Data Integration Techniques Research Articles

Infrared and Visible Camera Integration for Detection and Tracking of Small UAVs: Systematic Evaluation

Given the recent proliferation of Unmanned Aerial Systems (UASs) and the consequent importance of counter-UASs, this project aims to perform the detection and tracking of small non-cooperative UASs using Electro-optical (EO) and Infrared (IR) sensors. Two data integration techniques, at the decision and pixel levels, are compared with the use of each sensor independently to evaluate the system robustness in different operational conditions. The data are submitted to a YOLOv7 detector merged with a ByteTrack tracker. For training and validation, additional efforts are made towards creating datasets of spatially and temporally aligned EO and IR annotated Unmanned Aerial Vehicle (UAV) frames and videos. These consist of the acquisition of real data captured from a workstation on the ground, followed by image calibration, image alignment, the application of bias-removal techniques, and data augmentation methods to artificially create images. The performance of the detector across datasets shows an average precision of 88.4%, recall of 85.4%, and mAP@0.5 of 88.5%. Tests conducted on the decision-level fusion architecture demonstrate notable gains in recall and precision, although at the expense of lower frame rates. Precision, recall, and frame rate are not improved by the pixel-level fusion design.

Fog Computing Enabled Adaptive Prognosis of Cutting Tool Remaining Life through Multi-source Data

Abstract Cutting tool remaining life during machining processes is essential for achieving sustainable production. Traditional prognosis methods are often crippled by the inability to adapt to diverse working conditions across the machining process lifecycle. This paper introduces a fog computing-enabled adaptive prognosis framework utilising multi-source data to address these challenges effectively. The key innovations include: (1) The proposed system integrates power and vibration data collected from LGMazak VTC-16A and IRON MAN QM200 machines. A standardised data fusion method combines multi-source data to enhance robustness and accuracy. (2) The transformer model is employed to improve prognosis accuracy of cutting tool remaining life, best accuracy of 98.24% and an average accuracy of 97.63% are achieved. (3) Finite Element Analysis (FEA) is incorporated to validate the model's predictions to validate reliability of deep learning model. (4) The fog computing optimisation mechanism based on the bees algorithm, which shows fitness value of 0.92 and convergence within 15 iterations. The proposed method reduces total data volume in cloud by 54.12%, prediction time by 33.64% and time complexity in the cloud layer by 4.62%. The effectiveness of fog computing in enhancing the operational efficiency and reliability of manufacturing systems is validated through advanced data integration and deep learning techniques.

Patient-centric knowledge graphs: a survey of current methods, challenges, and applications.

Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care by mapping the patient's health information holistically and multi-dimensionally. PCKGs integrate various types of health data to provide healthcare professionals with a comprehensive understanding of a patient's health, enabling more personalized and effective care. This literature review explores the methodologies, challenges, and opportunities associated with PCKGs, focusing on their role in integrating disparate healthcare data and enhancing patient care through a unified health perspective. In addition, this review also discusses the complexities of PCKG development, including ontology design, data integration techniques, knowledge extraction, and structured representation of knowledge. It highlights advanced techniques such as reasoning, semantic search, and inference mechanisms essential in constructing and evaluating PCKGs for actionable healthcare insights. We further explore the practical applications of PCKGs in personalized medicine, emphasizing their significance in improving disease prediction and formulating effective treatment plans. Overall, this review provides a foundational perspective on the current state-of-the-art and best practices of PCKGs, guiding future research and applications in this dynamic field.

Biofuser: a multi-source data fusion platform for fusing the data of fermentation process devices.

In the past decade, the progress of traditional bioprocess optimization technique has lagged far behind the rapid development of synthetic biology, which has hindered the industrialization process of synthetic biology achievements. Recently, more and more advanced equipment and sensors have been applied for bioprocess online inspection to improve the understanding and optimization efficiency of the process. This has resulted in large amounts of process data from various sources with different communication protocols and data formats, requiring the development of techniques for integration and fusion of these heterogeneous data. Here we describe a multi-source fusion platform (Biofuser) that is designed to collect and process multi-source heterogeneous data. Biofuser integrates various data to a unique format that facilitates data visualization, further analysis, model construction, and automatic process control. Moreover, Biofuser also provides additional APIs that support machine learning or deep learning using the integrated data. We illustrate the application of Biofuser with a case study on riboflavin fermentation process development, demonstrating its ability in device faulty identification, critical process factor identification, and bioprocess prediction. Biofuser has the potential to significantly enhance the development of fermentation optimization techniques and is expected to become an important infrastructure for artificial intelligent integration into bioprocess optimization, thereby promoting the development of intelligent biomanufacturing.

