Improve Data Quality Research Articles

Land Characterization System Software: Implementing Land Cover Ontology

Land cover (LC) plays a crucial role in the monitoring and planning of the environment among various domains, such as climate change, the management of natural resources, and sustainable development. However, inconsistent LC legends hamper their usability, particularly as technologies, like remote sensing, rapidly improve data quality and quantity for extracting valuable information. Ontologies play a pivotal role in improving the standardization and harmonization of different LC taxonomies, enabling both a reduction in inconsistencies and a way to harness ever-increasing computing power to help scientists provide faster and better answers. However, using ontologies without suitable tools, especially when expressive power is matched with complexity, can prove a daunting task. This paper introduces the land characterization system (LCHS), an innovative tool built to support the ontology discussed in the ISO 19144-2 standard, known as Land Cover Meta Language (LCML). The LCHS can help streamline and speed up the creation and editing of LC legends using a data-driven design approach.

Smart grid public datasets: Characteristics and associated applications

AbstractThe development of smart grids, traditional power grids, and the integration of internet of things devices have resulted in a wealth of data crucial to advancing energy management and efficiency. Nevertheless, public datasets remain limited due to grid operators' and companies' reluctance to disclose proprietary information. The authors present a comprehensive analysis of more than 50 publicly available datasets, organised into three main categories: micro‐ and macro‐consumption data, detailed in‐home consumption data (often referred to as non‐intrusive load monitoring datasets or building data) and grid data. Furthermore, the study underscores future research priorities, such as advancing synthetic data generation, improving data quality and standardisation, and enhancing big data management in smart grids. The aim of the authors is to enable researchers in the smart and power grid a comprehensive reference point to pick suitable and relevant public datasets to evaluate their proposed methods. The provided analysis highlights the importance of following a systematic and standardised approach in evaluating future methods and directs readers to future potential venues of research in the area of smart grid analytics.

Assessment of BRCA1 and BRCA2 Germline Variant Data From Patients With Breast Cancer in a Real-World Data Registry.

The emergence of large real-world clinical databases and tools to mine electronic medical records has allowed for an unprecedented look at large data sets with clinical and epidemiologic correlates. In clinical cancer genetics, real-world databases allow for the investigation of prevalence and effectiveness of prevention strategies and targeted treatments and for the identification of barriers to better outcomes. However, real-world data sets have inherent biases and problems (eg, selection bias, incomplete data, measurement error) that may hamper adequate analysis and affect statistical power. Here, we leverage a real-world clinical data set from a large health network for patients with breast cancer tested for variants in BRCA1 and BRCA2 (N = 12,423). We conducted data cleaning and harmonization, cross-referenced with publicly available databases, performed variant reassessment and functional assays, and used functional data to inform a variant's clinical significance applying American College of Medical Geneticists and the Association of Molecular Pathology guidelines. In the cohort, White and Black patients were over-represented, whereas non-White Hispanic and Asian patients were under-represented. Incorrect or missing variant designations were the most significant contributor to data loss. While manual curation corrected many incorrect designations, a sizable fraction of patient carriers remained with incorrect or missing variant designations. Despite the large number of patients with clinical significance not reported, original reported clinical significance assessments were accurate. Reassessment of variants in which clinical significance was not reported led to a marked improvement in data quality. We identify the most common issues with BRCA1 and BRCA2 testing data entry and suggest approaches to minimize data loss and keep interpretation of clinical significance of variants up to date.

Application and Prospects of Artificial Intelligence Technology in Early Screening of Chronic Obstructive Pulmonary Disease at Primary Healthcare Institutions in China.

Chronic Obstructive Pulmonary Disease (COPD), as one of the major global health threat diseases, particularly in China, presents a high prevalence and mortality rate. Early diagnosis is crucial for controlling disease progression and improving patient prognosis. However, due to the lack of significant early symptoms, the awareness and diagnosis rates of COPD remain low. Against this background, primary healthcare institutions play a key role in identifying high-risk groups and early diagnosis. With the development of Artificial Intelligence (AI) technology, its potential in enhancing the efficiency and accuracy of COPD screening is evident. This paper discusses the characteristics of high-risk groups for COPD, current screening methods, and the application of AI technology in various aspects of screening. It also highlights challenges in AI application, such as data privacy, algorithm accuracy, and interpretability. Suggestions for improvement, such as enhancing AI technology dissemination, improving data quality, promoting interdisciplinary cooperation, and strengthening policy and financial support, aim to further enhance the effectiveness and prospects of AI technology in COPD screening at primary healthcare institutions in China.

