Big Data Mining Research Articles

Design of a Traditional Village Cultural Heritage Preservation and Management System Based on Multi-Agent Networks

Preserving cultural heritage is essential for maintaining cultural diversity and promoting sustainable development. This study investigates the application of multi-agent networks in the protection and management of traditional village cultural heritage, capitalizing on their inherent distributed, collaborative, and adaptable characteristics. By integrating advanced technologies such as big data, data mining, and machine learning, the research meticulously develops a comprehensive system architecture and functional modules aimed at revitalizing traditional village spaces. The findings demonstrate significant enhancements in protection efficiency, while simultaneously fostering cultural inheritance and innovation within village communities. Through these innovative approaches, this study provides valuable insights into the preservation of traditional village cultural heritage and contributes to the broader objectives of cultural diversity conservation.

Fleet sizing and static rebalancing strategies for shared E-scooters: A case study in Indianapolis, USA

With the rapid development of shared e-scooters, it is essential to understand their usage patterns for formulating informed e-scooter fleet management policies. This study first analyzes the usage pattern of shared e-scooters in Indianapolis, USA, by mining big e-scooter trip data. The analysis reveals an oversupply of shared e-scooters relative to actual user demand. Thus, a minimum fleet sizing algorithm is proposed to determine the required minimum e-scooter fleet size with the objective of reducing total operation cost, while ensuring demand coverage. Furthermore, three heuristic algorithms are proposed to address the static e-scooter rebalancing problem, focusing on minimizing rebalancing distance cost and rebalancing time. These algorithms consider practical operational constraints, including the number of rebalancing vehicles, their capacity, and the frequency of visits to e-scooter stations by rebalancing vehicles. The proposed algorithms are applied to e-scooter rebalancing scenarios with comparisons between the minimum and actual fleet sizes. The case study results in Indianapolis, USA demonstrate that the rebalancing distance cost with the minimum fleet size is significantly lower than that with the actual fleet size. What’s more, the rebalancing time can be reduced by about 12.34% to 27.80% when using the minimum fleet size. The findings of this study offer valuable policy implications and managerial insights for shared e-scooter operators and policymakers in developing effective e-scooter management strategies.

A conceptual hybrid model based on Ethereum for implementing sustainable development goals

The United Nations’ sustainable development goals (SDGs) present a global challenge that demands innovative solutions. Combining emerging technologies can be a transformative approach to achieving these goals and overcoming associated challenges. Blockchain provides a secure and transparent platform, while big data and data mining enable the analysis of vast datasets to identify critical areas and measure progress. Additionally, blockchain facilitates secure knowledge exchange among stakeholders through knowledge sharing and management. Recent research demonstrates the effectiveness of blockchain in implementing SDGs. Our study explores the benefits of various technologies and proposes a hybrid model based on Ethereum. This model leverages additional technologies to enhance performance, increase transparency, and reduce gas fees. This combination empowers informed decision-making and accelerates progress towards a sustainable future.

Integrity management system of hydrogen pipeline based on big data mining model optimized by improved whale algorithm

ABSTRACT In order to improve the integrity management level of the hydrogen pipeline, the intelligent information management system of the hydrogen pipeline based on big data intelligent mining model is proposed. The improved whale optimization algorithm is proposed by applying the adaptive weight update formula and quantum revolving door operation. Finally, a hydrogen pipeline is selected to carry out abnormal data detection, and the running time with different nodes and different clustering algorithms optimized by different intelligent algorithms is obtained, and the results show that the proposed Gaussian clustering optimized by an improved whale optimization algorithm can effectively find out abnormal data of integrity management of hydrogen pipeline. Monitoring precision of the proposed model is improved effectively, and the running time of the proposed model is also shortened; therefore, the proposed model can be an effective means for integrity management of the hydrogen pipeline.

Using Big Data Analytics to Identify Trends and Group Crimes through Clustering

The incidence of crime in a city presents a challenge in the absence of trend analysis that impacts citizen security. The objective of this research was to analyze and visualize crime trends in the area, using the concepts and fundamentals of Big Data Analytics, Data Mining and Clustering, the problem is addressed with a quantitative approach, using the CRISP-DM process, Principal Component Analysis (PCA) and the K-Means algorithm for clustering. Validation is performed with the Elbow Score and the Average Silhouette method, ensuring the robustness of the data clustering. The results show that crimes against property, such as robbery and theft, are the most frequent. Four crime clusters are identified, each associated with a specific category, providing a detailed view of crime distribution. Comparison with previous studies highlights the effectiveness of Big Data technologies in reducing crime, providing a solid basis for more accurate security strategies.

