Computer Big Data Research Articles

Application of Computer Big Data Technology in Groundwater Environment Modeling of Framed City

In this paper, GIS theory and method are used to establish the analysis model of groundwater dynamics, chemical field and groundwater environment evolution. The corresponding system functions are realized by programming. A discrete model of aquifer medium space is established and numerically simulated. The technical framework aims to simplify the finite element analysis process, optimize the calculation efficiency of the model, and realize the 3D visualization of the finite element numerical simulation of groundwater and the calculation results. The feasibility and effectiveness of this method are proved by a case study.

An Exploration of the Computer Big Data Mining Service Model Under Resource Sharing

In order to meet the diverse needs of users for data mining services and improve resource utilization and enterprise competitiveness, this article aims to construct a Big Data Analytics (BDA) data mining service model based on resource sharing mechanisms. This article designs a customized data mining service model for BDA based on its characteristics. In this model, the authors apply the improved Apriori algorithm to determine the optimization plan and improve the ant colony optimization algorithm to improve the efficiency and accuracy of data mining. By analyzing the experimental results, the scientificity and rationality of the proposed data mining service model for BDA were demonstrated, and the implementation strategy of the data mining model was improved. These research findings provide important references for BDA's data mining service model based on response surface modeling and also provide guidance for enterprises on how to better utilize resources and improve competitiveness when facing big data.

Research on the construction path of large-scale industrial agglomeration circle in Foshan based on computer big data fusion

This paper innovatively proposes the goal of research on the decision support system of the large-scale industrial agglomeration circle in Foshan. Based on the computer big data combined with the process of urban planning and management in Foshan, this paper proposes the construction content of the current planning decision support system for the new industrial agglomeration circle in Foshan. This paper proposes the relevant models for the analysis and construction of the development and evolution model and the construction of the auxiliary decision-making management system. The method of large-scale industrial agglomeration area in Foshan proposed in this paper provides services for the government to make rational and scientific decision-making. The computer big data industry circle planning model proposed in this paper has certain practical significance.

Computer Network Virus and Computer Network Security Prevention

With the rapid development of information technology, network security management is becoming increasingly complex, especially, from the point of view of data security, the protection of data should be strengthened, now, the popularity of computers and smart phones will bring a large amount of information, which is inextricably linked with our property and personal privacy, in the human society, with the continuous development of intelligent technology, people's personal information and property protection are also paying more and more attention. Therefore, in order to achieve the purpose of network security and preventing information leakage, it is necessary to analyze and categorize all kinds of data and build a unified management platform on this basis. In recent years, the development of China's computer technology has been more and more attention, especially with the rapid development of big data, data storage, processing capacity, security and other aspects of the rapid development of all walks of life has brought great economic benefits, and the development of social productivity has played a huge role in promoting, but also brought a huge security risks, but the combination of big data technology and computer network security is a good technology, and its advantages are fully reflected in daily life.

A Method for Constructing an Intelligent Maintenance Assistance System for Hydroelectric Stations

This paper primarily addresses the work requirements of a certain hydroelectric dam and explores a method for constructing an intelligent assistance system for maintenance in hydroelectric stations. The dam boasts a diverse array of equipment and is situated in a region characterized by complex terrain and structures, posing significant challenges to maintenance work across various disciplines. This study aims to integrate emerging technologies such as big data computation, data analysis, and knowledge graphs, alongside cutting-edge human-computer interaction theories. It seeks to develop advanced theories of maintenance assistance for hydroelectric stations, focusing on the intelligent decision-making and guidance in maintenance operations using digital technology. By achieving digitization and intelligence in maintenance management and workflow processes, this research endeavors to provide participants at every stage of maintenance operations with safe, efficient, and reliable support capabilities. Ultimately, it aims to elevate the level of digitization and intelligence in maintenance operations at hydroelectric stations to unprecedented heights, thereby enhancing the management efficiency of maintenance operations and further improving quality and efficiency.

