Data Processing Research Articles

Market orientation and change capability on an Indonesian sharia banking performance: the moderating effect of leadership religiosity

Purpose Changes are required to improve the performance of Sharia banking in Indonesia. Using dynamic capability theory, this study aims to analyze organizational learning, change process and change context (OCC) in Bank Syariah Indonesia as influenced by market orientation (competitor and customer orientation) and moderated by religiosity and its impact on organizational performance (Maqashid Sharia) using a multilevel perspective. Design/methodology/approach To test the hypothesis, this research surveyed by distributing online questionnaires to managers and employees at 62 branch offices of Bank Syariah, the largest Sharia bank in Indonesia. Data processing involved multilevel structural equational modeling with Mplus analysis software. Findings Market orientation determines an organization’s ability to change significantly, which leads to organizational performance being significantly moderated by religiosity. In OCC, organizational learning influences change context through change processes, ultimately influencing organizational performance (Maqashid Sharia). Originality/value This research used a multilevel perspective by combining market orientation and OCC variables moderated by religiosity and its impact on Maqashid Sharia. This study was conducted at the largest Sharia bank in Indonesia, Bank Syariah Indonesia.

Boosting the efficacy of green accounting for better firm performance: artificial intelligence and accounting quality as moderators

Purpose This study aims to deepen our understanding of how conventional technologies and robust accounting education standards can impact the effectiveness of green accounting practices in enhancing firm performance. To achieve this, the paper explores the moderating effects of artificial intelligence (AI) and accounting education quality on the relationship between green accounting and firm performance. Design/methodology/approach Using generalized method of moments estimation, this research uses a comprehensive dataset comprising 32,680 firm-year observations of listed companies from ten prominent countries – Canada, the UK, the USA, China, France, Germany, India, Japan, South Korea and Italy – over the period from 2012 to 2022. These countries, selected based on their high gross domestic product rankings as reported by the International Monetary Fund, ensure a diverse representation of economic strengths and capture a wide range of green accounting practices. Findings The study shows that green accounting practices positively impact current firm performance. Country-level AI positively moderates this relationship, suggesting that advanced AI infrastructure enhances the benefits of green accounting through improved data accuracy and decision-making. However, country-level accountancy education quality negatively moderates the relationship, indicating that stringent implementation of green accounting standards in these regions may introduce complexities and costs that reduce firm performance. Practical implications Integrating AI enhances data processing, predictive analytics and decision-making, improving green accounting effectiveness. High-quality accounting education ensures accurate reporting and greater transparency. These insights, when applied, can empower businesses to optimize sustainability strategies, assist policymakers in developing targeted regulations and guide educators in preparing accountants for the evolving demands of green accounting. Originality/value To the best of the authors’ knowledge, this study is the first to explore the combined moderating effects of AI and accounting education quality on the relationship between green accounting and firm performance. By highlighting the synergistic role of digital innovation and robust educational standards, this research offers novel insights into how these factors can enhance the effectiveness of green accounting practices and improve financial outcomes.

A Scientometric Review of Research on Intestinal Dysbiosis in Dogs (2011–2023): Trends and Insights

The aim of this study is to quantify and describe, through scientometrics, data on studies related to intestinal dysbiosis in dogs. For this purpose, articles published between 2011 and 2023 were selected using the Web of Science database. Through an active search, 137 articles were screened, of which 82 were included in this study, selected based on title and abstract evaluation. Data were collected on the number of articles published by date, number of authors, citations per author, and the countries with the highest number of publications. Data processing was conducted using R Studio software and the Bibliometrix package, ensuring greater specificity in the analyses. The results show that the year 2020 presented the highest peak of published studies, with 13 articles. It was also observed that the most cited author published 41 of the 82 selected articles, and that the United States is responsible for the largest number of publications. Through this study, it was possible to conclude that countries with greater technological resources directly impact the scientific dissemination of information on the proposed topic.

Digitalization and Dynamic Criticality Analysis for Railway Asset Management

The primary aim of this paper is to support the optimization of asset management in railway infrastructure through digitalization and criticality analysis. It addresses the current challenges in railway infrastructure management, where data-driven decision making and automation are key for effective resource allocation. The paper presents a methodology that emphasizes the development of a robust data model for criticality analysis, along with the advantages of integrating advanced digital tools. A master table is designed to rank assets and automatically calculate criticality through a novel asset attribute characterization (AAC) process. Digitalization facilitates dynamic, on-demand criticality assessments, which are essential in managing complex networks. The study also underscores the importance of combining digital technology adoption with organizational change management. The data process and structure proposed can be viewed as an ontological framework adaptable to various contexts, enabling more informed and efficient asset ranking decisions. This methodology is derived from its application to a metropolitan railway network, where thousands of assets were evaluated, providing a practical approach for conducting criticality assessments in a digitized environment.