Employing Multi-Omics Analyses to Understand Changes during Kidney Development in Perinatal Interleukin-6 Animal Model.

Chronic kidney disease (CKD) is a leading cause of morbidity and mortality globally. Maternal obesity during pregnancy is linked to systemic inflammation and elevated levels of the pro-inflammatory cytokine interleukin-6 (IL-6). In our previous work, we demonstrated that increased maternal IL-6 during gestation impacts intrauterine development in mice. We hypothesized that IL-6-induced inflammation alters gene expression in the developing fetus. To test this, pregnant mice were administered IL-6 or saline during mid-gestation. Newborn mouse kidneys were analyzed using mRNA-seq, miRNA-seq and whole-genome bisulfite-seq (WGBS). A multi-omics approach was employed to quantify mRNA gene expression, miRNA expression and DNA methylation, using advanced bioinformatics and data integration techniques. Our analysis identified 19 key genes present in multiple omics datasets, regulated by epigenetics and miRNAs. We constructed a regulatory network for these genes, revealing disruptions in pathways such as Mannose type O-glycan biosynthesis, the cell cycle, apoptosis and FoxO signaling. Notably, the Atp7b gene was regulated by DNA methylation and miR-223 targeting, whereas the Man2a1 gene was controlled by DNA methylation affecting energy metabolism. These findings suggest that these genes may play a role in fetal programming, potentially leading to CKD later in life due to gestational inflammation.

The Value Attribution Solution: A Novel Approach to Financial Allocation and Performance Evaluation in Organizations

This paper introduces the Value Attribution Solution , a novel approach to financial allocation and performance tracking that enables organizations to monitor and allocate both costs and revenues at an unprecedented level of detail. By leveraging advanced data integration techniques and multi-dimensional analysis, the framework provides organizations with real-time insights into value creation and resource utilization across various operational dimensions. This research presents the theoretical foundations of the solution, details its methodology and implementation process, and discusses its potential impact on organizational performance and decision-making. Through a series of case studies and empirical analyses, we demonstrate the solution's effectiveness in enhancing financial performance, operational efficiency, and strategic planning across diverse industries. The solution represents a significant advancement in management accounting and financial control systems, offering a powerful tool for organizations seeking to optimize their resource allocation and value creation processes in an increasingly complex and dynamic business environment.

Designing cost-effective data solutions: Best practices from multi-million dollar projects involving cross-functional teams and diverse data sources

This paper explores the design and implementation of cost-effective data solutions, focusing on multi-million dollar projects that involve cross-functional teams and diverse data sources. The study aims to identify best practices and technological advancements that enhance data management efficiency, ensuring scalability, security, and compliance. Employing a comprehensive review of existing literature and case studies, the paper examines the challenges and opportunities in integrating technologies such as AI, machine learning, cloud computing, and blockchain. Key findings highlight the importance of cross-functional collaboration in addressing data management complexities and the adoption of agile methodologies to enhance adaptability and efficiency. The study also underscores the significance of advanced data integration techniques and the application of circular economy principles to promote sustainability in data management. The conclusion provides actionable recommendations for organizations seeking to optimize their data solutions, emphasizing the need for strategic investment in emerging technologies and a robust data governance framework. By offering insights into future trends and research directions, this paper contributes to the ongoing discourse on developing cost-effective, secure, and sustainable data solutions in an increasingly digitalized world. Keywords: Cost-Effective Data Solutions, Cross-Functional Teams, Data Integration, Cybersecurity, Artificial Intelligence, Circular Economy Principles

Bayesian mapping of protein kinases to vasopressin-regulated phosphorylation sites in renal collecting duct.

