Big Data Integration Research Articles

IoT Applications in the Innovation System of Enterprise Human Resource Performance Management Based on the Integration of Big Data

Under the pattern of financial globalization, execution the executives has drawn in a great deal of consideration from scholarly and industry. Related empowering innovations make the human asset execution the executives business smarter. As one of the vital innovations in man-made brainpower, huge information-driven examination further develops the presentation seriousness of human asset industry by mining the secret information worth and expected capacity of modern huge information and assists undertaking pioneers using sound judgment in different complex human asset execution the executives conditions. This paper gives a hypothetical examination premise to large-information-driven innovation to direct decision-production in human asset execution the board, completely exhibiting the practicability of enormous information-driven innovation in the astute human asset execution the executives, including key benefits and interior inspiration. A reasonable system of smart dynamic in view of modern enormous information-driven innovation is proposed in this review, which gives important experiences and musings to the serious difficulties and future examination bearings in this field.

Integration of Big Data in Port Logistics as a potentiator of competitiveness and productivity

Objetivo: este articulo tuvo como fin analizar la Integración del Big Data en la Logística Portuaria, desde el tópico de su capacidad para potenciar la competitividad y la productividad de los puertos. Método: Se estableció una revisión sistemática de información en diversas bases de datos de referencia para la búsqueda de artículos científicos, de revisión, y reflexión, así como libros y trabajos de grado o tesis que trataran el tema en el periodo de tiempo comprendido entre 2012 a 2022. Resultados: Como resultados se obtuvieron 28 fuentes documentales que aportaton relevantes analaiusus y consideraciones sobre el quehacer logístico, sobfre logistica portuaria específicamente, además sobre tecnología 4.0 y sobre Big Data Conclusiones: Se evidenció la alta relevancia de la integración del Big Data en la Logística Portuaria como potencializador de la competitividad, de la productividad y de la sostenibilidad de las organizaciones conocidas tradicionalmente como puertos.

Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries

Internet of Things (IoT) technology, which has made manufacturing processes more smart, efficient and sustainable, has received increasing attention from the industry and academia. As one of the most important applications for IoT, sustainable smart manufacturing enables lower cost, higher productivity and flexibility, better quality and sustainability during the product lifecycle management. Over the years, numerous enterprises have promoted the implementation of both sustainable and smart manufacturing. In the Industry 4.0 context, a ‘digital twin’ is widely used to achieve smart manufacturing, although this approach often ignores sustainability. This study aims to simultaneously consider digital twin and big data technologies to propose a sustainable smart manufacturing strategy based on information management systems for energy-intensive industries (EIIs) from the product lifecycle perspective. The integration of digital twin and big data provides key technologies for data acquisition in energy-intensive production environments, prediction and mining in uncertain environments as well as real-time control in complex working conditions. Moreover, a digital twin-driven operation mechanism and an overall framework of big data cleansing and integration are designed to explain and illustrate sustainable smart manufacturing. Two case studies from Southern and Northern China demonstrate the efficacy of the strategy, with the results showing that Companies A and B achieved the goals of energy saving and cost reduction after implementing the proposed strategy. By applying an energy management system, the unit energy consumption and energy cost of production in Company A decreased by at least 3%. In addition, the ‘cradle-to-gate’ lifecycle big data analysis indicates that the costs of environmental protection in Company B decrease significantly. Finally, the effectiveness of the proposed strategy and some managerial insights for EIIs in China are analysed and discussed.

A Study of Language Use Impact in Radio Broadcasting: A Linguistic and Big Data Integration Approach