The Role of AI in Data Engineering and Integration in Cloud Computing

This article presents a comprehensive analysis of the transformative role of Artificial Intelligence (AI) in revolutionizing data engineering and integration processes within cloud computing environments. The article examines the implementation of AI-driven solutions across multiple dimensions, including automated pipeline generation, intelligent schema matching, anomaly detection, and real-time data integration. Through a mixed-methods approach incorporating both quantitative and qualitative analyses, the article demonstrates significant improvements in data processing efficiency, with organizations achieving up to 67% reduction in processing time and 89% enhancement in accuracy. The article encompasses case studies from financial services, healthcare, and e-commerce sectors, providing concrete evidence of practical applications and scalability. Key findings reveal that AI-powered systems substantially outperform traditional approaches in cost efficiency, scalability, and data quality management, while simultaneously reducing operational overhead. The article also addresses implementation challenges, including legacy system integration and initial deployment complexities, offering strategic insights for organizations pursuing AI integration in their data engineering workflows. These article contribute to the broader understanding of how AI technologies can be effectively leveraged to address the growing challenges of data management in cloud computing environments, while providing a framework for future developments in this rapidly evolving field.

Information disclosure and funding success of green crowdfunding campaigns: a study on GoFundMe

AbstractCrowdfunding has become important in increasing financial support for the development of green technologies. Self-disclosed information significantly affects supporters’ decisions and is important for the success of green project funding. However, current studies still lack investigations into the impact of information disclosure on green crowdfunding performance. This research aims to fill this knowledge gap by exploring eight information disclosure-relevant factors in green crowdfunding performance. Applying machine learning techniques (e.g., Natural Language Processing and Computer Vision) and logistic regression, this study investigates 720 green crowdfunding campaigns on GoFundMe and empirically finds that the duration, length of campaign introductions, and length of the title influence fundraising outcomes. However, no evidence supports the impact of goal size, emotion of campaign introduction, or image content on funding success. This study clarifies the information disclosure-related data that green crowdfunding campaigns should consider and provides founders with a constructive guide to smoothly raise money for a green crowdfunding campaign. This study also contributes to data processing methods by providing future studies with an approach for transferring unstructured data to structured data.

Optimisation of River State Company (RTC) Fleet Management System

In the transportation industry, fleet maintenance is essential for ensuring operational efficiency, reducing costs, and extending vehicle lifespan. As fleets age, maintenance challenges escalate, impacting profitability. This study focuses on developing a maintenance reliability program for the Rivers State Transport Company (RTC) to optimize fleet management and enhance financial performance. The aim of this study is to assess RTC’s maintenance activities and design a reliability-based program that minimizes costs and maximizes profits. A mixed-method survey research design was adopted, with secondary data collected from RTC’s maintenance workshop records and financial reports spanning from 2014 to 2023. The dataset includes maintenance costs, replacement costs, and income for three vehicle models (Nissan Urvan, Toyota Hiace, and Ford Transit) across multiple years. The Dynamic Recursive Programming Model was employed to analyze vehicle age, cost, and income trends, and Microsoft Excel was used for data processing. The results show a significant rise in maintenance costs as vehicles age. For example, the Nissan Urvan’s maintenance costs increased from ₦1.2 million in 2014 to ₦2.6 million in 2023, representing a 116.7% increase over 9 years. Replacement costs for the Toyota Hiace surged from ₦4 million in 2014 to ₦8.5 million in 2023. Income generated by older vehicles also declined by approximately 25%, with the Ford Transit generating ₦5.1 million in 2023 compared to ₦6.8 million in 2014 due to frequent breakdowns and reduced reliability. The analysis further revealed that replacing vehicles after 6-7 years of operation would maximize financial returns, based on the balance of maintenance and income. In conclusion, maintaining an aging fleet presents a substantial financial burden, with rising maintenance costs, diminishing income, and increasing replacement expenses. This study recommends that RTC adopt a structured vehicle replacement policy guided by the Dynamic Recursive Programming Model. Furthermore, reliability-based maintenance strategies should be implemented to reduce unnecessary expenses and improve the overall performance and profitability of the fleet.