Free-breathing motion corrected magnetic resonance elastography of the abdomen.

Magnetic resonance elastography (MRE) is a non-invasive method to measure the viscoelastic properties of tissue and has been applied in multiple abdominal organs. However, abdominal MRE suffers from detrimental breathing motion causing misalignment of structures between repeated acquisitions for different MRE dimensions (e.g., motion encoding directions and wave phase offsets). This study investigated motion correction strategies to resolve all breathing motion on sagittal free-breathing MRE acquisitions in a phantom, in healthy volunteers and showed feasibility in patients. First, in silico experiments were performed on a static phantom dataset with simulated motion. Second, eight healthy volunteers underwent two sagittal MRE acquisitions in the pancreas and right kidney. The multi-frequency free-breathing spin-echo echo-planar-imaging (SE-EPI) MRE consisted of four frequencies (30, 40, 50, 60 Hz), eight wave-phase offsets, with 3 mm3 isotropic voxel size. Following data re-sorting in different number of motion states (4 till 12) based on respiratory waveform signal, three intensity-based registration methods (monomodal, multimodal, and phase correlation) and non-rigid local registration were compared. A ranking method was used to determine the best registration method, based on seven signal-to-noise and image quality measures. Repeatability was assessed for no motion correction (Original) and the best performing method (Best) using Bland-Altman analysis. Lastly, the best motion correction method was compared to no motion correction on patient MRE data [pancreatic ductal adenocarcinoma (PDAC, n=5) and metabolic dysfunction-associated steatotic liver disease (MASLD) (n=1)]. In silico experiments showed a deviation of shear wave speed (SWS) with simulated motion to the ground truth, which was (partially) resolved using motion correction. In healthy volunteers ranking resulted in the best motion correction method of monomodal registration using nine motion states, while no motion correction was ranked last. Limits of agreement were (-0.18, 0.14), and (-0.25, 0.18) m/s for Best and Original, respectively. Using motion correction in patients resulted in a significant increase in SWS in the pancreas (Original: 1.39±0.10 and Best: 1.50±0.17 m/s). After motion correction PDAC had a mean SWS of 1.56±0.27 m/s (Original: 1.42±0.25 m/s). The fibrotic liver mean SWS was 2.07±0.20 m/s (Original: 2.12±0.18 m/s). Motion correction in sagittal free-breathing abdominal MRE results in improved data quality, inversion precision, repeatability, and is feasible in patients.

The Optimization of LSTM Model by Wavelet Transform and Simulated Annealing Algorithm

The main objective of this paper is to study the integration of data processing methods and intelligent algorithms to optimize the results of LSTM prediction models. In this paper, continuous wavelet transform is used to clean and preprocess time series data and improve data quality. Wavelet reconstruction is used to restore the results. At the same time, the simulated annealing algorithm is introduced as an intelligent algorithm to search globally for the best solution to achieve the optimal prediction result. The application of this comprehensive approach can also improve the quality and precision of data analysis in various fields, such as the parameter estimation of pulse signals in physics. The core challenge of the research is to optimize the data prediction results, and for this purpose, a multi-level method of continuous wavelet transform, deep learning model (LSTM) and simulated annealing algorithm is adopted.

Optimizing Spectronaut Search Parameters to Improve Data Quality with Minimal Proteome Coverage Reductions in DIA Analyses of Heterogeneous Samples.

Data-independent acquisition has seen breakthroughs that enable comprehensive proteome profiling using short gradients. As the proteome coverage continues to increase, the quality of the data generated becomes much more relevant. Using Spectronaut, we show that the default search parameters can be easily optimized to minimize the occurrence of false positives across different samples. Using an immunological infection model system to demonstrate the impact of adjusting search settings, we analyzed Mus musculus macrophages and compared their proteome to macrophages spiked withCandida albicans. This experimental system enabled the identification of "false positives" as Candida albicans peptides and proteins should not be present in the Mus musculus-only samples. We show that adjusting the search parameters reduced "false positive" identifications by 89% at the peptide and protein level, thereby considerably increasing the quality of the data. We also show that these optimized parameters incurred a moderate cost, only reducing the overall number of "true positive" identifications across each biological replicate by <6.7% at both the peptide and protein level. We believe the value of our updated search parameters extends beyond a two-organism analysis and would be of great value to any DIA experiment analyzing heterogeneous populations of cell types or tissues.