An entity alignment method with attribute augmentation and contrastive learning

Abstract The entity alignment(EA) task is to identify entities with the same semantics in the knowledge graph(KG), an essential issue in KG fusion and big data mining. Existing entity alignment methods mainly adopt graph embedding-based methods. However, they still have some shortcomings. First, they heavily rely on high-quality alignment seed and external semantic information. Secondly, the present attention mechanism focuses on the entire graph information, neglecting the noise of attribute information. This paper proposes an EA approach based on Attribute Augmentation and Contrastive Learning (AACL). Our method introduces attribute augmentation to enhance the structure information of knowledge graphs and reduce dependence on alignment seed. A masked attention mechanism is developed to emphasize important attribute information and mask out invalid attributes to better capture semantic dependencies in the KG. Experimental results on three public datasets indicate that our AACL outperforms the present entity alignment approaches.

From crisis to prevention: mining big data for public health insights during the flint water crisis

This study investigates the utility of Google Trends and Google Search data in retrospectively analyzing the Flint Water Crisis, a significant public health event. By examining keywords relevant to the crisis, such as “lead,” “bottled water,” “water filters,” “pneumonia,” and “Legionnaires’ Disease,” we aimed to uncover patterns in public awareness and response during a public crisis and explore how such awareness and responses could potentially aid in reducing the risk of similar crises in the future. Our analysis reveals a clear correlation between search frequencies and the crisis timeline, with spikes in search terms corresponding to key events. This suggests that such data can serve as a valuable tool for understanding public sentiment and behavior in the face of environmental disasters. The research underscores the potential of “Big Data,” led by search engines and social media platforms, in shaping public policy and informed decision-making. However, it also addresses the limitations and challenges in using these data sources, including issues of data consistency, replicability, and the influence of sociocultural contexts on search behaviors. The study advocates for the combined use of Google Trends and Google Search data, complemented by other datasets, for a more comprehensive understanding of public engagement in environmental crises. This work contributes to the growing field of infodemiology, emphasizing the importance of big data analysis in environmental science and public health research.

The correlative factors of enterprise culture and enterprise human resource based on data analysis and federated learning

Under the current big topic of enterprise management, how to cultivate, use, and maintain talents is the critical problem of modern human resource management. Based on big data mining, cloud computing, and other related model optimization technologies, this study explores the crucial significance of enterprise culture construction and enterprise human resource management in achieving the business objectives of the entire enterprise. The establishment of this model also means that the enterprise’s profit margin and business indicators have been improved and developed under a certain model. The case results demonstrate that the optimized competency model is significant to studying corporate culture management system behavior, policy analysis, and improving corporate human resource management policies.

Analysis of Correlation Features in Material Application for Architectural Design Based on Big Data Text Mining

This study conducted a big data mining and lexical co-occurrence network analysis on design texts sourced from architectural media over the past decade, systematically evaluating the application trends of materials in architectural design. This approach distinguishes itself from traditional case studies that rely on small samples of material applications in architectural design, thereby significantly enhancing the generalizability of the conclusions. The research reveals a 27.2% increase in the material–project frequency of architectural materials over the past ten years, indicating that the materiality of architecture is gradually making a comeback to counteract the trend toward architectural visualization. Within this context, material properties, material interactions, and material presentation in architectural components emerge as universal concerns in architectural design. Notably, the sustainability and energy efficiency of wood and concrete have emerged as pivotal topics in architectural practice, in addition to their structural functions. Additionally, over the past decade, the material–project frequencies of brick and stone have grown rapidly, with respective increases of 24.3% and 11.8%. Brick and stone primarily respond to the locality and style of architectural design by emphasizing their inherent texture and color, thereby enhancing the spatial perception and experience within the designs. This study lays the groundwork for quantitative research on the influence of building materials on architectural design, and it illuminates current trends and preferences in material application within architectural design, thereby empowering architects to make more informed decisions in their material selection.

An analysis of Data mining techniques, multidimensional modelling on agriculture

This articles analysis the “An analysis of Data mining techniques, multidimensional modelling on agriculture.” The sole method for using and obtaining knowledge from large data sets is data mining. Samples are taken in increments from various locations within a bag. A consignment can contain up to 30 bags, of which one must be selected at random from each bag. For consignments containing 50 bags, however, samples of the Magadh division blocks in Bihar must be obtained from each bag. additionally to investigate how changing weather affects agriculture and how data mining in government platforms is formed. Whether there is a positive correlation between weather changes and agricultural and big data mining. Results shows that the Big Data mining has positive co-relation (r=.8) on weather changing on agriculture. The results confirms the hypothesis.