Vasopressin controls water permeability in the renal collecting duct by regulating the water channel protein, aquaporin-2 (AQP2). Phosphoproteomic studies have identified multiple proteins that undergo phosphorylation changes in response to vasopressin. The kinases responsible for the phosphorylation of most of these sites have not been identified. Here, we use large-scale Bayesian data integration to predict the responsible kinases for 51 phosphoproteomically identified vasopressin-regulated phosphorylation sites in the renal collecting duct. To do this, we applied Bayes' rule to rank the 515 known mammalian protein kinases for each site. Bayes' rule was applied recursively to integrate each of the seven independent datasets, each time using the posterior probability vector of a given step as the prior probability vector of the next step. In total, 30 of the 33 phosphorylation sites that increase with vasopressin were predicted to be phosphorylated by protein kinase A (PKA) catalytic subunit-α, consistent with prior studies implicating PKA in vasopressin signaling. Eighteen of the vasopressin-regulated phosphorylation sites were decreased in response to vasopressin and all but three of these sites were predicted to be targets of extracellular signal-regulated kinases, ERK1 and ERK2. This result implies that ERK1 and ERK2 are inhibited in response to vasopressin V2 receptor occupation, secondary to PKA activation. The six phosphorylation sites not predicted to be phosphorylated by PKA or ERK1/2 are potential targets of other protein kinases previously implicated in aquaporin-2 regulation, including cyclin-dependent kinase 18 (CDK18), calmodulin-dependent kinase 2δ (CAMK2D), AMP-activated kinase catalytic subunit-α-1 (PRKAA1) and CDC42 binding protein kinase β (CDC42BPB).NEW & NOTEWORTHY Vasopressin regulates water transport in the renal collecting duct in part through phosphorylation or dephosphorylation of proteins that regulate aquaporin-2. Prior studies have identified 51 vasopressin-regulated phosphorylation sites in 45 proteins. This study uses Bayesian data integration techniques to combine information from multiple prior proteomics and transcriptomics studies to predict the protein kinases that phosphorylate the 51 sites. Most of the regulated sites were predicted to be phosphorylated by protein kinase A or ERK1/ERK2.

Blockchain‐based transparent and certificateless data integrity auditing for cloud storage

SummaryCloud storage provides a convenient way of collaboration and sharing. An increasing number of users are becoming more open to sharing their data with others. Consequently, the integrity of shared data has started to draw significant attention due to the loss control of it. Remote data integrity techniques can solve this problem. To resist the collusion between the third party auditor and the cloud server, existing integrity auditing schemes utilize blockchain to replace the third party auditor to do some work. However, these schemes still involve collusion between the third party auditor and the miners and cannot guarantee the third party auditor to execute the auditing process honestly. Considering this, we propose a blockchain‐based transparent and certificateless data integrity auditing scheme. Specifically, we design the smart contract combining the commitment technology to generate the challenge information and record the auditing information. Besides, we combine certificateless cryptography and the blind technology to achieve the privacy‐preserving. The proposed scheme can resist collusion, improve the transparency of the auditing process, protect the data privacy and reduce the overhead of certificate management and key management. The security analysis and performance evaluation demonstrate the security and efficiency of the scheme.

Data integrity in an online world: Demonstration of multimodal bot screening tools and considerations for preserving data integrity in two online social and behavioral research studies with marginalized populations.

Internet-based studies are widely used in social and behavioral health research, yet bots and fraud from "survey farming" bring significant threats to data integrity. For research centering marginalized communities, data integrity is an ethical imperative, as fraudulent data at a minimum poses a threat to scientific integrity, and worse could even promulgate false, negative stereotypes about the population of interest. Using data from two online surveys of sexual and gender minority populations (young men who have sex with men and transgender women of color), we (a) demonstrate the use of online survey techniques to identify and mitigate internet-based fraud, (b) differentiate techniques for and identify two different types of "survey farming" (i.e., bots and false responders), and (c) demonstrate the consequences of those distinct types of fraud on sample characteristics and statistical inferences, if fraud goes unaddressed. We provide practical recommendations for internet-based studies in psychological, social, and behavioral health research to ensure data integrity and discuss implications for future research testing data integrity techniques. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Transforming Clinical Research: The Power of High-Throughput Omics Integration.