Research on Computational Mechanics Construction of Bridge Civil Engineering Construction Based on Computer Big Data Technology

In this paper, a 3D visualization system for bridge construction management is established based on VRP virtual platform. The bridge construction monitoring data management system is designed on the platform of Access database and the object processing technology of VRP. Starting from the basic theory of construction monitoring of cable-stayed bridge, the system realizes the problem of construction error identification in the monitoring process and using the recognition results to compensate and modify the finite element model. The system mainly realizes the functions of data editing, query, data analysis, data visualization, bridge information introduction and so on. It solves the key problem of using relational database to form various reports quickly and intelligently.

Research on the Application of Computer Big Data Technology and Information Systems in the Sustainability of Energy Industry

Research on the Application of Computer Big Data Technology and Information Systems in the Sustainability of Energy Industry

Distributed Computing and Inference for Big Data

Data are distributed across different sites due to computing facility limitations or data privacy considerations. Conventional centralized methods—those in which all datasets are stored and processed in a central computing facility—are not applicable in practice. Therefore, it has become necessary to develop distributed learning approaches that have good inference or predictive accuracy while remaining free of individual data or obeying policies and regulations to protect privacy. In this article, we introduce the basic idea of distributed learning and conduct a selected review on various distributed learning methods, which are categorized by their statistical accuracy, computational efficiency, heterogeneity, and privacy. This categorization can help evaluate newly proposed methods from different aspects. Moreover, we provide up-to-date descriptions of the existing theoretical results that cover statistical equivalency and computational efficiency under different statistical learning frameworks. Finally, we provide existing software implementations and benchmark datasets, and we discuss future research opportunities.

Effective combination and application path of computer big data analysis and cloud computing network technology

Abstract In this paper, based on the data mining algorithm to obtain a collection of big data attributes, the information entropy detection is used to analyze the data distribution characteristics, and the clustering center is extracted. Normalize the feature data, calculate the judgment matrix in the mining process based on the condition set, and perform normalization. The redundant features are extracted from the data to be mined, and the weight vector value of each feature is obtained to obtain the processed feature subset. The results show that the importance score and security score are above 6. The combination with cloud computing network technology in computer big data analysis helps improve data processing efficiency and provides more reliable data support for social and economic development.

Consistent Matrix: A Feature Selection Framework for Large-Scale Datasets

Large-scale data processing based on limited computing resources has always been a difficult problem in data mining, where feature selection is often used as an effective data compressing mechanism. For granular computing of big data, discernibility matrix and dependency degree are the most representative methods for matrix-based and feature importance degree-based feature selection, respectively. However, their temporal and space complexities are high and often lead to poor performance. In this paper, a novel feature selection framework for large scale data processing with linear complexities was proposed for the first time. Firstly, a much more concise fuzzy granule set, called fuzzy arithmetic covering, was introduced to reduce computational costs. Then a new matrix-based feature selection framework, namely consistent matrix, was proposed for general rough set models. As a result, a heuristic attribute reduction algorithm, i.e., HARCM, was designed accordingly. Compared with six state-of-the-art algorithms for feature selection, the average running time of the newly proposed algorithm was reduced up to 2913 times, with a comparable or even better classification performance.

Complementary structural and functional abnormalities to localise epileptogenic tissue

When investigating suitability for epilepsy surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to identify key white matter tracts for surgical avoidance. Here, we investigate whether structural connectivity abnormalities, inferred from dMRI, may be used in conjunction with functional iEEG abnormalities to aid localisation of the epileptogenic zone (EZ), improving surgical outcomes in epilepsy. We retrospectively investigated data from 43 patients (42% female) with epilepsy who had surgery following iEEG. Twenty-five patients (58%) were free from disabling seizures (ILAE 1 or 2) at one year. Interictal iEEG functional, and dMRI structural connectivity abnormalities were quantified by comparison to a normative map and healthy controls. We explored whether the resection of maximal abnormalities related to improved surgical outcomes, in both modalities individually and concurrently. Additionally, we suggest how connectivity abnormalities may inform the placement of iEEG electrodes pre-surgically using a patient case study. Seizure freedom was 15 times more likely in patients with resection of maximal connectivity and iEEG abnormalities (p=0.008). Both modalities separately distinguished patient surgical outcome groups and when used simultaneously, a decision tree correctly separated 36 of 43 (84%) patients. Our results suggest that both connectivity and iEEG abnormalities may localise epileptogenic tissue, and that these two modalities may provide complementary information in pre-surgical evaluations. This research was funded by UKRI, CDT in Cloud Computing for Big Data, NIH, MRC, Wellcome Trust and Epilepsy Research UK.