Research on monitoring and energy management systems for energy storage stations on the power generation side

Abstract Addressing the challenges of limited operational efficiency, inadequate battery safety, inflexible system scalability, inadequate AGC/AVC functionality, and low levels of intelligent operation and maintenance encountered by energy storage power stations integrated with new energy generation sources, this article delves into the pivotal role of these power stations on the generation side. It meticulously examines the network architecture, functional framework, and pivotal technologies underpinning the system. Furthermore, the article proposes a comprehensive monitoring and energy management system tailored specifically for energy storage on the generation side. The system uses micro-service architecture and container technology to realize the processing and storage of massive data. Through complete isolation of the information network and control network and millisecond communication with GOOSE, the charging and discharging response speed of the energy storage station is greatly improved. The power allocation algorithm based on device status recognition completes the system’s high-precision power control of PCS, and this system uses big data analysis and data mining technology to conduct a comprehensive statistical analysis of battery health status, power station operation efficiency, and equipment operation status and energy consumption. The design of the system has favorable technical and application guidance for the subsequent energy management design and engineering application of the energy storage power station.

Analysis of user trends in digital health communities using big data mining.

Camoni, the largest digital health community in Israel, involves thousands of patients in the decision-making process concerning their illness and treatment. This approach reflects the recent global shift towards digital tools that combine professional information with social networking capabilities to enable problem-solving, emotional support, and knowledge sharing. Digital health communities serve as an invaluable resource for individuals seeking to learn more about their health, connect with others with shared experiences, and receive encouragement. Our research investigates user trends in digital health communities using the Camoni platform as a case study. To this end, we compile a comprehensive database of 12 years of site activity and conduct a large-scale analysis to identify and assess significant trends in user behavior. We observe several significant trends concerning different genders engagement and note a narrowing of gaps between men and women users' participation and publication volume. Furthermore, we find that younger users have become increasingly active on the platform over time. We also uncover unique gender-specific behavior patterns that we attempt to characterize and explain. Our findings suggest that the rise of digital health communities has accelerated in recent years, reflecting the public's growing preference to take a more active role in their medical care.

How drug repurposing can advance drug discovery: challenges and opportunities

Traditional de novo drug discovery, which typically presents an 11% approval rate from phase I trials and even higher failure rates in fields like neurodegeneration, often requires two to three billion dollars and 10–17 years per new drug. In contrast, drug repurposing can reduce risks and bring drugs to the market in 3–12 years, with an average of $300 million investment. In this article, we will outline how drug repurposing can accelerate the discovery of drugs derived from natural and synthetic products. The vast amount of chemical, biological, structural, and clinical data available in public repositories will greatly facilitate drug discovery, without the need to start a discovery campaign from scratch. In the big data era, data mining and artificial intelligence will play major roles in both drug repurposing and drug discovery. This article will provide valuable insights into how drug repurposing can support drug discovery and vice versa, emphasizing its impact in addressing unmet medical needs, achieving cost-effectiveness, and enabling faster market access. Despite legal and regulatory challenges, the cost-effectiveness, and the potential to give new life to compounds already in the pipeline make drug repurposing a crucial complement to traditional drug discovery in the era of precision medicine.

Temporal subgraph matching method for multi-connected temporal graph

Temporal graph matching is used to find a matching subgraph set that satisfies temporal and topological information on the temporal graph. It is widely used in real scenarios and provides important support for big data mining and analysis. Most existing matching methods take the single-connected graph as the matching graph, which has a high computational cost. Simultaneously, the construction and matching complexity of its index on multi-connected temporal subgraph matching is high. To solve the above problems, we propose a new temporal subgraph matching problem for multi-connected temporal graphs and proposes the corresponding efficient filtering-selecting-matching three-stage multi-connected temporal subgraph matching method. In the filtering stage, a vertex-based filter is proposed to generate a candidate temporal subgraph set. In the select phase, a set of temporal subgraphs satisfying the vertex mapping is found. In the matching stage, the single-filter temporal subgraph matching method and temporal subgraph matching based on the temporal information tree index method are proposed to solve the problems that the edges of multi-connected temporal subgraphs cannot be mapped and the single graph matching efficiency is low, respectively. The experimental results show that the proposed method improves the matching accuracy, and the corresponding index size is reduced.

Data-Driven Entrepreneurship in Green Hydrogen Economy: An Integrated Framework for Entrepreneurial Enterprises in the Era of Big Data

In the era of big data, in order to improve the aggregation effect of start-ups, a digital framework with a promoting role has been formed. This paper proposes a data-driven approach in the context of the green hydrogen economy. Firstly, the data function was used to collect, classify and sort out the entrepreneurial data to form an orderly data set. Then, with the help of data-driven functions, the nonlinear reorganization of start-ups is carried out to verify their discreteness, the correlation between the hydrogen economy and start-up data is mined, and an integrated framework is formed through iterative calculation. The results show that there is a significant correlation between the hydrogen economy and start-ups, and big data can drive the integrated development of start-ups, and the data-driven method proposed in this paper can accurately identify start-up data, with a recognition rate of 90%, which is better than the previous machine learning methods and decision tree methods, and has strong big data mining capabilities. Therefore, there is a significant correlation between the hydrogen economy, big data, and start-ups, and a data-driven approach can identify the relationship between the two and facilitate the construction of an integrated framework.

