Big Data-based Analysis Research Articles

Physicochemical reaction mechanism study and model optimization based on big data analysis

The huge amount of data generated by information technology, intelligence and automation has serious redundancy and duplication, which becomes a hidden cost of consuming resources. In the field of physicochemical analysis, for the explanation of mechanism, the analysis of big data can provide reference and integration. The purpose of big data-based analysis is to remove redundancies and inconsistencies in the given raw characterization data to better improve its completeness and accuracy. The paper takes the density matrix of quantum bits, which is different from the classical real number representation, etc. as the representation of feature data, and investigates the cleaning of feature data based on quantum representation from the perspective of the intersection of theoretical physics and computer science. This paper comprehensively describes the basic theories and methods of data mining. On the basis of understanding and analyzing a variety of data mining techniques, it focuses on the BP neural network-based data mining techniques to provide in-depth analysis and elaboration. Further, this paper proposes an improved algorithm with multi-algorithm advantage integration and joint optimization for the deficiencies in the BP neural network algorithm, which is fully demonstrated in the nonlinear function simulation. In addition, based on the HA-BP algorithm, this paper has designed anomaly data detection model and variable factor analysis model, and their reliability and practicality are also fully demonstrated in the nonlinear function simulation in the analytical study of physicochemical reaction mechanism.

Rail wear rate on the Belgian railway network – a big-data analysis

ABSTRACT This paper presents a big data-based analysis of the rail wear of the whole Belgian railway network measured in 2012 and 2019. Wear rates are reported, discussed, and quantitatively formulated as functions of critical factors in terms of curve radius, annual tonnage (rail age), high rail in curves, an average from both rails in straight tracks at rail top (vertical wear) and gauge corner (45° wear) and for steel grade R200 and R260. The influence of preventive grinding is also analysed. The wear rates are derived in an aggregated manner for the whole network. The wear rates do not show significant change with changes in rolling stock over the years, implying that the wear rates could also hold for other networks. It is found that R200 shows, on average, a 34% higher wear rate than R260. Also, the wear rate per tonnage is lower for high-loaded tracks. Thus, time is a relevant factor in explaining the wear evolution of low-loaded tracks; for instance, the effect of corrosion may have an important role. The paper provides statistically significant information that can be used for wear modelling, understanding and treating rolling contact fatigue based on the wear rate and developing tailored rail maintenance strategies.

기후변화와 자연재해 이슈에 대한 빅데이터 분석과 함의

This paper aims to analyze key issues and characteristics of environmental policy over time through big data-based analysis on climate change and natural disasters. To achieve this, articles from 1900 to August 2022 are extracted and text mining analysis is conducted. Methodologically, text mining is a tool for extracting useful information from electronic (Documents Text Data) such as web pages, and is used in various ways such as extracting major (Keywords Word Cloud), network analysis, and topic analysis. In this study, it is analyzed through word cloud analysis and simultaneous network analysis through simple frequency analysis. The results show that the frequency of natural disasters caused by climate change has been highly increasing, and the damages are caused mainly by localized torrential rains. It implies that natural disaster damage caused by climate change are diversifying, so that countermeasures should be prepared to reduce future damages related to climate change.

Big data-assisted urban governance: An intelligent real-time monitoring and early warning system for public opinion in government hotline

The implementation of big data-based analysis, conducting forecasts, and increasing service capabilities in smart government affairs can effectively enhance the efficiency of urban emergency management. The government hotline is an effective citizen relationship management (CRM) tool that generates a large amount of appeal information every day. Identifying and predicting public opinion hotspots of citizen complaints in real time and accurately identifying periodic and mass incidents present major challenges to city managers. In this study, we propose a pattern-based identification and early warning system for public opinion. This system uses an improved mining algorithm for frequent patterns to accurately identify topics and their corresponding case information and adopts a word-weight method to assign weights to frequent patterns, such that more important information would have a higher weight. Cosine similarity is used to calculate a similarity matrix for frequent patterns in the text content, thereby accurately distinguishing between repeated events and events of major interest. Moreover, a hash table-based retrieval and improved text rank are proposed to extract text summaries. Finally, we define sudden issues of major interest and develop an identification and early warning system for public opinion that is easy to operate, with effective user interaction and data visualization interfaces. A real case study is implemented to experimentally identify public opinion accurately and perform early. The average accuracy rate of data mining reached 87.95% in the first half of the operational evaluation of the system. Besides, when compared with the analysis of the conventional SQL statements, the retrieval efficiency is improved by 6 times and supports multi-keyword retrieval. Consequently, the enhanced text rank summary extraction algorithm improves p@10 accuracy by 6%.

Big Data-Based Analysis of Characteristics of Consumption Sales by Region and Age : Focused on Seoul

Big Data-Based Analysis of Characteristics of Consumption Sales by Region and Age : Focused on Seoul

Analysis on the Membership Management of a Fashion Brand by Big Data Technology

This paper makes multi-dimensional big data analysis on the membership data of a well-known women’s bag fashion brand in China. We use a combination of data cleaning, data visualization, data classification, correlation analysis and data prediction. Based on these analyses, we found eight business insights and made twelve suggestions for business development of the fashion brand. These suggestions include both business development and strategic aspects, as well as suggestions for the upgrading of the member management system. The member management system of the fashion brand has not been very effective maintenance, for example, a lot of data is incomplete, which has a wide range of representation in the member management of Chinese enterprises. Our big data-based analysis method is of great significance for using these incomplete member data to guide the development of the business.

Environmental benefits of bike sharing: A big data-based analysis

Bike sharing is a new form of transport and is becoming increasingly popular in cities around the world. This study aims to quantitatively estimate the environmental benefits of bike sharing. Using big data techniques, we estimate the impacts of bike sharing on energy use and carbon dioxide (CO2) and nitrogen oxide (NOX) emissions in Shanghai from a spatiotemporal perspective. In 2016, bike sharing in Shanghai saved 8358 tonnes of petrol and decreased CO2 and NOX emissions by 25,240 and 64 tonnes, respectively. From a spatial perspective, environmental benefits are much higher in more developed districts in Shanghai where population density is usually higher. From a temporal perspective, there are obvious morning and evening peaks of the environmental benefits of bike sharing, and evening peaks are higher than morning peaks. Bike sharing has great potential to reduce energy consumption and emissions based on its rapid development.