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        <title>Frontiers in Big Data | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/big-data</link>
        <description>RSS Feed for Frontiers in Big Data | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-11-03T00:40:28.415+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1910002</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1910002</link>
        <title><![CDATA[The limits of privacy enhancement in healthcare federated learning: a ELSA perspective in the EU context]]></title>
        <pubdate>2026-10-29T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Marine Jacquemin</author>
        <description><![CDATA[Federated Learning (FL) usage is steadily increasing in healthcare. Offering an attractive alternative to centralized data processing, FL is often promoted as a privacy-enhancing technology (PET) because it keeps patient-level data local, exchanging only aggregated data or model updates. As such, it is frequently presented as a means of addressing the ethical, legal and societal challenges associated with the use of sensitive health data. This framing has contributed to growing policy interest in FL within initiatives such as the European Health Data Space (EHDS). This paper examines FL from an ethical, legal and societal (ELSA) perspective in the EU context, focusing on its interaction with the European data protection framework and contemporary debates on health data governance. First, it analyzes the interaction between FL architectures and the General Data Protection regulation (GDPR), showing that questions of controllership, joint responsibility, and identifiability persist despite decentralization, particularly in complex multi-institutional settings. Second, it argues that privacy-centric approaches to health data governance remain ethically insufficient because harmful inferences, discrimination, and collective harms may arise even when data remains formally anonymous. The dominant focus on reidentification risk thus reflects a narrow and individualistic conception of privacy that is poorly adapted to the contemporary data landscape of the EU. It fails to capture broader forms of harm, including group-level discrimination, inferential profiling, and structural power asymmetries. The paper concludes that, despite its framing as a PET, the use of FL alone is not sufficient to guarantee appropriate health data protection. Indeed, FL should also be approached as a distributed data-processing paradigm in which risks and benefits depend on implementation choices, institutional arrangements, and regulatory interpretation. Accordingly, FL should be situated within broader questions of data governance, including how access, accountability, and collaboration are structured and distributed across institutions.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1916400</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1916400</link>
        <title><![CDATA[Exploring perceptions of OCR technology adoption for paper document archiving in Saudi Arabia: an exploratory study]]></title>
        <pubdate>2026-10-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Alia Mohammed AlSulaimi</author>
        <description><![CDATA[The ongoing digital transformation agenda introduced under Saudi Vision 2030 has encouraged organizations across the Kingdom to modernize document management practices and reduce dependence on paper-based records. Within this context, Optical Character Recognition (OCR) technology has become an important enabler of document digitisation by supporting the conversion of printed records into searchable digital content. Despite its potential, the implementation of OCR in Saudi organizations continues to face a number of organizational, technical, and linguistic challenges, particularly when processing Arabic-language documents and integrating OCR into existing archival workflows. This study investigated the adoption of OCR technology for document archiving in Saudi Arabian organizations through the theoretical perspective of the Technology Acceptance Model (TAM). A descriptive cross-sectional design was employed using a structured questionnaire that included both closed-ended items and optional open-ended responses. Data were collected from two participant groups comprising domain experts (n = 8) and general technology users (n = 15) representing public and private sector organizations. The findings indicate that participants generally viewed OCR technology positively, particularly with regard to its perceived usefulness. Document retrieval efficiency received unanimous agreement from both participant groups, identifying faster access to archived information as the most widely recognized benefit of OCR adoption. Simultaneously, respondents identified several factors that continue to hinder wider implementation. These included limited access to specialized OCR training and concerns about digitizing fragile or historical paper records. Challenges associated with Arabic script are considered in the manuscript as contextual issues identified in the existing literature rather than as direct empirical findings of the present survey. Participants also reported that although OCR improves document retrieval and day-to-day workflow efficiency, its integration into organizational processes still depends on adequate technical infrastructure, workforce preparedness, and effective implementation planning. The findings offer practical insights that may support future organizational digitisation initiatives aligned with the objectives of Saudi Vision 2030. Overall, the study provides exploratory descriptive evidence on participants' perceptions of OCR adoption, implementation challenges, and perceived organizational impact within Saudi Arabian document-archiving contexts. The study contributes to the limited empirical literature examining the organizational and user-centered dimensions of OCR adoption in Arabic-language archival environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1953628</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1953628</link>
        <title><![CDATA[Indirect tax accountability as record agreement: inventory accounting and auditing under digital technologies and big data]]></title>
        <pubdate>2026-10-28T00:00:00Z</pubdate>
        <category>Conceptual Analysis</category>
        <author>Nigora Alimkhanova</author><author>Rakhmonkul Hamidovich Djalilov</author><author>Salima Rayimovna Egamberdieva</author><author>Jasurbek Anvarovich Ismatullaev</author><author>Otabek Normamatovich Kholmuradov</author><author>Nafosat Oramova</author><author>Oman Khushmuradov</author><author>Lobarkhon Anvarova</author><author>Asqarjon Asrorovich Raimberdiev</author>