Streamlining Patient Data Processing for Payer Compliance with Scalable Big Data Automation

In the fast-changing environment of value-based healthcare, providers must prepare and submit diagnosis files for patients to payers with ever-changing requirements from CMS for contracts. Changing specifications from payers repeatedly requires a flexible system to automatically process items with little reliance on human intervention. It provides a scalable data engineering framework that makes use of PySpark and Apache Airflow to automate and streamline the generation of patient diagnosis files. Its design allows it to be dynamically adapted to changes in specifications, which enhances both operational efficiency and compliance with the automation of complex data transformation processes and large-scale data processing. This framework not only optimizes the reimbursement process but also strengthens the foundations of value-based care by supporting improved patient outcomes and ensuring financial alignment with payer requirements through improving accuracy, timeliness, and reducing manual coding efforts. The proposed framework significantly enhances data processing speed, achieving a 50% reduction in processing time compared to the traditional ETL approach. It offers scalability improvements, allowing data handling from 1 TB to 5 TB—an increase of four times the previous limit—while minimizing manual effort through automation with Airflow, resulting in a 70% reduction in manual intervention.

Empirical and Machine Learning Models for Soil Erosion Risk Assessment: A Case Study of Tsageri Municipality, Georgia

Soil erosion caused by water is one of the most common causes of land degradation worldwide. Within framework of this research soil erosion risk in Tsageri municipality, Georgia was evaluated using Revised Universal Soil Loss Equation (RUSLE) and a machine learning-based Random Forest (RF) model. Open access digital datasets and field observations collected in 2023-2024, which included visually identified erosion areas and GPS-recorded data on the presence or absence of erosion, were utilized in modeling process. Data processing and modeling conducted using ArcGIS Pro 3.0 and RStudio software. According to RUSLE results, 39.7% of the study area falls under the very low erosion risk zone, and 20.7% is in the very high risk zone. The RF model results indicated that 16.5% of the territory is under very low risk of erosion and 13.9% - very high risk. It was observed that RUSLE model tends to overestimate erosion rates on steep, forested slopes, while the RF model, by incorporating additional variables, provided more accurate prediction. These findings suggest that combining RUSLE with machine learning improves soil erosion risk assessment, particularly in complex landscapes such as in Tsageri municipality. Future researche should focus on testing additional variables to refine the modeling process further and enhance predictions. The generated digital thematic maps offer valuable insights for understanding the spatial dynamics of soil erosion within the study area, analyzing the factors driving the process and developing effective mitigation strategies.

Numerical Data Processing by The Implementation of Trees and Graphs

Trees and Graphs play a vital role in transport and logistics. In tree, decision tree is one of the important, not only implemented for data processing, but also considered for Numerical data analysis. The decision tree is a flow chart-like structure, in which each internal node represents a ‘test’ on an attribute, which has a node known as root being at the top, which further divides the given data into branches depending upon the conditions. Every branch consists of a rule, and each leaf node is its outcome. A support tool with a tree-like structure that models probable outcomes, cost of resources, utilities, and possible consequences. Decision trees are also used in operations research along with planning logistics. They can help in determining appropriate plans that will help a company achieve its target. In Graph, Dijkstra's algorithm is an admired algorithm used to find the shortest paths between nodes in a graph, which may represent road networks for example. Dijkstra's algorithm finds the shortest path from a given source node to every other node. It can also be used to find the shortest path to a specific destination node, by concluding the algorithm once the shortest path to the destination node is found. It is also commonly used on graphs where the edge weights are in real numbers. A common application of shortest path algorithms is network routing protocols and also support in route optimization for delivery services, ensuring timely and cost-effective deliveries by finding the best paths for transportation.

Smart Fuel Monitoring for Agricultural Fleet Operations Using Tilt Sensors and Cloud Data Processing

This paper presents a novel Internet of Things (IoT)-based fuel monitoring and tracking system designed to address limitations of traditional fuel gauges and enhance fleet management efficiency. The system leverages an accelerometer and Arduino microcontroller to measure fuel level by detecting tilt angles within the tank. This data, along with real-time location from GPS, is transmitted to a cloud platform for processing and visualization. A mobile application, "Tracer," empowers users with insights into fuel level, location on a map, and distance traveled. Graphical fuel consumption patterns further aid in optimization strategies. This solution offers real-time data for improved fuel efficiency, proactive route planning, and overall fleet management. The proposed system is successfully implemented IOT based technology. This sys in Ada fruit IO cloud issued to provide information about vehicle location, fuel level in tank and distance travelled by the vehicle.

