Frontiers in Big Data最新文献

{"title":"How transport coverage shapes access to opportunities: a national-scale isochrone analysis of bus network accessibility in England.","authors":"Sam Townley, Federico Botta","doi":"10.3389/fdata.2026.1942887","DOIUrl":"10.3389/fdata.2026.1942887","url":null,"abstract":"<p><p>Equitable access to opportunities is a cornerstone of social inclusion, and public transport plays a central role in delivering it for the millions of households without access to a private car. Yet most accessibility research has focused on case studies of individual cities, while transport funding decisions in the UK are typically made at regional or national scales. This paper evaluates the infrastructure-based accessibility of bus networks in England, together with their connectivity to key destinations such as places of employment, healthcare and education. We propose a scalable, isochrone-based methodology that compares the area reachable by bus to the area reachable by car within a fixed travel-time budget, and combines it with a destination-count ratio that quantifies the share of destinations reachable by bus relative to those reachable by car. Applied across all 33,755 Lower-layer Super Output Areas (LSOAs) in England, the analysis reveals deep and spatially structured inequalities in mobility-mediated access to opportunities. We show that 61.4% of LSOAs, home to 60.9% of the resident population, reach less than 1% of the equivalent car-accessible area by bus, and that 71% of LSOAs (70.6% of the resident population) reach fewer than 1 in 20 of the destinations a car user could reach. Spatial autocorrelation analysis reveals widespread clustering of low accessibility outside major cities, while urban areas show finer-grained, heterogeneous patterns of disadvantage. The findings highlight rural-urban inequalities and within-city heterogeneity, and point to areas where targeted bus-network investment could yield significant gains in social inclusion. The methodology relies only on openly available timetable and street-network data and is therefore transferable to other countries and applicable at finer geographical scales.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1942887"},"PeriodicalIF":3.8,"publicationDate":"2026-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13735104/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149344559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A retrieval-augmented multi-stage prompting framework for legal analysis in the Indian judicial system.","authors":"Kanishkavardhan P, Gowtham S, Maheswari N","doi":"10.3389/fdata.2026.1933830","DOIUrl":"https://doi.org/10.3389/fdata.2026.1933830","url":null,"abstract":"<p><p>The aim of this endeavor was to create an Artificial Intelligence (AI) based legal researcher that utilizes a multi-stage Retrieval-Augmented Generation (RAG) methodology to solve the challenges of finding and comprehending pertinent legal information in the documents. The traditional methods for finding legal documents will usually yield very few results that are useful to someone without a law degree. This new system uses semantic search, keyword retrieval and Large Language Model (LLM) reasoning methods to improve the legal research process. The submitted query will be transformed into a vector embedding while simultaneously being classified into the appropriate category for retrieval. Once the query is classified, the system will use a hybrid retrieval mechanism to find the most relevant legal document based on both BM25 (Best Matching 25) and FAISS (Facebook AI Similarity Search) search algorithms and will combine these results using Reciprocal Rank Fusion (RRF) in order to determine the best results. Later, it will be processed through LLM models to produce formatted (structured) answers to the query in accordance with the relevant legal citation. The overall performance of the proposed system demonstrates improved answer relevancy, reliability and contextual accuracy, thereby making it an effective tool for legal research and assistance. LLaMA-3.3-70B achieved the highest scores in Answer Relevancy (0.91) and Faithfulness (0.92), proving its ability to generate accurate, intent aligned, and evidence-grounded legal responses with minimal hallucination. Gemini-2.0-Flash showed higher Context Recall (0.78), indicating strong retrieval capability, while Mistral-Large excelled in BLEU (0.769) and semantic similarity, highlighting fluent text generation but weaker evidence grounding.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1933830"},"PeriodicalIF":3.8,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13724461/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149327420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Managing quality-oriented big data: a conceptual architecture for acquisition, fusion, and analytics in data-driven organizations.","authors":"Shahlokhon Gafurova, Shukhrat Rahmatov, Umida Zakhidova, Dilnoza Rakhimova, Gulshan Kabilova","doi":"10.3389/fdata.2026.1977331","DOIUrl":"10.3389/fdata.2026.1977331","url":null,"abstract":"<p><p>Data-driven organizations increasingly depend on large, heterogeneous, and fast-moving data to sustain the quality of their products, services, and processes. The transition toward data-driven organizational quality, often discussed under the label of Quality 4.0, has so far been examined mainly through a managerial lens. As a result, the data foundations of that transition remain fragmented and under-theorized. This Conceptual Analysis reframes quality-oriented big data as a first-class data management problem and proposes a layered conceptual architecture that organizes the lifecycle of such data across five interconnected layers: multi-source acquisition, integration and fusion, data quality assurance, analytics and knowledge extraction, and data governance and ethics. The central argument is that data quality operates as a central mediating construct linking big data capability to organizational quality outcomes: analytics improves organizational quality in large part to the extent that the underlying data are accurate, complete, consistent, timely, and well governed. Drawing on the data management, data quality, and machine learning systems literatures, the article specifies the constituent elements of each layer, the dependencies among them, and the failure modes that arise when the data foundation is neglected. Five propositions and a structured research agenda position the acquisition, fusion, quality assurance, and governance of quality-oriented big data as priorities for data mining and management research. The architecture offers a data-centric vocabulary for designing and evaluating the data infrastructures on which data-driven organizational quality ultimately depends.