Application Of Big Data Mining Research Articles

Application and Feasibility Analysis of Network Models and Data Analysis in Combating Organized Crime

This article discusses and analyzes in detail the development potential and application of big data mining and network visualization analysis in combating organized crime. Firstly, the article emphasizes the importance of combating criminal gangs and organized crime in modern society, comparing them to the foundation of stable national development and the cornerstone of national progress. Then it explores the security challenges faced by modern society and more issues related to organized crime, including the problem of crime caused by the wealth gap caused by urbanization and economic development, as well as the outdated technology for cracking down on existing security crimes. This article provides a detailed introduction to the potential of big data mining and network visualization, as well as the advantages of these technologies in practical applications. It covers the collection, processing, sharing, and application of data. At the same time, network visualization is used to visualize crime data, helping decision-makers better understand the current situation and problems of public security and organized crime. The article also discusses the application of artificial intelligence and mobile internet technology in combating organized crime. And the potential of big data in improving urban governance. Finally, this article looks ahead and emphasizes that big data mining and network visualization analysis will continue to play an important role in optimizing social security, indicating the broad prospects of these technologies in the future transportation field.

Emotional analysis of evaluation discourse in business English translation based on language big data mining of public health environment.

This paper conducts sentiment analysis on the evaluation discourse of business English translation based on language big data mining of public health environment, and aims to find a reasonable algorithm to conduct detailed research on all aspects of sentiment analysis. This paper focuses on three areas of sentiment information, extraction, sentiment information retrieval, and sentiment information submission, using scale analysis and feedback analysis, combined with related algorithms of big data mining technology, such as decision trees and clustering algorithms, through the level of emotional words appearing in the corpus, phrase-level, text-level, etc., and combine the text model with the combined reliability to output the evaluation object and evaluation feature separately, and propose an evaluation method to calculate the sensitivity of the evaluation feature, so as to accurately improve the sensitivity of the evaluation feature. It is mainly divided into two categories for data analysis. One is to focus on the public health environment of the characteristics of business English translation itself, and the other is to conduct research on the application of big data mining in the evaluation of translation discourse. The research data show that the smallest gap between the sentiment orientation of the discourse evaluation perspective is the output of the language discourse, and the smallest gap in the attributes of the evaluation object is at the phrase level, and the gap value is 3.5; for the evaluation object, the maximum difference is 3.4. With the development of science and technology and the economy, the public health environment has become more and more complex, and business English translation has received more and more attention. The sentiment analysis of evaluation discourse in this field is a means of expressing language characteristics. In order to enrich research in this field, the study of this article is necessary. This study has a deeper understanding of the affective analysis of evaluation discourse in public health environment business English translation. The clustering algorithm of big data mining technology applied can provide an important guarantee for the actual conclusion of this research and quantitative analysis of the positive evaluation and criticism of evaluation. To solve the various problems encountered in translation, so as to improve the translator's own translation level, and promote the research of translation methods in Chinese translation.

Application of Big Data Mining in the Design of Logistics Website

With the development of the information society, the information storm brought about by big data is changing the way we live, work and think. At present, many existing logistics operation systems have begun to transfer transaction mechanisms to websites due to the influence of networking. Therefore, how to use big data mining in the logistics website operating system has become a concern of many companies. The use of data mining technology can complete the analysis of a large amount of data in the logistics website, so as to realize the logistics operation on the website. This article mainly introduces the decision tree method and K-means method. This article uses big data to design the logistics website operation system and establish a potential time-discrete model. The model is solved by the decision tree method, and the design status of the big data mining in the logistics website operation system is evaluated, and historical data is used to revise the model to improve accuracy. The experimental results of this paper show that the application of big data mining in the design of logistics website operation system has increased by 48% and the false report rate has been reduced. However, the large database of my country’s big data mining in the design of logistics website operation system has not been established, so that the use of big data cannot be fully implemented.