Identifying and Overcoming Obstacles to ESG Integration in Indian Companies: An Exploratory Study

This research paper aims to identify and overcome obstacles to ESG (Environmental, Social, and Governance) integration in Indian companies through an exploratory study. The study employs a qualitative research approach. Preliminary results suggest that Indian companies face several obstacles in integrating ESG factors into their decision-making processes, including a lack of awareness or understanding of ESG factors, limited availability of ESG data, weak regulatory frameworks, and cultural or institutional barriers. Potential solutions to these obstacles include increasing awareness through education and training, improving data quality and availability, strengthening regulatory frameworks, and building partnerships between companies and other stakeholders to promote ESG integration. The findings of this study have important implications for Indian companies, investors, regulators, and other stakeholders, by providing insights into the major obstacles to ESG integration in India and identifying strategies to overcome these obstacles

Incorporation of Three Different Optical Trains into the IR-MALDESI Mass Spectrometry Imaging Platform to Characterize Artemisia annua.

Artemisinin is the leading medication for the treatment of malaria and is only produced naturally in Artemisia annua. The localization of artemisinin in both the glandular and non-glandular trichomes of the plant makes it an ideal candidate for mass spectrometry imaging (MSI) as a model system for method development. Infrared matrix-assisted laser desorption electrospray ionization MSI (IR-MALDESI-MSI) has the capability to detect hundreds to thousands of analytes simultaneously, providing abundance information in conjunction with species localization throughout a sample. The development of several new optical trains and their application to the IR-MALDESI-MSI platform has improved data quality in previous proof-of-concept experiments but has not yet been applied to analysis of native biological samples, especially the MSI analysis of plants. This study aimed to develop a workflow and optimize MSI parameters, specifically the laser optical train, for the analysis of Artemisia annua with the NextGen IR-MALDESI platform coupled to an Orbitrap Exploris 240 mass spectrometer. Two laser optics were compared to the conventional set up, of which include a Schwarzschild-like reflective objective and a diffractive optical element (DOE). These optics, respectively, enhance the spatial resolution of imaging experiments or create a square spot shape for top-hat imaging. Ultimately, we incorporated and characterized three different optical trains into our analysis of Artemisia annua to study metabolites in the artemisinin pathway. These improvements in our workflow, resulted in high spatial resolution and improved ion abundance from previous work, which will allow us to address many different questions in plant biology beyond this model system.

Emerging underwater survey technologies: A review and future outlook

Emerging underwater survey technologies are revolutionizing the way we explore and understand the underwater world. This review examines the latest advancements in underwater survey equipment, highlighting their operational benefits and potential areas for future development. Recent developments in underwater survey technologies have led to significant improvements in accuracy, efficiency, and data quality. Advanced sonar systems, such as multibeam and sidescan sonars, provide high-resolution images of the seafloor, allowing for detailed mapping of underwater features. Autonomous underwater vehicles (AUVs) equipped with sophisticated sensors and cameras enable precise data collection in challenging environments, such as deep-sea areas or complex underwater structures. The operational benefits of these technologies are vast. They allow for faster surveying, reduced costs, and improved safety for personnel. Additionally, the high-quality data obtained from these surveys can lead to better decision-making in various industries, including offshore energy, marine research, and underwater archaeology. Looking ahead, the future of underwater survey technologies is promising. There is a growing interest in developing integrated systems that combine multiple sensors and data processing capabilities to provide a more comprehensive view of underwater environments. Artificial intelligence and machine learning algorithms are also being increasingly utilized to analyze large datasets and extract valuable insights. However, several challenges remain, such as the need for better underwater communication systems, improved battery life for autonomous vehicles, and enhanced data processing capabilities. Addressing these challenges will be crucial for the continued advancement of underwater survey technologies. In conclusion, the latest advancements in underwater survey technologies offer exciting opportunities for enhancing our understanding of the underwater world. By leveraging these technologies and addressing key challenges, we can unlock new possibilities for underwater exploration and research.