Opportunities for enhancing MLCommons efforts while leveraging insights from educational MLCommons earthquake benchmarks efforts

MLCommons is an effort to develop and improve the artificial intelligence (AI) ecosystem through benchmarks, public data sets, and research. It consists of members from start-ups, leading companies, academics, and non-profits from around the world. The goal is to make machine learning better for everyone. In order to increase participation by others, educational institutions provide valuable opportunities for engagement. In this article, we identify numerous insights obtained from different viewpoints as part of efforts to utilize high-performance computing (HPC) big data systems in existing education while developing and conducting science benchmarks for earthquake prediction. As this activity was conducted across multiple educational efforts, we project if and how it is possible to make such efforts available on a wider scale. This includes the integration of sophisticated benchmarks into courses and research activities at universities, exposing the students and researchers to topics that are otherwise typically not sufficiently covered in current course curricula as we witnessed from our practical experience across multiple organizations. As such, we have outlined the many lessons we learned throughout these efforts, culminating in the need for benchmark carpentry for scientists using advanced computational resources. The article also presents the analysis of an earthquake prediction code benchmark while focusing on the accuracy of the results and not only on the runtime; notedly, this benchmark was created as a result of our lessons learned. Energy traces were produced throughout these benchmarks, which are vital to analyzing the power expenditure within HPC environments. Additionally, one of the insights is that in the short time of the project with limited student availability, the activity was only possible by utilizing a benchmark runtime pipeline while developing and using software to generate jobs from the permutation of hyperparameters automatically. It integrates a templated job management framework for executing tasks and experiments based on hyperparameters while leveraging hybrid compute resources available at different institutions. The software is part of a collection called cloudmesh with its newly developed components, cloudmesh-ee (experiment executor) and cloudmesh-cc (compute coordinator).

Heterogeneous High-Performance System Algorithm Based on Computer Big Data Technology

INTRODUCTION: In this paper, a scheduling algorithm for heterogeneous systems based on prioritization (PQDSA) is proposed. This algorithm is a sort method based on directed acyclic graph (DAG). The key nodes in the network are grouped according to the communication and computing costs in the network. This increases the parallelism between task schedules and reduces the completion time of work sets. Then, a method of assigning multiple tasks to multiple processors using interpolation is proposed. The PQDSA method can effectively reduce the time of scheduling multiple tasks and improve the scheduling effect. PQDSA is compared with EDL-θ and EDF scheduling methods. The results show that this method has better scheduling efficiency.

Analysis of the problems and countermeasures faced by the innovation and entrepreneurial education of college students in higher education institutions under the background of computer big data

Abstract The issues of teaching innovation and entrepreneurship to college students in higher education institutions are first examined in this research, respectively, in terms of practical activities, evaluation system, and faculty strength. Based on the CIPP theory, the assessment framework for innovation and entrepreneurial education was developed, and the redundant indexes were screened by using the improved hierarchical analysis method. Secondly, the AHM-GRA model was used to conduct an in-depth analysis of the innovation and entrepreneurship curriculum at higher education institutions. Finally, the index system was empirically analyzed through gray correlation analysis. The results show that, on average, the approach utilized in this work performs 12.67% better than the fuzzy comprehensive assessment. With the help of this methodology, higher education institutions may assess their current level of innovation and entrepreneurship instruction and strive to improve it.

Analysis of the construction and reform path of preschool education professional practice courses in the context of computer big data

Abstract This paper first uses the big data adaptive learning system to facilitate students’ mastery of knowledge points, improve their learning performance, and stimulate their interest in learning. Then, the practical curriculum of preschool education is realized through curriculum facilities, curriculum objectives, curriculum content, curriculum organization and implementation, and curriculum evaluation. Next, the QFD entity model for quality assurance of preschool education personnel training is established. The realistic basis for the construction of the model is also elaborated, and the conceptual framework of the QFD model for quality assurance of higher education personnel training is proposed and comprehensively analyzed. The results show that the cooperative group learning approach is more favored by students, up to 64.44% in the practical courses. The number of graduates who chose to work in early education and other industries increased by 10%. It shows that improving students’ personal comprehensive quality can adapt to society’s comprehensive demand for professionals.