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1977331"},"PeriodicalIF":3.8,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13719298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149302037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliability-aware ensemble recommendation for open-network commerce: a dual-metric framework for jointly evaluating ranking accuracy and serving correctness.","authors":"Rish Praveen S, Vani Rajasekar","doi":"10.3389/fdata.2026.1911540","DOIUrl":"https://doi.org/10.3389/fdata.2026.1911540","url":null,"abstract":"<p><p>Recommendation quality in open-network commerce is conventionally measured through offline ranking metrics that implicitly assume reliable upstream retrieval, stable item identities, and consistent catalog availability. In open networks such as India's Open Network for Digital Commerce (ONDC), these assumptions are routinely violated: catalogs are contributed by heterogeneous, independently operated providers, transactions follow asynchronous Beckn-style protocol lifecycles, and identifiers drift across service boundaries. This paper presents LocalMarket, a reliability-aware recommendation platform (Next.js frontend, FastAPI backend, ensemble ranking layer) that implements ONDC-pattern transaction flows in a controlled, repository-reproducible environment. We propose and validate a dual-metric evaluation framework that decouples model effectiveness-measured through Mean Average Precision (MAP@10), Recall@10 and Normalized Discounted Cumulative Gain (NDCG@10)-from serving correctness, measured through a novel Route Resolution Success Rate (RRSR) and Wishlist Consistency Rate (WCR). Using a controlled within-system ablation across Model-only, Reliability-only, and Combined configurations on fixed traffic traces, we utilize a convex-combination ensemble containing neural collaborative filtering (NCF) (utilizing only GMF embeddings for retrieval latency), graph-based, session-aware, and content-based components primarily as a vehicle for testing this framework. While this ensemble configuration improves NDCG@10 by 70.7% over a Popularity-based baseline, our primary finding validates that four targeted reliability interventions raise RRSR from 60.0 to 99.8% and WCR from 0.0 to 100.0%. Critically, the combined configuration preserves both gains simultaneously, indicating that model-level and serving-level improvements are additive rather than substitutive. The results support a systems-level thesis: in open-network commerce recommenders, ranking accuracy and serving correctness are independent, jointly necessary axes of quality, and reporting only one materially overstates the value delivered to end users. We further contribute a reusable integration failure-mode taxonomy, a constrained ensemble-weight selection protocol, and a reproducible experiment-replay procedure intended to generalize to other open-network commerce deployments.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1911540"},"PeriodicalIF":3.8,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13686825/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149276932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DermArtifactDB: a multi-label dataset for artifact annotation in public dermoscopic image repositories.","authors":"Vanesa Gómez-Martínez, David Chushig-Muzo, Isabel Polimón-Olabarrieta, Cristina Soguero-Ruiz","doi":"10.3389/fdata.2026.1866430","DOIUrl":"https://doi.org/10.3389/fdata.2026.1866430","url":null,"abstract":"","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1866430"},"PeriodicalIF":3.8,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13686860/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149277000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Responsible and robust evaluation for real-world recommendation and search systems.","authors":"Alejandro Bellogín, Pablo Sánchez, Laura Sebastiá","doi":"10.3389/fdata.2026.1995917","DOIUrl":"https://doi.org/10.3389/fdata.2026.1995917","url":null,"abstract":"","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1995917"},"PeriodicalIF":3.8,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677343/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Big data analytics and sentiment analysis for health misinformation detection: a systematic literature review of influencing factors, implementation challenges and a data-driven adoption framework.","authors":"Khurram Shahzad, Asfa Muhammed Din Javeed, Shakil Ahmad, Mujahid Latif, Kashaf Saleem","doi":"10.3389/fdata.2026.1948398","DOIUrl":"10.3389/fdata.2026.1948398","url":null,"abstract":"<p><p>The study aimed to identify the factors influencing big data analytics (BDA) and sentiment analysis (SA) for health misinformation detection and associated challenges for the adoption of BDA and SA across health ecosystems. The systematic literature review (SLR) methodology was applied for addressing the study's objectives. Thirty most relevant seminal studies (peer-reviewed articles and conference proceedings) were retrieved from 11 digital platforms to conduct the SLR. Results of the study showed that advanced deep learning architectures, feature engineering, sentiment intelligence, real-time analytics, and human-centered verification were key factors that influenced big data analytics and sentiment analysis based health misinformation detection. It was also identified that annotation limitations, scalability constraints, domain adaptation challenges, ethical concerns, and sustainability challenges negatively impacted the adoption of big data and sentiment driven health misinformation detection systems. The study has developed a data-driven framework to effectively adopt and sustain big data analytics and sentiment analysis to detect health misinformation. <b>Systematic review registration:</b> https://osf.io/wjrm6.