Big Data Integration Research Articles

THE EVOLUTION OF MEDICAL INFORMATION MANAGEMENT: PAST, PRESENT, AND FUTURE PERSPECTIVES

Medical Information Management (MIM) oversees the collection, storage, retrieval, and utilization of healthcare data. Historically, MIM relied on manual record-keeping, which shifted to standardized forms in the early 20th century. The digital revolution in the late 20th century introduced Electronic Health Records (EHRs), streamlining data sharing and storage. As EHRs proliferated, interoperability became vital, leading to Health Information Exchanges. However, digital records also posed challenges in data security, prompting regulations like HIPAA. Presently, the integration of AI and big data in MIM offers diagnostic support and personalizes patient care. Future projections suggest global health data integration, but with this come ethical dilemmas, especially concerning AI's role in healthcare. This evolution from rudimentary records to sophisticated, interconnected systems exemplifies technological advancement in healthcare, emphasizing the balance between innovation and ethical considerations.

An Ontology-Based and Deep Learning-Driven Method for Extracting Legal Facts from Chinese Legal Texts

The construction of smart courts promotes the in-deep integration of internet, big data, cloud computing and artificial intelligence with judicial trial work, which can both improve trials and ensure judicial justice with more efficiency. High-quality structured legal facts, obtained by extracting information from unstructured legal texts, are the foundation for the construction of smart courts. Based on the strong normative characteristics of Chinese legal text content and structure composition and the strong text feature learning ability of deep learning, this paper proposes an ontology-based and deep learning-driven method for extracting legal facts from Chinese legal texts. The proposed method utilizes rules and patterns generated in the process of knowledge modeling to extract simple entities, and then extracts complex entities hidden in legal text details with deep learning methods. Finally, the extracted entities are mapped into structured legal facts with clear logical relationships by the Chinese Legal Text Ontology. In the information extraction test of judicial datasets composed of Chinese legal texts on theft, the proposed method effectively extracts up to 38 categories of legal facts from legal texts and the number of categories extracted increases significantly. Among them, the rule-based extractor obtains an F1-score of 99.70%, and the deep learning-driven extractor obtains an F1-score of 91.43%. Compared with existing methods, the proposed method has great advantages in extracting the completeness and accuracy of legal facts.

Exploring epigenomic mechanisms of neural tube defects using multi-omics methods and data.

Neural tube defects (NTDs) are a heterogeneous set of malformations attributed to disruption in normal neural tube closure during early embryogenesis. An in-depth understanding of NTD etiology and mechanisms remains elusive, however. Among the proposed mechanisms, epigenetic changes are thought to play an important role in the formation of NTDs. Epigenomics covers a wide spectrum of genomic DNA sequence modifications that can be investigated via high-throughput techniques. Recent advances in epigenomic technologies have enabled epigenetic studies of congenital malformations and facilitated the integration of big data into the understanding of NTDs. Herein, we review clinical epigenomic data that focuses on DNA methylation, histone modification, and miRNA alterations in human neural tissues, placental tissues, and leukocytes to explore potential mechanisms by which candidate genes affect human NTD pathogenesis. We discuss the links between epigenomics and gene regulatory mechanisms, and the effects of epigenetic alterations in human tissues on neural tube closure.

Countermeasure of Telecom Network Fraud Investigation Based on Big Data

With the diversification of information data in the information age of big data and the integration of network technology and the development of different industries, criminals who carry out telecommunication fraud are also using the technical loopholes existing in the process of integration of big data with different industries as an opportunity to commit crimes. This paper studies the investigation process and countermeasures of telecom network fraud through big data technology. This paper first introduces the characteristics of big data, analyzes the challenge of personal information security under the background of big data, warns people to protect their personal information in the era of big data, puts forward the clustering algorithm based on big data, introduces the concrete steps based on big data clustering algorithm, and then puts forward the specific steps of big data clustering algorithm. The current situation of telecom network fraud is analyzed, and the telecommunication network fraud is clustered based on big data. The experimental results show that, based on the clustering analysis of telecommunication network fraud based on big data, it is found that through the information age of big data, as long as big data are used rationally, it can effectively suppress telecommunications fraud and reduce it by 80%.