Analysis of positive predictors in diagnosing lung cancer on transthoracic and transbronchial procedure

Background: Lung cancer remains the most cause of death due to cancer. Due to high mortality rate of lung cancer, early diagnosis of lung cancer plays a very important role in therapeutic management. Clinical symptoms and tumor characteristics such as size and location are found to vary in lung cancer patients. Various modalities can be used to obtain materials or samples include transthoracic and transbronchial methods. Research was conducted to analyze positive predictors of cancer between transthoracic and transbronchial procedures in lung cancer patients. Methods: This research is an analytical observational study with a cross-sectional design in using secondary data through medical records from January 2022 to December 2023. Univariate analysis presents data in the form of frequency, mean and standard deviation. Bivariate analysis was carried out using chi-square and multivariate analysis using multiple logistic regression. The measure used as a predictor is the adjusted odds ratio. Inference or conclusion is based on the 95% confident interval and p value at the alpha value limit of 0.05. The entire data process above uses SPSS 26.0 statistical software Results: The total research subjects who met the research requirements were 111 patients. The characteristics of the research subjects were symptoms of cough (n=97; 87.4%), hemoptyisis (n=51; 45.9%), breathlessness (n=104; 93.7%) and chest pain (n=87; 78.4%), mostly peripheral tumor (n=70; 63.1%) and size >3 cm (n=103; 92.8%). Logistic regression analysis showed cough symptoms and peripheral tumor location each had an adjOR value of 5.247 (95% CI 1.432-19.552; p=0.013) and adjOR 0.088 (95% CI 0.034-0.229; p=0.000) for lung cancer positivity on transbronchial procedure. On transthoracic procedure, cough symptoms and peripheral tumor location respectively had an adjOR value of 0.190 (95% CI 0.051-0.703; p=0.013) and adjOR 11.407 (95% CI 4.374-29.747; p=0.000) for lung cancer positivity. Conclusions: Cough is a positive predictor of lung cancer in transbronchial procedures. Meanwhile, peripheral tumor location is a positive predictor of lung cancer in transthoracic procedures.

An empirical study on smart city construction to enhance residents' participation in physical activity.

Smart city construction, as a new model of urban development, has become a crucial means to encourage residents' participation in physical exercise. However, there is still a lack of systematic exploration regarding the mechanisms by which smart city construction specifically affects residents' engagement in physical activity. This paper analyzes the impact of smart city construction on residents' participation in physical activity using a Difference-in-Differences (DID) model, based on data from the 2010 to 2020 China Family Panel Studies (CFPS). Data processing involved removing missing values, addressing outliers, and introducing control variables at the individual, household, and city levels. The study found that smart city construction significantly promotes residents' participation in physical activity. Smart city policies have a notable positive effect on this participation, and there are significant differences in the responses of various resident groups to physical activity engagement. The results of the mediation analysis indicate that the built environment, information access channels, and enjoyment of consumption play significant mediating roles between smart city policies and residents' participation in physical activity. Smart city construction influences residents' participation in physical activity both directly or indirectly by enhancing urban infrastructure, expanding access to information, and fostering enjoyable consumption experiences. In the future, smart city initiatives should prioritize the equitable distribution of resources, strengthen infrastructure development in rural and western regions, and further enhance the national fitness level.

Mathematical foundation of High-Dimensional Data Analysis: Leveraging Topology and Geometry for Enhanced Model Interpretability in AI