High-throughput omics technologies have dramatically changed biological research, providing unprecedented insights into the complexity of living systems. This review presents a comprehensive examination of the current landscape of high-throughput omics pipelines, covering key technologies, data integration techniques and their diverse applications. It looks at advances in next-generation sequencing, mass spectrometry and microarray platforms and highlights their contribution to data volume and precision. In addition, this review looks at the critical role of bioinformatics tools and statistical methods in managing the large datasets generated by these technologies. By integrating multi-omics data, researchers can gain a holistic understanding of biological systems, leading to the identification of new biomarkers and therapeutic targets, particularly in complex diseases such as cancer. The review also looks at the integration of omics data into electronic health records (EHRs) and the potential for cloud computing and big data analytics to improve data storage, analysis and sharing. Despite significant advances, there are still challenges such as data complexity, technical limitations and ethical issues. Future directions include the development of more sophisticated computational tools and the application of advanced machine learning techniques, which are critical for addressing the complexity and heterogeneity of omics datasets. This review aims to serve as a valuable resource for researchers and practitioners, highlighting the transformative potential of high-throughput omics technologies in advancing personalized medicine and improving clinical outcomes.

Integrating business intelligence and predictive analytics in banking: A framework for optimizing financial decision-making

Integrating Business Intelligence (BI) and predictive analytics in banking has become a pivotal strategy for optimizing financial decision-making. This review paper explores this integration's theoretical foundations, current trends, and challenges while proposing a comprehensive framework to enhance decision-making processes. The proposed framework emphasizes the importance of a unified data architecture, advanced data integration techniques, and the synergistic use of BI tools and predictive models. By examining the implications for banking institutions, the paper highlights how this integration can transform strategic and operational decisions, improve risk management, and drive personalized customer engagement. Additionally, future trends such as the adoption of AI, real-time analytics, and enhanced data governance are discussed, along with recommendations for banks aiming to implement this integration successfully. The findings underscore the transformative potential of combining BI and predictive analytics to foster innovation, efficiency, and competitive advantage in the banking sector. Keywords: Business Intelligence (BI), Predictive Analytics, Banking Sector, Financial Decision-Making, Data Integration.

Effect of Data Integration Techniques on Operational Efficiency in Manufacturing Industries in Iran

Purpose: The aim of the study was to assess the effect of data integration techniques on operational efficiency in manufacturing industries in Iran. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that by seamlessly amalgamating data from disparate sources across production lines, supply chains, and customer feedback systems, manufacturers have been able to streamline processes, optimize resource allocation, and achieve significant cost savings. For instance, real-time data integration facilitates timely decision-making, allowing for adaptive production planning and inventory management. Furthermore, the integration of advanced analytics and machine learning algorithms has enabled predictive maintenance strategies, reducing downtime and enhancing overall equipment effectiveness (OEE). These technological advancements not only bolster operational efficiency but also foster innovation, as companies leverage integrated data insights to drive continuous improvement initiatives and meet evolving consumer demands. Implications to Theory, Practice and Policy: Resource-based view (RBV), technology acceptance model and dynamic capabilities theory may be used to anchor future studies on assessing the effect of data integration techniques on operational efficiency in manufacturing industries in Iran. Manufacturing firms should prioritize strategic implementation plans for data integration technologies to ensure that investments in IoT, AI, ETL, data warehousing, data virtualization, and APIs are aligned with their overall business objectives. Policymakers should provide incentives and support for the adoption of advanced data integration technologies in the manufacturing sector.

Multi-Level Characterization of Lignite Mine Waste by the Integration of Wide Wavelength Range Infrared Spectroscopy

Both the mineralogy and geochemistry of coal mine waste presents environmental and social challenges while simultaneously offering the potential source for recovery of metals, including critical raw materials (CRMs). Assessing these challenges and opportunities requires effective waste management strategies and comprehensive material characterization. This study deals with the integration of analytical data obtained from various portable sensor technologies. Infrared reflection spectroscopy (covering a wide wavelength range of 0.4 to 15 µm), and geochemical x-ray fluorescence (XRF) were utilized to differentiate between samples belonging to various geological lithologies and quantify elements of interest. Therefore, we developed a methodological framework that encompasses data integration and machine learning techniques. The model developed using the infrared data predicts the Sr concentration with a model accuracy of R2 = 0.77 for the testing dataset; however, the model performances decreased for predicting other elements such as Pb, Zn, Y, and Th. Despite these limitations, the approach demonstrates better performance in discriminating materials based on both mineralogical and geochemical compositions. Overall, the developed methodology, enables rapid and in-situ determination of coal mine waste composition, providing insights into waste composition that are directly linked to potential environmental impact, and the possible recovery of economically valuable metals.