Broadcasting more culturally educating and language-reviving contents are ways radio stations can help revitalize indigenous languages in Delta North in Nigeria. The challenges faced in communicating through indigenous dialects on radio stations are majorly caused by the lack of indigenous language professionals and linguists in the broadcast stations. The absence of these professionals is a major constraint to the development of the community. The broadcast media can help manage multilingualism through the introduction of new words which would give little or no room for lexicon dearth but would expand the language lexicon. Using these dialects during broadcast gives relevance to all dialects, reduces language dearth, and keeps people connected to their culture. Programmes anchored in indigenous dialects enhance the vocabulary, comprehension, and language vitality of the language. The study examined the impact of local language used in radio broadcasting using a descriptive big data survey research design. The study’s population comprises of all the inhabitants of Delta North from which a sample of 10 broadcast staff and 120 radio listeners in Delta North Senatorial District in Nigeria was drawn using a stratified random sampling technique. The instrument of data collection was a structured questionnaire with closed questions and a self-structured interview. The sample employed frequency distribution tables, percentages, and charts in the presentation and analysis of data. The results revealed that majority of the respondents in Delta North listened to radio broadcast indicating that the use of indigenous dialects/language can have massive impact on the people. The study also found that majority of the respondents use indigenous languages in their day-to-day activities, with English being used majorly only in schools. The study recommends, among others, that the National Broadcasting Commission review their policy on the allocated time of broadcast in indigenous languages and that more indigenous language experts and linguists should be incorporated into the broadcast system.

Smart Campus Economy One-Card Management Mode Based on the Integration of Big Data and Cloud Computing

In order to create an information construction road suitable for its own development faster and better, this paper has established a smart campus through new technologies such as big data. By increasing the application of data, cloud computing, and internet of things technologies, colleges and universities have improved their ability to comprehensively collect and efficiently process information. Combined with big data, cloud computing, and internet of things technology, this paper analyzes the construction of smart campus in colleges and universities. Finally, it summarizes the integration of big data, cloud computing, and internet of things technologies in the construction of smart campus in colleges and universities. The construction of intelligent campus card in colleges and universities can not only give full play to the advantages of campus network, but also use a card instead of various certificates of teachers and students in various consumption places on campus. In the future, with the sustainable development of information technology, smart campus will occupy a higher proportion in school management. The university must constantly improve the application level of big data and cloud computing technology in the smart campus. Therefore, in the new era, major universities should deeply understand the importance of smart campus construction and improve the application efficiency of advanced technology.

Open Sharing Management of Artificial Intelligence Laboratory Based on Deep Fusion of Big Data

In order to improve the efficiency of information management of open laboratories in colleges and universities and reduce the cost of information management of open laboratories in colleges and universities, this paper proposes the open sharing management of artificial intelligence laboratories based on the deep integration of big data. The infrastructure layer of the system transmits the information management data of the university open laboratory to the cloud platform layer. The cloud platform layer completes the data management, cloud computing, and access control and transmits the processed data to the application layer. The application layer realizes the information management of the university open laboratory according to the data fed back by the cloud platform layer. Among them, in the process of data transfer in open laboratories of colleges and universities, pyramid technology is used to realize data transfer, and the optimization calculation of particle swarm optimization algorithm is used to complete the transfer of multidimensional and complex data transmission information, so as to improve the efficiency of data transfer. The experimental results show that the system has high accuracy in transferring multidimensional and complex data in university open laboratories, the average resource utilization rate of the system is as high as 92%, the average response of the system is as fast as 8.5 ms, and the management cost is effectively reduced. Conclusion. The system designed in this paper can reduce the information management cost of open laboratories in colleges and universities.

An Ontology based Smart Management of Linguistic Knowledge

Natural language processing provides a very significant contribution to various application areas such as multilingual big data, information retrieval, data integration and multilingual web. However, handling linguistic knowledge to develop such lingware applications is a crucial issue, especially for linguistic novice users. To deal with this issue, a "smart" linguistic knowledge management may help the users to understand the meaning, scope and especially the use of related techniques and algorithms. In this paper, (1) we propose a semantic processing of linguistic knowledge based on a multilingual linguistic domain ontology, called LingOnto. Compared to related work, LingOnto does not only handles linguistic data, but also linguistic processing functionalities and linguistic processing features. Besides, it allows, via a reasoning engine, inferring new linguistic knowledge and assisting in the process of proposing lingware applications. This is particularly useful for novice users, but can also provide new perspectives for the expert ones. LingOnto covers the French, English and Arabic languages. (2) We propose also an assisted user friendly ontology visualization tool called LingGraph. It facilitates the interaction with LingOnto. It offers an easy to use interface for users not familiar with ontologies. It is based on a SPARQL pattern-based approach to allow a smart search interaction functionality to visualize only the ontological view corresponding to the user’s needs and preferences. In order to evaluate LingOnto, we apply it to a framework of identifying valid natural language processing pipelines. Finally, we give the results of the carried-out experiments.