One of the most important challenges for modern AI and machine learning is the analysis of high-dimensional data. Traditional methods face serious complications in such cases due to high complexity of datasets: the curse of dimensionality, overfitting, and lack of transparency of model behavior. In this paper, we adopt a novel approach to analyze high-dimensional data; topological and geometric techniques will be exploited, taking advantage of better model interpretability and deeper insights into the structure. Precisely, we discuss Topological Data Analysis, mainly Persistent Homology (Edelsbrunner et al., 2002), which allows the extraction of topological features-like loops and connected components that enable the extracting knowledge about the global structure of data. We also see how some concepts of differential geometry and Riemannian geometry (Do Carmo, 1976) can be used to cast light on manifold data structure lying at the heart of any attempt at modeling intrinsic patterns in high-dimensional spaces. We will review how these mathematical pillars, combined with state-of-the-art techniques for dimensionality reduction like t-SNE, UMAP, Principal Component Analysis, are able to provide interpretable and low-dimensional representations of high-dimensional data that can be used to understand models and make decisions. Case studies are also included, which explain the practical working of these methods in AI systems and show how much complex models can be made transparent using these, especially in domains that are very critical, such as healthcare (Caruana et al., 2015), finance (Chen et al., 2018), and autonomous systems ( Wang et al., 2019). We also discuss some of the difficulties in using these methods for practical applications: computational complexity; the need for large-scale data processing (Bengio et al., 2007); and integration of topological and geometric intuition with the rest of the machine learning pipeline (Zhu et al., 2020). We conclude with possible future directions of research toward fine-tuning these methods and exploring their broader applicability to AI in its quest for more robust, interpretable, and reliable AI models. Given this work, we focus on how linking topology, geometry, and AI bears great promise for solving one of today's critical challenges: model interpretability in high-dimensional data analysis.

Enhancing Fraud Detection Systems through Advanced Data Engineering Techniques

Aims: Fraud remains a persistent issue in various industries, particularly in finance, e-commerce, and healthcare, where traditional rule-based systems have struggled to keep pace with the evolving complexity of fraudulent activities. This study aims to develop an enhanced fraud detection framework by addressing the limitations of traditional rule-based systems, particularly in industries where sophisticated fraud schemes prevail. Study Design: The research utilizes advanced data engineering techniques, including big data analytics, machine learning, and real-time processing, to improve the accuracy and efficiency of fraud detection systems. Place and Duration of Study: The study was conducted over two years across industries with high fraud susceptibility, including financial services, e-commerce platforms, and healthcare organizations. Methodology: The framework integrates various data sources, including transaction logs, user behavior, and external fraud indicators. These datasets were pre-processed through data cleaning, feature engineering, and integration. Supervised and unsupervised machine learning models, such as Random Forest and Gradient Boosting, were applied to detect fraud patterns. Real-time data processing enabled immediate detection and response. The system continuously learned from historical data, adapting to new fraud tactics and improving detection over time. Results: The proposed framework demonstrated a significant improvement in fraud detection accuracy, with machine learning models achieving over 90% accuracy rates. There was also a 30% reduction in false positives compared to traditional methods, and detection times were shortened by 40%, enabling faster identification and mitigation of emerging fraud schemes. Conclusion: This study concludes that integrating advanced data engineering techniques with machine learning significantly enhances fraud detection systems' accuracy, scalability, and adaptability. While promising, further improvements are needed, particularly in addressing the evolving nature of fraud schemes and ensuring the scalability of real-time data processing. These areas present opportunities for future research and development.

The Role of Digital Marketing Technologies in Enhancing Business Process Management

In the many stages of the digital revolution's progress, digitalisation has strong foundations and significant turning points. The word "digit" was derived etymologically from the Latin phrase "digitalis." The practice of counting with fingers and toes numbers lower than ten was commonly referred to by this phrase in the middle of the fifteenth century. Going forward to the 1650's numerical meaning, data processing on computers began in 1938, replacing analogue methods. 1945 marked yet another turning point in the rapid processing of evolutionary changes, before the 1960s saw the emergence of extensive data collection and broadcasting of events. Marketing operates through an integrated method that combines the arts and sciences. The arts create demand, while the sciences assess market potential and the company's ability to meet that need. It is harder to market services than it is to sell items. The field of digital marketing has grown more dynamic and complex. Technology is constantly requiring exceptional upgrades due to its rapid changes, diversified applications, version modifications, and advanced generations. A company's ability and desire to adapt to the evolving form, function, and structure of digital technology is a prerequisite for any stable reliance on it.