Opportunities and Challenges in Soft Tissue Sarcoma Risk Stratification in the Era of Personalised Medicine.

Soft tissue sarcomas (STS) are a rare and heterogeneous group of cancers. Treatment options have changed little in the past thirty years, and the role of neoadjuvant chemotherapy is controversial. Accurate risk stratification is crucial in STS in order to facilitate clinical discussions around peri-operative treatment. Current risk stratification tools used in clinic, such as Sarculator, use clinicopathological characteristics and may be specific to anatomical site or to histology. More recently, risk stratification tools have been developed using molecular or immunological data. Combining Sarculator with other risk stratification tools may identify novel patient groups with differential clinical outcomes. There are several considerations when translating risk stratification tools into widespread clinical use, including establishing clinical utility, health economic value, being applicable to existing clinical pathways, having strong real-world performance, and being supported by investment into infrastructure. Future work may include incorporation of novel modalities and data integration techniques.

A Review of Integration Techniques of Multi-Geoscience Data-Sets in Mineral Prospectivity Mapping

In every sphere and utility aspects of human life, there is need of metals and construction materials. Minerals which are below the near subsurface is almost explored on the basis of direct geospatial evidences. There is high demand of metals and other materials which are mined below the surface of earth In the current landscape, there's a demand for faster and more precise exploration strategies, particularly emphasizing Greenfield exploration and deep-seated mineralization. This paper conducts a comprehensive review of existing methodologies for integrating multi-geoscience datasets aimed at mineral prognostication, with a focus on identifying the most precise and authentic Artificial Intelligence (AI) - based data integration techniques. Additionally, it offers insights into the current status of mineral exploration in India and the global evolution of data integration practices. Several types of geoscientific datasets i.e. geological, geophysical, geochemical and geospectral data have to be organized in geospatial domain for meaningful mineral exploration outcome. These datasets have been processed to extract exploratory indicator layers for data integration are called Mineral Prospectivity Mapping (MPM). Indeed, MPM is a multiple criterion decision making (MCDM) task which provide a predictive model for categorizing of sought areas in terms of ore mineralization. There after based upon Geological factors i.e. lithology, structure, shear & fault zones, alteration zones etc. of sought mineralized area, selection of drilling parameters (depth, angle, level, type, rpm, feed) is done for resource assessment. Literature survey suggests that minerals exploration by integrated approach on the basis of these datasets is still poorly performed. It has been gathered that knowledge-driven data integration using Fuzzy Gamma Operator and Multiclass Index Overlay method is best suited for mineral exploration. In past, few researchers of other countries have exploited data integration approach with encouraging results. Despite the abundance of data available in India, this approach has not been utilized very successfully and no standard protocols exist even for decision making for drilling operation. Thus, it's evident that employing the Fuzzy Inference System (FIS) algorithm, particularly utilizing the Fuzzy Gamma Operator and Multiclass Index Overlay integration method, remains underutilized in designing standardized operating procedures (SOP) for mineral exploration in India and decision-making for drilling operations. This approach holds promise for minimizing time lag and optimizing resources such as manpower, instruments, and finances.

Enhancing Vehicle Routing Efficiency through Branch and Bound and Heuristic Methods

The Vehicle Routing Problem (VRP) is a critical challenge in logistics, impacting delivery efficiency and costs. Traditional VRP solutions often fail to address real-world dynamics such as fluctuating traffic conditions and varying customer demands. This research proposes a novel VRP model integrating real-time data to enhance route optimization. By combining the precision of the Branch and Bound (B&B) approach with the flexibility of heuristics like Genetic Algorithms and Simulated Annealing, the hybrid method dynamically adjusts routes based on live traffic and demand updates. The objective is to reduce operational costs and improve logistical performance. The hybrid model’s effectiveness is validated through comparative analysis with traditional VRP solutions, demonstrating significant improvements in cost reduction, fuel consumption, vehicle wear and tear, and customer satisfaction due to timely deliveries. These advancements highlight the potential of real-time data integration and advanced optimization techniques in providing robust solutions for modern logistics challenges. Future research should focus on incorporating more advanced data sources and testing the model in various real-world scenarios to further enhance its practicality and performance, ensuring businesses remain competitive in a dynamic market. This study underscores the importance of continuous innovation in VRP solutions to achieve sustainable, efficient, and customer-centric logistics operations.