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1948398"},"PeriodicalIF":3.8,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677458/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Controlled knowledge updating in memory-enabled AI agents: beyond model editing and recall.","authors":"Gabriel Chavira Juárez, Eder Jahir Gonzalez Bravo, Guadalupe Esmeralda Rivera García, Javier A Arcos Espinosa, Salvador W Nava-Díaz","doi":"10.3389/fdata.2026.1938137","DOIUrl":"10.3389/fdata.2026.1938137","url":null,"abstract":"<p><p>Large language model agents that persist across sessions, tools, users, and changing environments do more than answer isolated prompts; they accumulate state. When new evidence arrives, the central question is where that change should live: transient context, external memory, tool or workflow definitions, activation states, or model parameters. Recent benchmarks compare update mechanisms and assess memory over time, while emerging architectures coordinate multiple memory types. In this Perspective, we examine how a decision layer should select among update substrates and evaluate whether its choices remain appropriate. A controlled update is not merely a successful edit or a recalled fact; it is a decision to alter the least invasive substrate sufficient for the claim's scope, expected persistence, and evidentiary strength, including when the correct action is not to write persistent state. We propose a substrate-aware view of controlled knowledge updating organized by three principles: substrate proportionality, temporal defeasibility, and auditable continuity. This framing treats knowledge updating as a longitudinal control problem and motivates evaluation criteria that include update selection, temporal consistency, interference, reversibility, efficiency, and robustness. The resulting agenda connects memory, knowledge editing, agent evolution, and benchmarking under a single practical question: how should an agent decide what to change so that future behavior improves without accumulating uncontrolled state?</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1938137"},"PeriodicalIF":3.8,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13654771/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149166599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine-assisted adaptive feedback consensus method for large-scale group decision making based on reinforcement learning.","authors":"Hongyu Yu, Xuanhua Xu, Weiwei Zhang","doi":"10.3389/fdata.2026.1921037","DOIUrl":"10.3389/fdata.2026.1921037","url":null,"abstract":"<p><strong>Introduction: </strong>Traditional consensus-reaching processes in large-scale group decision making (LSGDM) rely heavily on empirically determined feedback parameters and have difficulty balancing consensus efficiency with the preservation of expert opinions. To address this problem, this paper proposes a reinforcement learning-based machine-assisted adaptive feedback consensus method.</p><p><strong>Methods: </strong>First, a hesitation degree is introduced to improve the score function of hesitant fuzzy linguistic term sets (HFLTSs), thereby reducing information loss during linguistic information transformation. Second, a comprehensive relationship matrix integrating preference similarity and social trust relationships is constructed, and K-Means clustering is employed to divide large-scale expert groups. On this basis, the consensus-reaching process is modeled as a Markov decision process (MDP), and the LSGDM consensus-reaching process is modeled as a dynamic sequential decision problem. A deep deterministic policy gradient (DDPG) agent is utilized to learn feedback adjustment strategies, enabling feedback parameters to be dynamically adjusted according to the evolution state of group opinions. Meanwhile, network text data and the TF-IDF method are combined to determine attribute weights and improve decision objectivity. Finally, an emergency decision-making case of the \"Beijing-Tianjin-Hebei rainstorm\" is conducted to verify the proposed method.</p><p><strong>Results: </strong>The results show that the proposed method can achieve the preset consensus threshold within fewer discussion rounds while effectively balancing the group consensus level and expert opinion retention.</p><p><strong>Discussion/conclusion: </strong>These findings verify the effectiveness and feasibility of the proposed method, indicating that it can provide effective decision support for adaptive feedback consensus reaching in LSGDM emergency decision-making.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1921037"},"PeriodicalIF":3.8,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13645702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149139089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anticipating user desires: predicting software product feature demand through consumer behavioral analytics.","authors":"Abdullah A Aldaeej","doi":"10.3389/fdata.2026.1931695","DOIUrl":"10.3389/fdata.2026.1931695","url":null,"abstract":"<p><p>In this study, we introduce the Dynamic Behavioral Feature Predictor (DBFP), a machine learning model designed to predict feature demands for software products based on consumer behavioral analytics. The model leverages advanced techniques such as Deep Convolutional Neural Networks (1D CNN) and Long Short-Term Memory (LSTM) networks to identify complex user behavior patterns. We demonstrate that DBFP achieves a high accuracy of 99%, outperforming established models such as LSTM and 1D CNN in extensive comparative experiments. Although the model shows promising performance, we note that its accuracy may be sensitive to data quality, and further research is needed to evaluate its robustness in real-world applications. DBFP excels at identifying diverse user behavior types, offering tailored recommendations for software features that align with individual user preferences. This paper highlights the potential of behavioral analytics in personalizing software development, enhancing the user experience, and improving the efficiency of feature prioritization. By bridging the fields of machine learning, big data (BD), and software personalization, DBFP lays the foundation for future advancements in user-centric software engineering. This study not only contributes to the field of predictive analytics but also opens new avenues for applying behavioral insights to software design and development.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1931695"},"PeriodicalIF":3.8,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13645530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149139086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}