Geoscience Knowledge Graph (GeoKG): Development, construction and challenges

AbstractBig earth data is a cross‐domain of geoscience and information science, which provides a novel perspective for solving geoscience problems. Most contemporary research is driven by data but neglect the potential value of knowledge. As a new scientific language in Geoscience, GeoKG is essential for understanding, representing, and mining geoscience knowledge, and can contribute to the integration of big earth data, geoscience knowledge, and geoscience models. However, research on GeoKG lack spatiotemporal perspectives in knowledge cognition, representation, acquisition and management. To this end, this article first outlines a cognitive mechanism from the human–machine double perspective and categorizes the characteristics and content of geoscience knowledge. To express evolution and complex natural rules, a knowledge representation framework is proposed through ‘state‐process’ and ‘condition‐result’ models. Aiming at multimodal data, a workflow is put forward to acquire knowledge from a small sample, a knowledge graph, a map, and a schematic diagram. Furthermore, we discuss the organization of GeoKG by improving existing data models, developing spatiotemporal correlation indexing and advancing knowledge graph completion. The concrete construction process of GeoKG is analyzed thoroughly in this study, which can support the synthetic analysis of big earth data, promote the development of knowledge engineering and provide a foundation for improving intelligent geoscience.

True-Data Testbed for 5G/B5G Intelligent Network

Future mobile communications will shift from supporting the internet of everything (IoE) to facilitating interpersonal communications beyond fifth-generation (B5G) and sixth-generation (6G) mobile communications. Intelligent communications with full integration of big data and artificial intelligence (AI) will play a key role in improving network efficiency and providing high-quality service. The AI-powered mobile communications require vast volumes of data to be collected from a real network environment for systematic testing and verification because it is a rapidly growing paradigm. As a result, we create the first true-data testbed for 5G/B5G intelligent networks (TTIN), which includes on-site experimental 5G/B5G networks, data collection and storage, and an AI engine and network optimization. True network data collecting, storage, standardisation, and analysis are possible in the TTIN, allowing for data-driven networks and system-level online verification of important B5G/6G technologies.

The Practical Dilemma and Path Optimization of Big Data Integration into Ideological and Political Teaching in Private Colleges and Universities

The promotion and application of big data have promoted the reform of quality classes in Colleges and universities, enriched the teaching content, and changed the traditional education mode. However, at present, due to the difficulties in funding, understanding teachers, and system construction, the integration of data into teaching often presents an embarrassing situation of separation between form and content, the separation between theory and practice, and the distance between teaching results and teaching objectives. For this reason, many private colleges and universities should make full use of data technology to change the quality of education mode according to the new characteristics of college students in the data environment, use the network information carrier to strengthen the interaction and exchange with college students, do a good job in guiding the ideological dynamics, actively promote positive capacity, and improve the networking level of quality education. At the same time, in order to change the current situation of incomplete integration and break through the dilemma, private colleges and universities should change their ideas and strengthen their attention to the practical ideological and political teaching. In view of the above problems, this study analyzes the internal logic of the integration of private colleges and big data, combines the actual difficulties, and uses LSTM neural network to put forward reasonable optimization strategies and suggestions, aiming to expand and cultivate high-quality practical teaching teams of Ideological and political courses, so as to improve the system and establish and improve the relevant teaching system and mechanism.

Special aspects of using Big Data in the learning process

ABSTRACT The paper examines the special aspects of using Big Data technology in education. The population was made up of 356 third-year university students. To study Big Data technology, a questionnaire was used where respondents rated: cloud technology; apps; Massive Open Online Courses (MOOCs) and digital learning platforms. The study suggested that the education sector is ambitiously applying Big Data technology, both online and offline. All surveyed respondents use apps in Big Data learning and analysis: 73.03% use Moodle, 67.13% use Zoom, 65.17% use Quizlet, 50.84% use Skype, and 35.11% use Slack. MOOCs in education are used by 75% of respondents. Digital learning platforms are used by all respondents. All students use cloud technology. When dealing with Big Data technologies, students preferred apps (8.9 ± 1.33) instead of the cloud (6.9 ± 0.11). Students believe that the important factors for using Big Data in the learning process include: quality of information (85.96%); interest (77.81%); instructor’s support (66.85%). The research findings make it possible to integrate Big Data technology into the learning process, thus improving learning outcomes and providing greater speed in processing reliable and meaningful data.