Research on the Application of Computer Big Data Technology in English Online Translation

Aiming at the disadvantages of the traditional rule-based machine translation model that the English translation results are not accurate enough and it is difficult to accurately describe the relationship between words, the English machine translation model based on the semantic network is designed and improved. The algorithm analyses the English grammatical rules, then performs Gaussian marginalization on the semantics to obtain the rectangular window function, obtains the window feature vector, projects the semantic information entropy data, and adds the semantic correlation factors to the information entropy and information gain of the text., So as to obtain the semantic nonlinear spectral characteristics. Experimental results show that the designed English machine translation model has high translation accuracy and stability.

DECREASE HEALTH ISSUES BY BIG DATA ANALYSIS

The goal of "Swastha Bharat," or a healthy India, is to help every Indian realise his or her dream of living in an incredible India. Article 21 of the Constitution of India guarantees every citizen the right to health care. Health, as defined by the World Health Organisation (WHO), is not only the absence of disease but rather the whole mental, physical, and social flourishing of a person. New computer technologies have had a profound impact on the health industry, driving the generation of more medical data and spawning new subdisciplines of study. In order to make Swastha Bharat (Healthy India) a reality, this article discusses the crucial computing and analytical capabilities of Big Data in handling massive amounts of transactional information in real time. A universally applicable system is proposed. Keywords: Big Data Analytics, Swastha Bharat, Big Data Challenges, e-Health Care, Health Predictive Analysis, Health care in India.

Searchable encryption algorithm in computer big data processing application

With the continuous development of computer technology, the amount of data has increased sharply, which has promoted more and more diversified data transportation and processing methods. At the same time, computer data analysis technology can effectively process data. This is reflected in the computer big data analysis technology not only can realize data visualization analysis, but also has data prediction and data quality management. The development of cloud computing network technology can not only provide convenience points for individuals, but also provide space for enterprises to store data. The emergence of keyword search encryption algorithms solves this problem. When users use keywords to search encryption algorithms, they can search for cipher text keywords to find the files or data they want in the cloud environment. At present, it has been widely used. In addition, this article also improves the keyword search plan and the user's query plan according to the dynamic changes of keywords, and proposes a user's multi-dynamic keyword search encryption plan. Through this program, users can search for encrypted files by keywords and change them, and the changed data will be dynamically updated. In this way, the program can realize multi-user data sharing, and can realize efficient search and dynamics.

Deep reinforcement learning‐based precise prediction model for Smart M‐Health system

AbstractThe use of Android mobile phones and other wireless technology in the field of healthcare infers to mHealth (mobile health). Modern Science and technological developments have paved way for better and more sophisticated solutions towards Smart mHealth systems and preventive Smart Healthcare services for disease—treatment, surveillance, management of chronic disease and tracking epidemic outbreaks. Thus, the mHealth data can be collected from various end users, deposited in repository, perform analysis and suitable Smart Healthcare services can be made accessible to end users, anytime, anywhere, over the internet. This can be achieved by incorporating the methodologies of advanced Soft Computing methodologies, data communication, cloud storage and cloud computing, big data analysis, artificial intelligence, computer communication/networking and other engineering techniques. However, the analysis of such huge volumes of data and to provide precise Smart Healthcare services is a million‐dollar question. This research article exposes a Deep Reinforcement Learning model for precisely predicting the disease and offer precise Smart m‐Healthcare services to the end users. This research provides an intensive experimental analysis and investigation using synthetic health parameters that were simulated using various mHealth sensors. The dataset includes 15 varieties of mHealth metrics with a total dataset size of 285.

Retracted: Heterogeneous Cluster Application Communication Optimization and Computer Big Data Management

Retracted: Heterogeneous Cluster Application Communication Optimization and Computer Big Data Management