Determining the Boundaries of Overlying Strata Collapse Above Mined-Out Panels of Zhomart Mine Using Seismic Data

The present article is devoted to the issue of studying the patterns of displacement of superincumbent rock over panels of a mine obtained using advanced seismic technologies, allowing for the study of the boundaries of caving zones in the depths of rock mass. A seismic exploration has been performed in local areas of Zhomart mine responsible for the development of Zhaman-Aybat cuprous sandstone deposits in Central Kazakhstan at the stage of repeated mining with pulling of previously non-mined ore pillars and superincumbent rock caving. A 2D field seismic exploration has been accomplished, totaling to 8000-line m of seismic lines using seismic shot point. The survey depth varied from 455 m to 625 m. The state-of-the-art technologies of kinematic and dynamic analysis of wavefield have been widely used during data processing and interpretation targeted at identifying anomalies associated with the structural heterogeneity of the pays and rock mass, engaging modern algorithms and mathematical apparatuses of specialized geodata processing systems. The above effort resulted in new data regarding the location and morphology of the reflectors, characterizing geological heterogeneity of the section, zones of smooth rock displacement, and displacement of strata with significant disturbance of the rocks overlying mined-out productive pay. The potential of the application of modern 2D seismic exploration to studying an underworked zone with altered physical and mechanical properties located over an ore deposit has been assessed. The novelty and practical significance of the research lies in the determination of the boundaries of zones of displacement and superincumbent rock caving over the panels obtained using state-of-the-art technologies of seismic exploration. The deliverables may be used to improve the process of recognizing specific types of technogenic heterogeneities in the rock mass, impacting the efficiency and safety of subsurface ore mining, both for localization and mining monitoring.

An introduction for non-experts on using X-ray micro computed tomography as a tool for pore scale digital subsurface characterisation of siliciclastic materials

This paper presents an overview of using X-ray micro computed tomography (μCT) as a valuable tool for micro scale investigation of siliciclastic materials. When processed using digital image analysis (DIA), valuable quantitative data can be extracted from μCT 3D images. Subsurface reservoirs are of great importance to society as fluid-bearing formations, but also as storage reservoirs for carbon dioxide. μCT imaging has the capability to perform preliminary, highly detailed investigations of potential reservoirs. This approach has a range of benefits when compared to traditional 2D techniques, such as optical and scanning electron microscopy (SEM). Key advantages include the technique being non-destructive and capable of 3D and 4D visualisation. This facilitates rapid repeated digital measurements and experiments on microstructures. Digital samples can also be readily shared within the scientific community to replicate results and quickly launch new investigations. However, limitations still exist, posing challenges to the wider application of such a methodology. Such limitations include the identification of a representative elementary volume (REV), computational cost, and suitable processing of the output image data. Here, we highlight the value of using μCT and DIA, from our first experiences, to facilitate pore scale siliciclastic reservoir characterisation, but also highlight our perceived limitations and barriers to its much wider application. This paper introduces the key processing stages, opportunities and limitations of these techniques.

Towards Precision Biocatalysis ‐ Leveraging Inline NMR for Autonomous Experimentation in Flow Reactors

AbstractReactor automation is a transformative force for chemical processes, but the potential of reaction monitoring for machine‐assisted autonomous biocatalytic reaction optimization is still largely unexplored. To address this gap, we report on automated reactor optimization for biocatalytic flow‐through microreactors. For this purpose, the inline NMR analysis of an enzymatically catalyzed stereoselective reduction of a prochiral diketone was combined with a self‐developed open‐source analysis and control software. The algorithm is continuously fed with spectra from a benchtop NMR instrument acquired from a reaction solution from a microreactor filled with biocatalytically active materials and adjusts the flow rate of the pumps to achieve predetermined target concentrations of the product. We show that through this automated coupling of data analysis and process parameterization, for example, maximum conversion efficiency can be achieved for a given bioreactor. This work illustrates the potential of inline NMR reaction monitoring for biocatalytic processes and provides a starting point for innovation to develop automated processes for precision biocatalysis through integrated data analysis.

Research on Big Data Platform Deployment and Maintenance Course Practical Teaching based on "Job-Curriculum-Competition-Certification + Ideological and Political Integration"

With the rapid development of the big data industry, the demand for big data technical talents has increased sharply. The big data technology major in higher vocational colleges aims to cultivate high-quality skilled talents with capabilities in data collection, processing, operation and maintenance, and technical services. The major integrates the core skills of big data implementation and operation and maintenance positions into professional courses and conducts teaching practice based on the "Job-Curriculum-Competition- Certification + Ideological and Political Integration" model. By constructing an education system that integrates knowledge imparting, ability cultivation, and value shaping, ideological and political education is integrated into course teaching, achieving an organic combination of professional teaching and ideological and political education content. At the same time, a blended teaching mode is adopted, focusing on the comprehensive cultivation of students' knowledge, abilities, and qualities, using competitions to promote learning and training, and improving the quality of talent cultivation.