Design of a big data integration system for physical fitness training development based on speech recognition technology

Physical fitness training development research needs a large amount of data support, and the current data integration system uses the global view to extract data sequentially, which has the problems of too slow loading data and low query efficiency. Design a big data integration system for physical fitness training development based on speech recognition technology. Using FPGA as the carrier, design the speech recognition control module, storage communication module and speech input judgment module. Based on the hardware part of the system, the recognition signal is processed using speech recognition technology. By establishing global ontology mapping relationship, the integration of big data of physical training development is realized. The system functional test results show that the designed system has its minimum query efficiency improved by about 52.6%, and has higher loading efficiency and better processing capability..

Deconstruction of the Optimal Design of Urban Road Interchange Based on the Integration of Smart Transportation and Big Data.

Urban interchange is the core hub connecting various regions, and it is of great significance for alleviating the problem of traffic congestion. In the process of urban interchange design, it is impossible to strictly control the traffic volume, interchange types, and standards by relying on traditional technologies. Smart transportation and big data are emerging technologies based on data, which can provide technical support for design and decision making. Based on this, this paper first uses smart transportation and big data technology to predict the traffic volume of Nancheng New District, so as to calculate the future development trend of the target area. Then, on the basis of traffic volume, the article uses smart transportation and big data technology to optimize the original urban interchange design scheme from the aspects of traffic capacity, safety, economic benefits, and environmental benefits. Finally, the article evaluates the optimized urban interchange scheme by means of comprehensive quantitative indicators and evaluation methods. Experiments show that the traffic capacity of the interchange on the outer ring road optimized by smart transportation and big data has increased to 72.6%, and the environmental coordination has increased from 45.2% to 55.2%. Moreover, the design aesthetics of the urban interchange after optimized design based on smart transportation and big data has increased to 65.9%. In addition, the comprehensive evaluation value of the urban interchange after optimization of smart transportation and big data reached 82.6. This fully shows that the optimal design of urban interchange based on the integration of smart transportation and big data can greatly improve the traffic capacity of urban roads.

The Michigan O'Brien Kidney Research Center: transforming translational kidney research through systems biology.

Research on kidney diseases is being transformed by the rapid expansion and innovations in omics technologies. The analysis, integration, and interpretation of big data, however, have been an impediment to the growing interest in applying these technologies to understand kidney function and failure. Targeting this urgent need, the University of Michigan O'Brien Kidney Translational Core Center (MKTC) and its Administrative Core established the Applied Systems Biology Core. The Core provides need-based support for the global kidney community centered on enabling incorporation of systems biology approaches by creating web-based, user-friendly analytic and visualization tools, like Nephroseq and Nephrocell, guiding with experimental design, and processing, analysis, and integration of large data sets. The enrichment core supports systems biology education and dissemination through workshops, seminars, and individualized training sessions. Meanwhile, the Pilot and Feasibility Program of the MKTC provides pilot funding to both early-career and established investigators new to the field, to integrate a systems biology approach into their research projects. The relevance and value of the portfolio of training and services offered by MKTC are reflected in the expanding community of young investigators, collaborators, and users accessing resources and engaging in systems biology-based kidney research, thereby motivating MKTC to persevere in its mission to serve the kidney research community by enabling access to state-of-the-art data sets, tools, technologies, expertise, and learning opportunities for transformative basic, translational, and clinical studies that will usher in solutions to improve the lives of people impacted by kidney disease.