An ontology for maintenance activities and its application to data quality

Maintenance of assets is a multi-million dollar cost each year for asset intensive organisations in the defence, manufacturing, resource and infrastructure sectors. These costs are tracked though maintenance work order (MWO) records. MWO records contain structured data for dates, costs, and asset identification and unstructured text describing the work required, for example ‘replace leaking pump’. Our focus in this paper is on data quality for maintenance activity terms in MWO records (e.g. replace, repair, adjust and inspect). We present two contributions in this paper. First, we propose a reference ontology for maintenance activity terms. We use natural language processing to identify seven core maintenance activity terms and their synonyms from 800,000 MWOs. We provide elucidations for these seven terms. Second, we demonstrate use of the reference ontology in an application-level ontology using an industrial use case. The end-to-end NLP-ontology pipeline identifies data quality issues with 55% of the MWO records for a centrifugal pump over 8 years. For the 33% of records where a verb was not provided in the unstructured text, the ontology can infer a relevant activity class. The selection of the maintenance activity terms is informed by the ISO 14224 and ISO 15926-4 standards and conforms to ISO/IEC 21838-2 Basic Formal Ontology (BFO). The reference and application ontologies presented here provide an example for how industrial organisations can augment their maintenance work management processes with ontological workflows to improve data quality.

Research on data preprocessing method for artificial intelligence algorithm based on user online behavior

This paper delves into the significance and efficacy of data preprocessing techniques specifically tailored for user online behavior data in enhancing the performance of artificial intelligence algorithms. Through a comprehensive literature review, we identify gaps and opportunities in current methodologies, setting the stage for the development of a novel data preprocessing approach. This proposed method is meticulously designed to handle the complexities and nuances of user online behavior data. We conduct a series of case studies and rigorous empirical analyses to evaluate the effectiveness of our approach. The results clearly demonstrate that our method substantially improves data quality by effectively reducing noise and eliminating irrelevant information, which, in turn, enhances the overall performance of the AI algorithms. The paper concludes with a discussion on the limitations of the current study and provides insightful directions for future research in the field. This includes potential refinements to the preprocessing technique and its application to other types of behavioral data in different AI domains.

A novel battery abnormality diagnosis method using multi-scale normalized coefficient of variation in real-world vehicles

Accurate and efficient diagnosis of battery voltage abnormality is crucial for the safe operation of electric vehicles. This paper proposes an innovative battery voltage abnormality diagnosis method based on a normalized coefficient of variation in real-world electric vehicles. Vehicle and laboratory data are collected and analyzed, with joint preprocessing to improve data quality, and battery voltages are log-transformed to improve the contribution of anomalous voltage fluctuations. The normalized coefficient of variation is proposed to detect the fluctuation inconsistency of cell voltage, and the risk coefficient rule is formulated by Z-score and normalization. Furthermore, the validity and robustness are verified by laboratory and real-world battery faults. The results demonstrate that the optimal slide step and calculation window for real-world under-voltage fault are 10 and 40, and those for laboratory lithium plating and real-world thermal runaway are both 10 and 50, respectively. More importantly, this study introduces a battery abnormality diagnosis strategy based on the vehicle T-box, anticipated to be widely implemented to ensure the safety of real-vehicle operations. This method not only enhances the accuracy and efficiency of detecting electric vehicle battery abnormalities, but also offers a practical solution to prevent battery related faults.

Application of ultrasound to monitor in vivo residual bone movement within transtibial prosthetic sockets

Transtibial prosthetic users do often struggle to achieve an optimal prosthetic fit, leading to residual limb pain and stump-socket instability. Prosthetists face challenges in objectively assessing the impact of prosthetic adjustments on residual limb loading. Understanding the mechanical behaviour of the pseudo-joint formed by the residual bone and prosthesis may facilitate prosthetic adjustments and achieving optimal fit. This study aimed to assess the feasibility of using B-mode ultrasound to monitor in vivo residual bone movement within a transtibial prosthetic socket during different stepping tasks. Five transtibial prosthesis users participated, and ultrasound images were captured using a Samsung HM70A system during five dynamic conditions. Bone movement relative to the socket was quantified by tracking the bone contour using Adobe After-Effect. During the study a methodological adjustment was made to improve data quality, and the first two participants were excluded from analysis. The remaining three participants exhibited consistent range of motion, with a signal to noise ratio ranging from 1.12 to 2.59. Medial–lateral and anterior–posterior absolute range of motion varied between 0.03 to 0.88 cm and 0.14 to 0.87 cm, respectively. This study demonstrated that it is feasible to use B-mode ultrasound to monitor in vivo residual bone movement inside an intact prosthetic socket during stepping tasks.