Enhancing business intelligence in e-commerce: Utilizing advanced data integration for real-time insights

This paper explores how advanced data integration techniques and real-time insights can significantly enhance BI in the e-commerce sector. By leveraging methods such as ETL, ELT, data virtualization, API integration, and streaming data integration, e-commerce businesses can achieve a comprehensive, unified view of their data. The integration of real-time data enables dynamic pricing, personalized customer experiences, efficient inventory management, proactive fraud detection, and improved customer support. Despite challenges such as data silos, data quality issues, scalability, security, and technical complexity, solutions like data governance, cloud computing, and advanced analytics tools can address these obstacles. Future trends, including artificial intelligence, machine learning, edge computing, block chains, and data fabric, promise to further transform BI in e-commerce. Ultimately, advanced data integration and real-time insights are essential for e-commerce companies to stay competitive, optimize operations, and enhance customer satisfaction. In the fast-paced and competitive world of e-commerce, the ability to make quick, informed decisions is crucial for success. Business Intelligence (BI) plays a vital role by transforming vast amounts of data into actionable insights that drive strategic decisions and operational efficiency. This paper explores how advanced data integration techniques and real-time insights can significantly enhance BI in the e-commerce sector. By leveraging methods such as ETL, ELT, data virtualization, API integration, and streaming data integration, e-commerce businesses can achieve a comprehensive, unified view of their data. The integration of real-time data enables dynamic pricing, personalized customer experiences, efficient inventory management, proactive fraud detection, and improved customer support. Despite challenges such as data silos, data quality issues, scalability, security, and technical complexity, solutions like data governance, cloud computing, and advanced analytics tools can address these obstacles. Future trends, including artificial intelligence, machine learning, edge computing, block chain, and data fabric, promise to further transform BI in e-commerce. Case studies from leading e-commerce companies illustrate the practical benefits of implementing these advanced techniques, showcasing significant improvements in operational efficiency and customer satisfaction. Keywords: Business Intelligence, E-commerce Analytics, Data Integration, Real-Time Insights.

A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology.

Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims to comprehensively summarize and categorize these advancements, focusing on the integration of multi-omics data, which includes genomics, transcriptomics, proteomics and metabolomics, alongside clinical data. We discuss various machine learning techniques and computational methodologies used for integrating distinct omics datasets and provide valuable insights into their application. The review emphasizes both the challenges and opportunities present in multi-omics data integration, precision medicine and patient stratification, offering practical recommendations for method selection in various scenarios. Recent advances in deep learning and network-based approaches are also explored, highlighting their potential to harmonize diverse biological information layers. Additionally, we present a roadmap for the integration of multi-omics data in precision oncology, outlining the advantages, challenges and implementation difficulties. Hence this review offers a thorough overview of current literature, providing researchers with insights into machine learning techniques for patient stratification, particularly in precision oncology. Contact: anirban@klyuniv.ac.in.

A Review of Intelligent Research Dynamics in Oil and Gas Exploration and Development

The intelligentization of oil and gas exploration and development has attracted much attention in recent years. This involves artificial intelligence, big data analysis, and other cutting-edge technologies and methods. The paper explores the trend of intelligent development within the oil and gas industry, focusing on the utilization of large-scale data collection, integration, and analysis techniques. It delves into the in-depth exploration of geological, geophysical, production, and other pertinent information to deliver more comprehensive and precise data support for the exploration and development processes. Continuous advancements in big data and artificial intelligence technologies further refine solutions, ensuring greater accuracy and efficiency in oil and gas exploration and development. Data-driven intelligent technologies play a pivotal role in the exploration and development of oil and gas resources, providing a robust foundation for the industry's intelligent advancement. While intelligent technology presents significant opportunities, it also presents a myriad of challenges. Thus, collaboration and knowledge-sharing will serve as the primary catalysts for field development, facilitating the exchange of technology and expertise to collectively tackle challenges in the future.