Learning Path Planning Algorithm Based on Career Goals and Artificial Intelligence

With the development of the Internet, various forms of learning resources continue to flood the public, especially the promotion of video learning plat-forms. More and more people use their spare time to learn what they need. However, there is a general lack of intelligence in online learning platforms at present, which greatly reduced the utilization of online learning platforms and their educational advantages. The innovation of this paper is proposing and explaining that with the integration of big data, online education and ar-tificial intelligence, the contradiction of online education has turned into one between the lack of intelligence in online education and the demand of users. To solve this contradiction, this paper researches from the perspective of an algorithm. Course recommendation is the core algorithm of online ed-ucation. However, the current recommendation algorithm based on collabo-rative filtering has the disadvantages of cold start and useless recommenda-tion content. In this paper, Apriori and ACO algorithms in artificial intelli-gence are studied, and the proposal of an algorithmic framework named Posi-tion-Apriori-ACO brings forth new ideas in solving online education prob-lems. The Position-Apriori-ACO algorithm can effectively carry out course recommendation and learning path planning, and also provides a research di-rection for the intelligent development of online education.

Preliminary Study of the Integration of Big Data to Answer the Challenges of Islamic Education in the Technological Age

Along with the rapid development of technology, individuals today are required to align every aspect of their lives with the technological developments of the industrial revolution 4.0, consisting of artificial intelligence, the internet of things, and big data presented in society. Notably, it was related to education, including Islamic education, which frequently stereotyped about delays in responding to globalization's challenges. This preliminary study aims to encourage empirical research that is still lacking by exploring the role of big data in Islamic education and combining data from general education that has similar core. The study focused on using the scoping review method as a part of a literature review. As a result of this study, there are four impacted factors for strengthening the usage of big data: the performance and behavior factors of learning; the storage of education data; the update in the education system; and the use of big data in the education curriculum. Future studies should begin empirical research to elaborate more on these four impacted factors practically.

The Integrated Processing Method of Educational Information Resources Based on Edge Computing

Due to the development of information technology, the new type of smart campus integrated teaching, scientific research, office services, and many other applications, online education information resources show a blowout growth. However, huge amounts of data to the campus network carrying capacity put forward high requirements. With the help of edge computing and cloud computing platform and network, the teaching, scientific research, office services, and many other applications of big data integration mining analysis build intelligent management, teaching, scientific research, and life mode, to improve the utilization of education information resources. Improving the school management level and education quality is imperative. The research purpose of this paper is to study the integrated processing method of educational information resources based on edge computing. The research of this paper enriches the relevant optimization theory of edge computing on the one hand and provides reference for the practical integrated processing of practical educational information resources on the other hand, which has the dual significance of theory and practice.

Teaching Method of Japanese Professional Writing Course Based on Big Data Fusion

In recent years, the number of Japanese learners has increased year by year. When Japanese is learned as a language, knowledge of the language itself is indispensable. This article aims to explore the teaching methods of Japanese professional writing courses based on the integration of big data. This article first introduces big data fusion and introduces the definition and model of data fusion. Generally, there are two types of data fusion models: distributed and centralized. Then, I analyzed the artificial neural network algorithm, which is a computational model that imitates the animal brain, and then researched the context-based teaching of Japanese professional courses and analyzed the context-based teaching of Japanese professional writing. The results of the context-based Japanese course teaching research show that the average Japanese writing score of the experimental class is 72.7833, the control class score is 66.3333, and the composition fluency scores are 156.27 and 119.73. It can be concluded that the composition performance and composition fluency of students in the experimental class are higher than those in the control class, so educators should let students strengthen vocabulary spelling and memory skills in situational teaching.

A Big Data Integration Platform for Ideological and Political Education for Smart Campuses

The advent of large data and the era of large data has brought significant impact and changes to human production and life. Through the acquisition, analysis, processing, prediction, and dissemination of big data in scientific research, large data has emerged as the source of advanced technological to generate global quantitative data. It not only reveals the gaps and deficiencies of traditional approaches to ideological and mechanical studies methods in obtaining ideological behavior but also information about academic subjects providing support for optimizing and innovative technology and ideological transformation and then conducting intellectual and practical research in colleges and universities. In the age of large digital data, the ideology and politics of education are facing modern domestic and foreign situations, as well as new characteristics and new needs for ideology and politics study. This makes ideology and politics research methods even more important, which is seen as an opportunity for continuous creation and innovation and also adapts to the requirements of the times and reality. Smart campus refers to an intelligent integrated environment for campus work, study, and life based on the Internet of Things. This integrated environment takes various application service systems as the carrier and fully integrates teaching, scientific research, management, and campus life. From the large-scale data era, the educational theory of whether colleges and universities’ ideological and political research is rich or not supports more scientific theories to carry out psychological and mechanical research. It is evident that in the age of large data, the ideological and political research in colleges and institutes is not only a theoretical issue but also an important practical issue. Therefore, conducting ideological and political research in colleges and institutes in the age of large data has profound theoretical significance and great practical significance.

