Statistical and Computational Needs for Big Data Challenges

2022 ◽  
pp. 632-654
Author(s):  
Soraya Sedkaoui
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Statistical Processing ◽  
Maximum Amount ◽  
Computing Environment ◽  
Amount Of Information ◽  
Analysis Methods ◽  
Computational Data

The traditional way of formatting information from transactional systems to make them available for “statistical processing” does not work in a situation where data is arriving in huge volumes from diverse sources, and where even the formats could be changing. Faced with this volume and diversification, it is essential to develop techniques to make best use of all of these stocks in order to extract the maximum amount of information and knowledge. Traditional analysis methods have been based largely on the assumption that statisticians can work with data within the confines of their own computing environment. But the growth of the amounts of data is changing that paradigm, especially which ride of the progress in computational data analysis. This chapter builds upon sources but also goes further in the examination to answer this question: What needs to be done in this area to deal with big data challenges?

Statistical and Computational Needs for Big Data Challenges

2018 ◽  
pp. 21-53 ◽  
Author(s):  
Soraya Sedkaoui
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Statistical Processing ◽  
Maximum Amount ◽  
Computing Environment ◽  
Amount Of Information ◽  
Analysis Methods ◽  
Computational Data

The traditional way of formatting information from transactional systems to make them available for “statistical processing” does not work in a situation where data is arriving in huge volumes from diverse sources, and where even the formats could be changing. Faced with this volume and diversification, it is essential to develop techniques to make best use of all of these stocks in order to extract the maximum amount of information and knowledge. Traditional analysis methods have been based largely on the assumption that statisticians can work with data within the confines of their own computing environment. But the growth of the amounts of data is changing that paradigm, especially which ride of the progress in computational data analysis. This chapter builds upon sources but also goes further in the examination to answer this question: What needs to be done in this area to deal with big data challenges?

scholarly journals Social Network Big Data Hierarchical High-Quality Node Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Dongning Jia ◽  
Bo Yin ◽  
Xianqing Huang
Keyword(s):  
Big Data ◽  
Social Network ◽  
Data Analysis ◽  
Shopping Mall ◽  
Network Data ◽  
Clustering Methods ◽  
Learning Mechanisms ◽  
Social Network Data ◽  
Analysis Methods ◽  
Network Big Data

Compared with the conventional network data analysis, the data analysis based on social network has a very clear object of analysis, various forms of analysis, and more methods and contents of analysis. If the conventional analysis methods are applied to social network data analysis, we will find that the analysis results do not reach our expected results. The results of the above studies are usually based on statistical methods and machine learning methods, but some systems use other methods, such as self-organizing self-learning mechanisms and concept retrieval. With regard to the current data analysis methods, data models, and social network data, this paper conducts a series of researches from data acquisition, data cleaning and processing, data model application and optimization of the model in the process of application, and how the formed data analysis results can be used for managers to make decisions. In this paper, the number of customer evaluations, the time of evaluation, the frequency of evaluation, and the score of evaluation are clustered and analyzed, and finally, the results obtained by the two clustering methods applied in the analysis process are compared to build a customer grading system. The analysis results can be used to maintain the current Amazon purchase customers in a hierarchical manner, and the most valuable customers need to be given key attention, combining social network big data with micro marketing to improve Amazon’s sales performance and influence, developing from the original single shopping mall model to a comprehensive e-commerce platform, and cultivating their own customer base.

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.

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

The Potential of Big Data Analysis in the Shipbuilding Industry: A Way of Increasing Competitiveness

2021 ◽  
pp. 53-74
Author(s):  
Andrea Cappelli ◽  
Iacopo Cavallini
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Value Chain ◽  
Information Needs ◽  
Demand Forecasting ◽  
Big Data Analysis ◽  
Production Costs ◽  
Shipbuilding Industry ◽  
Analysis Methods

It is possible to exploit potentials of Big Data in the shipbuilding industry in order to increase efficiency and company performance. Big Data analysis will probably have a great impact on strengthening the competitiveness in the whole sector, providing various types of benefits and effective support to the decision-making system. Academics maintain that analysis methods and algorithms can offer spe-cific guidelines to managers and practitioners in order to satisfy their information needs. Even though it is recognized that the techniques for Big Data analysis are relevant, only a few studies provide practical guidelines on how to apply these techniques in specific industries like shipbuilding. This preliminary study aims to develop a conceptual framework of Big Data anal-ysis based on the value chain approach. By using a deductive methodology, the framework is built taking into consideration four phases of the value chain in the shipbuilding industry - i.e. pre-production, design, production, and post-production. For its relevance, the study considers the pre-production phase, trying to classify data sources, analysis methods, and algorithms for the main activities of this node and also providing various suggestions to shipbuilding managers and practitioners. The researchers develop the framework by considering secondary data collected from the literature analysis. Our results can successfully support decision making in shipbuilding companies, making processes and operations more cost-effective and helping companies be more competitive. Specifically, in the pre-production node this will lead to real-time demand forecasting and a more reliable estimation of initial production costs.

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
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