Momentary Factors and Study Characteristics Associated With Participant Burden and Protocol Adherence: Ecological Momentary Assessment.

Ecological momentary assessment (EMA) has become a popular mobile health study design to understand the lived experiences of dynamic environments. The numerous study design choices available to EMA researchers, however, may quickly increase participant burden and could affect overall adherence, which could limit the usability of the collected data. This study quantifies what study design, participant attributes, and momentary factors may affect self-reported burden and adherence. The EMA from the Phase 1 Family Matters Study (n=150 adult Black, Hmong, Latino or Latina, Native American, Somali, and White caregivers; n=1392 observation days) was examined to understand how participant self-reported survey burden was related to both design and momentary antecedents of adherence. The daily burden was measured by the question "Overall, how difficult was it for you to fill out the surveys today?" on a 5-item Likert scale (0=not at all and 4=extremely). Daily protocol adherence was defined as completing at least 2 signal-contingent surveys, 1 event-contingent survey, and 1 end-of-day survey each. Stress and mood were measured earlier in the day, sociodemographic and psychosocial characteristics were reported using a comprehensive cross-sectional survey, and EMA timestamps for weekends and weekdays were used to parameterize time-series models to evaluate prospective correlates of end-of-day study burden. The burden was low at 1.2 (SD 1.14) indicating "a little" burden on average. Participants with elevated previous 30-day chronic stress levels (mean burden difference: 0.8; P=.04), 1 in 5 more immigrant households (P=.02), and the language primarily spoken in the home (P=.04; 3 in 20 more non-English-speaking households) were found to be population attributes of elevated moderate-high burden. Current and 1-day lagged nonadherence were correlated with elevated 0.39 and 0.36 burdens, respectively (P=.001), and the association decayed by the second day (β=0.08; P=.47). Unit increases in momentary antecedents, including daily depressed mood (P=.002) and across-day change in stress (P=.008), were positively associated with 0.15 and 0.07 higher end-of-day burdens after controlling for current-day adherence. The 8-day EMA implementation appeared to capture momentary sources of stress and depressed mood without substantial burden to a racially or ethnically diverse and immigrant or refugee sample of parents. Attention to sociodemographic attributes (eg, EMA in the primary language of the caregiver) was important for minimizing participant burden and improving data quality. Momentary stress and depressed mood were strong determinants of participant-experienced EMA burden and may affect adherence to mobile health study protocols. There were no strong indicators of EMA design attributes that created a persistent burden for caregivers. EMA stands to be an important observational design to address dynamic public health challenges related to human-environment interactions when the design is carefully tailored to the study population and to study research objectives.

Remaining life prediction of rolling bearings with secondary feature selection and BSBiLSTM

Rolling element bearings are critical components in rotating machinery. To tackle the problem of difficult to accurately characterize the operating state of rolling bearings caused by irrelevance and varying sensitivity of multiple features to performance degradation, and introduction of subjective errors in determination of hyperparameters of deep learning models, which can affect the accuracy and efficiency of remaining useful life (RUL) prediction. To address these challenges, this paper proposed a novel RUL prediction method for rolling bearings with secondary feature selection and Bayesian optimization of self-attention mechanisms for bidirectional long short-term memory (BSBiLSTM). Firstly, multi-domain features are extracted from noise-reduced vibration signals. Then, a three-criterion constraint-based feature selection algorithm is used and a secondary selection algorithm with Pearson correlation coefficient is proposed to improve data quality. Next, the 3σ criterion is integrated to determine the first prediction time for rolling bearings and to divide the degradation stage. Subsequently, the BiLSTM model with Bayesian optimization and self-attention mechanism is proposed to predict the RUL of rolling bearings to further improve the algorithm efficiency. Finally, experimental validation is carried out based on the PRONOSTIA platform dataset and the XJTU-SY rolling bearing dataset, and the results show that the method proposed in this paper is better than many mainstream life prediction methods for rolling bearings at present, and the prediction accuracy is higher.

Improving Eye-Tracking Data Quality: A Framework for Reproducible Evaluation of Detection Algorithms.

High-quality eye-tracking data are crucial in behavioral sciences and medicine. Even with a solid understanding of the literature, selecting the most suitable algorithm for a specific research project poses a challenge. Empowering applied researchers to choose the best-fitting detector for their research needs is the primary contribution of this paper. We developed a framework to systematically assess and compare the effectiveness of 13 state-of-the-art algorithms through a unified application interface. Hence, we more than double the number of algorithms that are currently usable within a single software package and allow researchers to identify the best-suited algorithm for a given scientific setup. Our framework validation on retrospective data underscores its suitability for algorithm selection. Through a detailed and reproducible step-by-step workflow, we hope to contribute towards significantly improved data quality in scientific experiments.