Application of Voice Recognition Interaction and Big Data Internet of Things in Urban Fire Fighting

With the continuous development of science and technology, especially computer technology, people need a more convenient and natural way to communicate with the machine. Language can provide people with convenient and efficient information, and speech recognition technology makes this convenience extended to the field of science and deep into human daily life. In this paper, based on human-computer speech recognition interaction system, using big data Internet of things as technical support, the contribution of intelligent social service robot to urban fire protection is studied. In this system, the user can control the action of the service robot through voice command, and the user can also realize voice interaction with the robot. Because of the continuous expansion of information technology and computer technology, human beings have entered the era of information overload, and big data technology has become a hot spot in people’s production and life. The integration of big data and Internet of things technology will make the intelligence of human society to a new level, and its development has unlimited possibilities in the future. In recent years, China’s urbanization process continues to accelerate, and the land price and house price of cities begin to rise rapidly. In order to meet people’s need, a large number of high-rise, super high-rise and underground buildings continue to increase, which not only provides us with convenience, but also makes fire safety a hot concern of the whole society. Fire fighting plays an increasingly important role in the life of urban residents. In order to greatly reduce the lack of fire safety monitoring ability, this paper uses speech recognition technology to design a city fire safety management service platform based on big data Internet of things.

Ocean knowledge representation through integration of big data employing semantic web technologies

Ocean knowledge representation through integration of big data employing semantic web technologies

An Integrated Big Data Model to Salvage Nigeria's Insecurity Challenges

An Integrated Big Data Model to Salvage Nigeria's Insecurity Challenges

Integration and Development of Enterprise Internal Audit and Big Data Based on Data Mining Technology.

Auditing based on big data is the trend in the future audit development. First, the technical environment provides a technical support platform for continuous auditing. Through the development of information technology to promote the merger between financial services, the company's business operations have been digitized, and the original paper audit is also facing changes. This article aims to study the integration and development of enterprise internal audit and big data based on data mining technology. To this end, this article proposes a big data audit system, improves and optimizes the clustering algorithm (key algorithm) of data mining, and designs experiments and analysis to explore its related effects and improved performance, so that it can be more suitable for the research topic. The experimental results of this article show that the improved big data audit system improves the resource perfection of internal audit by 17.4%. The improved algorithm's accuracy rate has increased by 31.4%, and the best clustering ability has also been improved by 20.7%, which can be well applied to the company's internal audit.

Real-Time Information Exchange Strategy for Large Data Volumes Based on IoT.

In this paper, we study and analyse the real-time information exchange strategy of big data in the Internet of Things (IoT) and propose a primitive sensory data storage method (TSBPS) based on spatial-temporal chunking preprocessing, which substantially improves the speed of near real-time storage and writing of microsensory data through spatial-temporal prechunking, data compression, cache batch writing, and other techniques. The model is based on the idea of partitioning, which divides the storage and query of perceptual data into the microperceptual data layer and the perceptual data layer. The microaware data layer mainly studies the storage optimization and query optimization of raw sensory data and cleaned valid data; the aware data is the aggregation and statistics of microaware data, and the aware data layer mainly studies the storage optimization and query optimization of aware data. By arranging multiple wireless sensors at key monitoring points to collect corresponding data, building the core data service backend of the system, defining multifunctional servers, and constructing an optimal database model, we effectively solve the parameter collection and classification aggregation processing of different devices. To address the requirement of reliable and secure transmission in the process, we design a highly concurrent and high-performance TCP-based socket two-layer transmission framework and introduce the asymmetric encryption method (RSA) and data integrity verification method to design a transmission protocol that is both reliable and secure. The integration of big data and IoT is bound to bring the intelligence of human society to a new level with unlimited development prospects.

Recent trends and proposed response strategies of international standards related to shipbuilding equipment big data integration platform

Recent trends and proposed response strategies of international standards related to shipbuilding equipment big data integration platform

Multidimensional Dynamics and Forecast Models of Network Public Opinions Based on the Fusion of Smart Transportation and Big Data

With the increase of the world’s population, the means of transportation and vehicles that adapt to the times are still difficult to cope with the increase in traffic volume. The traffic problem can be said to be a worldwide problem. However, with the development of artificial intelligence, the emergence of smart transportation has brought new development to modern transportation, and the application of smart transportation and big data is inseparable. In contemporary society, the widespread use of the Internet allows the public to fully exercise their rights to participate in social management and conduct public opinion supervision, which provides a great impetus for the development of online public opinion. However, due to the huge scale of information, some false and harmful information and opinions will inevitably be mixed into it, which will make the network public opinion unable to perform its due function smoothly. Therefore, it is necessary to carry out highly effective management activities on the network public opinion. This paper studies the multidimensional dynamics and prediction model of network public opinion based on the integration of smart transportation and big data; the aim is to design a simple and effective forecasting model to provide traffic management departments with good public opinion forecasting and analysis methods so as to make better decisions. This paper analyzes the related technologies of smart transportation and network public opinion and designs a prediction model of smart transportation network public opinion. Finally, this paper uses rough set theory to optimize the model and compares the data before and after optimization. The results are as follows: the data correlation coefficient before and after optimization is 0.988, and the two-tailed significance level is 0.471, which proves that the results before and after processing are highly correlated, and the two sets of data have no significant difference, proving that the optimization of the model is effective, simplifies the analysis process, and does not change the results.