Empirical asset pricing based on network big data mining and privacy protection

Empirical asset pricing based on network big data mining and privacy protection

Research and Application of University English Writing Teaching Reform Based on Data Mining

Based on big data mining and learning analysis technology, this paper systematically summarizes the theoretical basis of network-based college students' English writing skills training mode and discusses the reform and application of college English writing teaching. By mining network data, establishing corresponding writing training database and writing corpus, and at the same time strengthening writing teaching methods with the help of existing network corpus. Students learn through reading, imitation and innovation in the course, and receive psychological and motivation training. The research shows how data mining ability can test the data production ability of front-line authors, and test the ability of front-line teachers to cultivate students' autonomous learning and self-adaptation. It is emphasized that this teaching mode updates the knowledge structure of front-line teachers and cultivates new teachers who combine knowledge with technology. By providing innovative methods to optimize the teaching process, it is expected to effectively improve students' writing ability.

The Technology Evolution and Future Trend of Smart Blackboard

As an important tool for educational digitization, the function of the smart blackboard has gradually evolved from a single teaching information-sharing tool to a smart system for knowledge expression, sharing and generation. Smart blackboard technology has been experienced from chalk + blackboard to projection equipment + blackboard, to interactive electronic whiteboard, and then to today's integrated touch, high-definition display and artificial intelligence technology. The evolution process of the smart blackboard is characterized by interactivity, effectiveness and intelligence. At present, the smart blackboard has become a collection of many advanced technologies. It is gradually integrated into modern technologies such as artificial intelligence, cloud computing, big data analysis and the Internet of Things, making remote collaboration and intelligent classroom management possible. It has become a smart system for big data collection, mining and analysis, evaluation and feedback, and teaching decision-making. Looking forward to the future, the smart blackboard will pay more attention to user experience design, provide an immersive learning experience through Augmented Reality and Virtual Reality technology, and optimize personalized teaching content in combination with machine learning, to lay a foundation for building an intelligent and personalized teaching environment.

Construction framework of smart tourism big data mining model driven by blockchain technology

In the era of sharing economy, the tourism market is increasingly characterized by personalized demand, mobile consumption and product segmentation. This paper aims to apply big data mining technology in the field of smart tourism. Firstly, it focuses on image summary selection and collaborative filtering technology based on big data mining. It then demonstrates the integration of blockchain in smart tourism, emphasizing the use of decentralized structures and smart contracts to achieve data security and transparency, and describes the testing process of smart tourism platforms, including data preparation and platform operational efficiency testing. Finally, the research results of this paper are summarized, and the development potential and practical application value of smart tourism are demonstrated. The results show that in the smart tourism big data mining model, the minimum support for the data set is 10 % and 20 %, respectively. Moreover, with the increase of the number of nodes in the same data set, the running time decreases gradually. It can be seen that smart tourism big data mining has strong scalability.

Exploring influential factors in Chinese engineering professors' industrial experience: A big data and CV analysis approach

There is a long-standing criticism in academic circles about the inadequacy of engineering industrial experience for professors in Chinese research universities, but there is a lack of analysis of the manifestations and root causes of the problem. The research aims to contribute meaningful insights into the factors influencing the industrial experience of engineering professors in Chinese research universities, facilitating informed policymaking and fostering collaborative efforts between academia and industry. This paper examines Chinese engineering professors' industrial experience using big data mining, computing, modeling, and image rendering. The data was collected from the publicly available CVs from the websites of various Chinese universities which contains data on colleges and universities across China and analyzed engineering professors' professional trajectories and industrial experience using the method of resume analysis. A nonlinear logistic regression model is fitted to determine the impact of multiple independent variables on the probability of engineering industrial experience among professors, with logit transformation and maximum likelihood estimation. The results show that a low percentage of engineering university professors in Chinese research universities have had industry employment after obtaining their Ph.D. degrees. The regression model indicates that gender, level of inbreeding, overseas study experience, university level, and birth age significantly affect the engineering experience of professors. This study proposes several policy recommendations based on comparative analysis and its research. The study's findings are critical for policymakers to create policies that promote industrial partnerships as a fundamental aspect of the professional development of engineering faculty and for further research in the field.