital Technologies ◽  
Big Data Analysis ◽  
Food Sector ◽  
Analysis Methods ◽  
Data Analysis Methods

Modelling and simulation for metabolomics data analysis

2005 ◽  
Vol 33 (6) ◽  
pp. 1427-1429 ◽  
Author(s):  
P. Mendes ◽  
D. Camacho ◽  
A. de la Fuente
Keyword(s):  
Data Analysis ◽  
Synthetic Data ◽  
Network Models ◽  
Large Data ◽  
Biochemical Network ◽  
Data Sets ◽  
Metabolomics Data ◽  
Analysis Methods ◽  
True Knowledge ◽  
Data Analysis Methods

The advent of large data sets, such as those produced in metabolomics, presents a considerable challenge in terms of their interpretation. Several mathematical and statistical methods have been proposed to analyse these data, and new ones continue to appear. However, these methods often disagree in their analyses, and their results are hard to interpret. A major contributing factor for the difficulties in interpreting these data lies in the data analysis methods themselves, which have not been thoroughly studied under controlled conditions. We have been producing synthetic data sets by simulation of realistic biochemical network models with the purpose of comparing data analysis methods. Because we have full knowledge of the underlying ‘biochemistry’ of these models, we are better able to judge how well the analyses reflect true knowledge about the system. Another advantage is that the level of noise in these data is under our control and this allows for studying how the inferences are degraded by noise. Using such a framework, we have studied the extent to which correlation analysis of metabolomics data sets is capable of recovering features of the biochemical system. We were able to identify four major metabolic regulatory configurations that result in strong metabolite correlations. This example demonstrates the utility of biochemical simulation in the analysis of metabolomics data.

Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods

2017 ◽  
Vol 207 ◽  
pp. 354-362 ◽  
Author(s):  
Yang Zhao ◽  
Peng Liu ◽  
Zhenpo Wang ◽  
Lei Zhang ◽  
Jichao Hong
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Electric Vehicles ◽  
Big Data Analysis ◽  
Analysis Methods ◽  
Defect Diagnosis ◽  
Data Analysis Methods

scholarly journals Exploratory data analysis on large data sets: The example of salary variation in Spanish Social Security Data

2020 ◽  
pp. 234094442095733
Author(s):  
Catia Nicodemo ◽  
Albert Satorra
Keyword(s):  
Data Analysis ◽  
Social Security ◽  
Gender Gap ◽  
Exploratory Data Analysis ◽  
Large Data ◽  
Jel Classification ◽  
Small Samples ◽  
Analysis Methods ◽  
Exploratory Data ◽  
Data Analysis Methods

New challenges arise in data visualization when the research involves a sizable database. With many data points, classical scatterplots are non-informative due to the cluttering of points. On the contrary, simple plots, such as the boxplot that are of limited use in small samples, offer great potential to facilitate group comparison in the case of an extensive sample. This article presents exploratory data analysis methods useful for inspecting variation across groups in crucial variables and detecting heterogeneity. The exploratory data analysis methods (introduced by Tukey in his seminal book of 1977) encompass a set of statistical tools aimed to extract information from data using simple graphical tools. In this article, some of the exploratory data analysis methods like the boxplot and scatterplot are revisited and enhanced using modern graphical computational devices (as, for example, the heat-map) and their use illustrated with Spanish Social Security data. We explore how earnings vary across several factors like age, gender, type of occupation, and contract, and in particular, the gender gap in salaries is visualized in various dimensions relating to the type of occupation. The exploratory data analysis methods are also applied to assessing and refining competing regressions by plotting residuals-versus-fitted values. The methods discussed should be useful to researchers to assess heterogeneity in data, across-group variation, and classical diagnostic plots of residuals from alternative models fits. JEL CLASSIFICATION: C55; J01; J08; Y10; C80

scholarly journals Application of Artificial Intelligence Wearable Technology in the Big Data Analysis of Physical Activity in China

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hao Cui ◽  
Zhiqiang Peng
Keyword(s):  
Physical Activity ◽  
Big Data ◽  
Energy Consumption ◽  
Data Analysis ◽  
Wearable Devices ◽  
Big Data Analysis ◽  
Analysis Methods ◽  
Detection Technology ◽  
Smart Wearable ◽  
Data Analysis Methods

Although the validity of the physical activity questionnaire is low, the questionnaire is still the most commonly used measurement tool for physical activity research in China in the past 10 years. In the era of big data, research in the field of physical activity in China needs to be more effective, economical, convenient, and suitable for long-term, large-sample research tools. Acceleration detection technology, heart rate detection technology, and GPS technology are the mainstream technologies for measuring the energy consumption of physical activity in wearable devices. The application of data mining and machine learning methods further enhances the validity of the test. Domestic smart wearable devices are not effective in estimating energy consumption but still have a large space for technical improvement. Smart wearable devices have a very broad application prospect in the field of big data research in physical activity. The impact of smart wearable device technology and big data analysis methods on physical activity research will be far-reaching and may lead to major changes in research concepts, research tools, and data analysis methods.

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