Structural Behavior of a Composite Curtain Wall Fabricated by the Fused Deposition Modeling 3D Printing Method.

In this era of the fourth industrial revolution, the integration of big data and 3D printing technology with the construction industry has maximized productivity. Currently, there is an active effort to research the optimal cladding structure through 3D printing technology to reduce production costs. This paper proposes a new type of 3D print curtain wall, using a high-strength ABS-M30 polymer panel, which is stronger than the standard acrylonitrile butadiene styrene (ABS) polymer, as an internally reinforced structure. This structure is fabricated via fused deposition modeling, a 3D printing method, to reduce the weight of the general cement panel. In addition, the shape of the polymer board was designed; three shapes were considered—O, W, and X types—which aided in further reducing the weight of the cladding. After comparing the center deformation of the structure through a lateral load test and finite element method analysis, the optimal model was selected. The measured data of the two methods at a design wind speed of 100% showed a difference of approximately 10%; however, at 150% of the design wind speed, the difference between the two sets of data increased to 27%.

Review on Integrating Geospatial Big Datasets

Abstract: Big Data and Local Systems (GIS) are both expanding technologies has influenced many areas over the past 10 years and will continue to improve and help resolve a global crisis problems, such as the effects of climate change or a global epidemic. More GIS applications it works with the continued growth of large geospatial data sources to drive precise and informed decisions. Geospatial Big Data Integration is designed to achieve compatibility with different geospatial data sets without the availability of space. A large number of large geospatial data sources seek to operate effectively the integration of data storage and management of such data, which will be used for geospatial data analysis and vision. For example, risk management data sets are related to health care and the environment heterogeneous and distinct. Finding an integrated view of large geospatial databases is also difficult it is challenging, especially when we consider the problems associated with the health epidemic and natural disasters. Therefore, before we try to predict and mitigate the processes that take place in these domains, we should see that big data integration is very important in combining data sets. We explore and chat issues involved in compiling large geospatial data sets in this study. We then split the big data collection it processes it in three phases, namely, data storage, data conversion and integration methods. In addition, several research challenges focus on large geospatial data, large global data, data retention, data conversion and linked data are presented. Lastly, open up research issues and emerging styles requires in-depth research into the near future highlighted in this study. Keywords: Big Data, Geospatial

Evolution of Society through Artificial Intelligence: A Review

Abstract: AI has been around for years. It is now gaining popularity in the tech world for two main reasons: big data sets and integration capabilities. In 2010 the Techonomic conference Eric Schmitt suggested that every two days now we are building as much information as we did from the beginning of civilization until 2003. Today, AI is currently being used in education, health care. Soon, smart machines will replace or improve people's skills in many areas, previously thought strongly within the human domain. for more than a decade, or sometimes even a hundred, for example, new buildings are usually designed to last 100 years. This study aims to examine the literature on the impact of artificial intelligence on society and its transformation. Keywords: Artificial Intelligence (AI), Impacts of AI, Problem solving through AI, ML and AI, AI-driven Tools

An ABGE-aided manufacturing knowledge graph construction approach for heterogeneous IIoT data integration

The Industrial Internet of Things (IIoT) provides a foundation for the development of emerging digital servitization paradigm in smart manufacturing. The deep integration of massive heterogeneous IIOT data plays a critical role in realising manufacturing digital servitization. However, there is a knowledge gap between different manufacturing fields, which brings a challenge for efficient integration and leverage of industrial big data. For this purpose, a Framework of Manufacturing Knowledge Graph (FMKG) is proposed, which is used to extracts industry knowledge triples from multi-source heterogeneous data to integrate domain knowledge. Also, an attention-based graph embedding model (ABGE) is proposed to discover and complement the implicit missing relationships in the knowledge graph to obtain a complete industrial knowledge graph. The effectiveness of the ABGE model has been verified on several knowledge graph data sets. And an aerospace enterprise production process was taken as an example to establish a product quality knowledge graph, which proved the feasibility of the proposed method.