An LSH-based [formula omitted]-representatives clustering method for large categorical data

Clustering categorical data remains a challenging problem in the era of big data, due to the difficulty in measuring dis/similarity meaningfully for categorical data and the high computational complexity of existing clustering algorithms that makes it difficult to be applied in practical use for big data mining applications. In this paper, we propose an integrated approach that incorporates the Locality-Sensitive Hashing (LSH) technique into the k-means-like clustering so as to make it capable of predicting the better initial clusters for boosting clustering effectiveness. To this end, we first utilize a data-driven dissimilarity measure for categorical data to construct a family of binary hash functions that are then used to generate the initial clusters. We also propose to use a nearest neighbor search at each iteration for cluster reassignment of data objects to improve the clustering complexity. These solutions are incorporated into the k-representatives algorithm resulting in the so-called LSH-k-representatives algorithm. Extensive experiments conducted on multiple real-world and synthetic datasets have demonstrated the effectiveness of the proposed method. It is shown that the newly developed algorithm yields comparable or better clustering results in comparison to the existing closely related works, yet it is significantly more efficient by a factor of between 2× and 32×.

Research on the Application of Big Data Mining in the Construction of Smart Campus

With the development of science and technology, the informatization construction of colleges and universities has stepped into the era of big data, and the informatization construction of colleges and universities has changed from digital campus to smart campus. This paper first summarizes the research status from the concept and evolution of the smart campus, the meaning and application of big data and data mining, the characteristics and architecture of the construction of the smart campus, and then puts forward the common problems in the construction of the smart campus. Finally it is concluded that increase knowledge, cultivate awareness of data sharing, and establish evaluation system improvement strategy in order to provide reference for the construction of wisdom campus in colleges and universities.

Big Data Mining and Decision Analysis Research on Personalized Ser-vice of Gansu Library

At this stage, with the development of Internet technology, big data has occupied a very important position in people's production and life. The library, as a modern information system, has begun to receive high attention from all walks of life. In this paper, first of all, the challenges and individual needs of Gansu Library under big data are analyzed. Secondly, it combs the structure and process of Gansu Library. Finally, it studies the personalized application of big data mining and decision-making in the library.

Study of big data mining based on cloud computing

The discovery of meaningful knowledge with high level of applicability in decision making relies on the maxim of efficient information management and analysis mechanism. Since organisations are functioning in a global scenario with exponentially high data management challenges, it is an imperative for them to adapt to the emerging paradigm of 'big data processing technology'. In the recent decade, the concept of 'big data mining' has acquired greater momentum due to its credible aspects such as cloud hosting, highly indexed and optimised data structures, automatic archival and extraction capabilities, and reporting interfaces. There exists a conspicuous difference between the 'conventional data mining' and 'big data mining'. This paper is an illustration of the advancement of the 'big data mining' and its application in various contexts. The authors of this article narrate on typical data mining algorithm and in-specific the 'parallel implementation of the algorithms'. The paper also analyses the architecture of big data mining system and the framework of big data mining platform based on cloud computing, which has provided reference to the users' cognition and application of big data mining.

The method and application of big data mining for mobile trajectory of taxi based on MapReduce

With the rapid development of urbanization, the imbalance between supply and demand of taxi is becoming more and more serious. Therefore, in this paper, the method and application of large data mining of taxi moving trajectory based on MapReduce were proposed. Firstly, the important role of the large data analysis and excavation of the taxi mobile trajectory in various fields and the related research results were elaborated. Secondly, the distributed computing framework MapReduce in the current mature Hadoop platform was combined with the mining algorithm to extract the trajectory characteristics of the taxi and analyze the hot spots. The mobile trajectory data of 13800 taxis in S City of Guangdong Province were used as experimental objects, and case verification was carried out. The result of the verification proves that the method is valid, and also validates the main reason for the current taxi from the side.

Application and Exploration of Big Data Mining in Clinical Medicine.

Objective:To review theories and technologies of big data mining and their application in clinical medicine.Data Sources:Literatures published in English or Chinese regarding theories and technologies of big data mining and the concrete applications of data mining technology in clinical medicine were obtained from PubMed and Chinese Hospital Knowledge Database from 1975 to 2015.Study Selection:Original articles regarding big data mining theory/technology and big data mining's application in the medical field were selected.Results:This review characterized the basic theories and technologies of big data mining including fuzzy theory, rough set theory, cloud theory, Dempster–Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis. The application of big data mining in clinical medicine was analyzed in the fields of disease risk assessment, clinical decision support, prediction of disease development, guidance of rational use of drugs, medical management, and evidence-based medicine.Conclusion:Big data mining has the potential to play an important role in clinical medicine.