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

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

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

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

Construction and Management Method of University Information Platform Based on Big Data Technology

In order to solve the problems faced in the current management of colleges and universities, this topic integrates data mining technology and traditional statistical analysis technology in the process of College data management, and integrates multisource campus raw data. Take corresponding research models for different research objectives, implement the fine, meticulous, and accurate management concept of colleges and universities for students, build a student information management platform and help the construction of smart campus. The research integrates the school’s multi-business system data, opens up the information channel between information systems, establishes a multisystem data center and realizes the whole life cycle management of College Students’ data, including data collection, cleaning, storage and file recycling, and records the data of important nodes during students’ enrollment, graduation, and departure. Through the analysis of the school’s business management needs and the study of the school’s data characteristics, the data collected from the system information has been effectively distributed, and the data linking process is only used to delete data features based on data features. Under the action of big data technology, the information platform for college students can be constructed, which can provide more efficient, safe, and comprehensive decision support for college students’ management, making all work decisions more scientific. It can be said that the integration of big data will support the management of college students to a new level, which is very important for the modern management of students.

An Innovative Thinking About the Management Mode in Colleges and Universities in the Context of “Da Zhi Yi Yun”

In recent years, “Da Zhi Yi Yun” (integration of big data, intelligentization, mobile internet, and cloud computing) has taken the wave of global management technology reform, especially in management decision-making and the education mode in colleges and universities. China boasts the world’s largest higher education; additionally, its internet education and information education are also developing at a rapid pace. Therefore, it offers a suitable environment for the application of “Da Zhi Yi Yun.” In this context, this paper aims at the problems in the management and application of current mobile cloud platforms in colleges and universities and proposes strategies for the effective application of “Da Zhi Yi Yun” from two levels: teaching management and student management.

Methodology for determining passengers correspondence by public transport from electronic tickets validation operations

Introduction. Current methods for determining passenger ridership, due to their complexity and limited efficiency, do not allow monitoring transport demand at the proper level (in terms of comprehensiveness and continuity). Nowadays, technologies based on the collection, integration and analysis of big data (Urban computing, Big data, Internet of things) are used to create effective solutions in many aspects of our lives (including for urban transit).Materials and methods. Within the framework of this approach, a methodology for determining the correspondence of transit passengers from the operations of validating any kind of electronic travel tickets: smart cards, transport cards, magnetic cards has been developed. Each operations details are recorded in the Automated Fare Collection (AFC) system during the validation.This methodology based on the definition and evaluation the set of acceptable options of passenger trips sequences, which form the passenger correspondence, taking into account many factors that effect on the route choice by a passenger. For example, in contrast to previous studies, the practice of paying for trip at any point on the route, not necessarily immediately after boarding the vehicle, was taken into account.Results. It was proved that passenger correspondence calculated using the developed methodology statistically corresponds to the general set of transit passenger ridership within acceptable errors. The chaacteristics of transit demand is provided in the results.Discussion and conclusion. The application of the developed methodology makes it possible to organize continuous monitoring of passenger flows, technical and operational indicators of the functioning of transit system and thus implement the concept of sustainable development of public transport by designing public transit supply that meets demand.

РАЗВИТИЕ ИНФРАСТРУКТУРЫ ЖЕЛЕЗНОДОРОЖНОГО ТРАНСПОРТАС ИСПОЛЬЗОВАНИЕМ ТЕХНОЛОГИЙ ИНФОРМАЦИОННОГО МОДЕЛИРОВАНИЯ (BIM) И БОЛЬШИХ ДАННЫХ (BIG DATA): ОБЗОР

The purpose of the article is to assess the level of integration of information modeling technologies and big data in relation to the transport infrastructure in railway transport based on the analysis of publishing activity presented in the Scopus database.
 To achieve the goal set in the study, a bibliometric and bibliographic review of international scientific publications presented in Scopus, a bibliographic and abstract database of scientists around the world was carried out. A generalized scheme for conducting a study includes several stages:
 1. Selecting keywords for the research topic
 2. Collection of scientific publications on the topic of research by the keywords defined in paragraph 1.
 3. Conducting an analysis of scientific publications on the research topic:
 2.1. Conducting bibliometric analysis to develop a cluster map of the relationship of keywords;
 2.2. Conducting bibliographic analysis to select scientific publications for review.
 3. Review of selected publications.
 The conducted studies allow us to conclude that the use of new technologies at transport infrastructure facilities is still in its infancy. And it seems promising to develop models and methods of using BIM and big data technologies to implement the concept of implementing a life cycle management system for capital construction of transport infrastructures.