Advances in filarial hydrocelectomy reporting in Uganda and Nigeria

IntroductionSurgery for hydrocele is part of the WHO recommended essential package of care for people affected by Lymphatic filariasis (LF). In Uganda and Nigeria, LF programme support for hydrocele surgery began in 2017. In 2021 the two programmes adopted a newly developed smartphone-based application to monitor hydrocele surgery and outcome. The application, referred to as the Hydrocele Tracker, gathers information on surgical registration, preoperative information, intraoperative information, postoperative information and follow-up evaluations. It was developed to improve supervision, understand surgical outcomes and reasons for complications, improve reporting and improve data quality for Ministries of Health with active LF programmes.MethodsHydrocele patient information was collected between October 2021 and May 2023 in Uganda (Lango sub-region, 9 districts) and March 2021 - September 2022 in Nigeria (Benue, Kaduna, Kebbi, Sokoto and Kogi States). Additionally patient feedback surveys were undertaken to elicit patient experience and satisfaction with the hydrocele surgery.ResultsPatient data from 2,911 records in the Hydrocele Tracker are presented.DiscussionThe discussion highlights the implications of the data for LF programming, the value of digitisation and approaches to address data quality.

Using interRAI Assessment for Research: Developing a National Research Agenda in Aotearoa New Zealand

interRAI provides a suite of standardized, validated instruments used to assess health and psychosocial well-being, and to inform person-centered care planning. Data obtained from these standardized tools can also be used at a population level for research and to inform policy, and interRAI is currently used in more than 40 countries globally. We present a brief overview of the use of interRAI internationally within research and policy settings, and then introduce how interRAI is used within the universal public health system in Aotearoa New Zealand (NZ), including considerations relating to Māori, the Indigenous people of NZ. In NZ, improvement in interRAI data utilization for research purposes was called for from aged care, health providers, and researchers, to better use these data for quality improvement and health advancement for New Zealanders. A national research network has been established, providing a medium for researchers to form relationships and collaborate on interRAI research with a goal of translating routinely collected interRAI data to improve clinical care, patient experience, service development, and quality improvement. In 2023, the network members met (hybrid: in-person and online) and identified research priorities. These were collated and developed into a national interRAI research agenda by the NZ interRAI Research Network Working Group. Research priorities included reviewing the interRAI assessment processes, improving methods for data linkage to national data sets, exploring how Indigenous Data Sovereignty can be upheld, as well as a variety of clinically focused research topics. Implications for Practice, Policy, and Research: This appears to be the first time national interRAI research priorities have been formally identified. Priorities identified have the potential to inform quality and clinical improvement activities and are likely of international relevance. The methodology described to cocreate the research priorities will also be of wider significance for those looking to do so in other countries.

The integration algorithm of digital resources in business administration based on cluster analysis

The field of business management involves a large amount of data and information sources, including market data, customer data, supply chain data, etc. In order to quantify and analyze different resources, help enterprises better plan and allocate resources, and improve resource utilization efficiency, a clustering analysis based digital resource integration algorithm for business management is studied. Build a business management digital resource integration framework, including data layer, integration layer, and storage layer, to integrate and store data from different sources of business management databases, thereby facilitating unified management and utilization of digital resources by enterprises. The data layer collects data from different business management databases and stores it in the database according to different sources; The integration layer preprocesses the collected data, simply fixes errors and missing information in the data, and improves data quality. Adopting a feature extraction method based on the projection direction uncorrelation strategy of the labeled power set conversion method, the useful feature information of digital resources in enterprise management can be effectively extracted; Based on the two-step clustering analysis method, business management digital resources are clustered according to similar characteristics to complete the classification and integration of business management digital resources, and improve the efficiency of resource utilization; The storage layer adopts the Security Information Diffusion Algorithm (IDA) storage model to store integrated and classified digital resources managed by enterprises, ensuring data security and effectively preventing data leakage and illegal access. The experimental results show that the digital resource structure of business management integrated by this algorithm is clear, with a data redundancy of less than 8% and a difference of less than 11%. The time consumption for data integration is less than 2.11 minutes, indicating good resource integration ability.