        <description><![CDATA[An industrial enterprise keeps three accounts of the same goods. Operations hold a physical record of what moved, the enterprise system holds a transactional record of what was documented and priced, and the tax function holds a fiscal record of what was declared. Indirect taxation works to the extent that the three records agree. The standard evasion schemes in manufacturing, namely unrecorded output, invented purchasing, disguised disposal and elastic valuation, all drive the records apart while leaving each one internally plausible. Serialized marking, sensor networks, enterprise event logs and clearance-model electronic invoicing have changed what can be tested, because the three records now exist at population scale and in machine-readable form. Drawing on tax compliance economics, big data research in auditing, continuous assurance and organizational capability theory, this article reconstructs indirect tax accountability as a property of agreement among the three records rather than of any one of them. It first states the conditions under which agreement is evidence at all. A reconciliation carries weight only where its two sides descend from causally separate processes, so the records must contain measurement anchors generated outside the enterprise transaction-processing layer. Integrated systems violate that condition by default. Agreement is then formalized as a set of reconciliation residuals, and a detection condition is derived from stated assumptions. A scheme survives only while its divergence stays inside the noise floor of the tightest independent check that sees it, so the sustainable tax gap is the sum of those noise floors rather than a function of the number of controls in place. A sensitivity analysis shows how the bound moves when the assumptions of separability, exogenous tolerances and zero detection risk are relaxed. Four leakage channels are traced through the inventory cycle, matched to the reconciliation that exposes each, and condensed into four propositions with refuting observations and candidate measures. Uzbekistan is the instantiating setting, where clearance invoicing, serialized labeling and, from January 2026, automated invoice risk scoring have outrun enterprise capability, and where documented administrative reconciliation practice between 2021 and 2023 supplies a live episode and its governance failure mode.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1972023</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1972023</link>
        <title><![CDATA[From benchmark to user: an end-to-end evaluation audit of an AI-based oral health screening prototype]]></title>
        <pubdate>2026-10-28T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Ivan Cheng Jun Char</author><author>Abdullah Sayed Ahmed Sayed</author><author>Kazuki Ichikawa</author><author>Youssif Mahmoud Gomaa Sayed</author><author>Abdurrahman Bin Milhan</author><author>Doreen Ying Ying Sim</author><author>Anand Ramanathan</author>
        <description><![CDATA[BackgroundArtificial intelligence (AI) for oral screening is usually judged by aggregate detection metrics on public benchmarks whose partitions, labels, and relevance to users are rarely checked.MethodsWe audited a prototype combining a YOLOv8m detector for six conditions (1,500 test images), a DenseNet169 histopathology network, and a ResNet18 router. Under a pre-specified plan, we measured duplication across partitions, tested removal of duplicated test images against 10,000 matched controls, compared duplicated copies' labels and predictions, compared the library versions, and ran every test image through the application pipeline.ResultsNear-duplicates of training images accounted for 17.3% of test and 17.1% of validation images (lower bounds). Removing duplicated test images changed mean average precision (mAP)@0.5 by +0.001 (95% confidence interval [CI] −0.012 to +0.014). Only 22% of duplicated photographs had identical labels on both copies; exploratory analysis suggested memorization of training labels in orientations seen in training. A library update moved mAP@0.5:0.95 by −0.012 with identical predictions. The router rejected 23% of in-domain photographs and 51% of photographs with ulcers, so ulcer sensitivity per image was 0.36 despite the highest benchmark recall (0.91). A class-mapping defect, still live in a public demonstration in September 2026, gave the wrong name to 73% of findings. The histopathology classifier classified every image as positive; its label was not a diagnosis.ConclusionDuplication compromised the benchmark's independence without detectably changing aggregate mAP; the defects that changed what was reported or displayed lay in the labels, evaluation software, and pipeline. Evaluations should verify partitions and labels, pin the library, and test deployed systems end to end.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1949829</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1949829</link>
        <title><![CDATA[The Cognitive City Cybersecurity Readiness Index (C3RI): an evidence-based model for AI-driven urban infrastructure]]></title>
        <pubdate>2026-10-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Reem Alshareef</author><author>Mahmood Niazi</author><author>Mohammad Alshayeb</author><author>Sajjad Mahmood</author>
        <description><![CDATA[IntroductionCognitive cities integrate autonomous decision-making, machine learning, and Internet of Things networks to create adaptive urban environments. These technologies introduce cybersecurity challenges that existing enterprise and smart city frameworks do not adequately address. This study develops an evidence-based readiness model to assess and advance cybersecurity preparedness in cognitive cities.MethodsA three-phase methodology develops the model. Phase 1 elicits cybersecurity best practices through a focused review mapped to documented cognitive city security challenges, Phase 2 organizes the practices into knowledge areas and readiness levels, and Phase 3 evaluates the result through a panel of ten cybersecurity experts and an initial application case study in a large-scale real-world cognitive city.ResultsThe resulting Cognitive City Cybersecurity Readiness Index (C3RI) comprises 117 best practices organized into eight Cybersecurity Knowledge Areas and distributed across five progressive readiness levels (Foundational, Structured, Intelligent, Autonomous, and Adaptive). A Motorola-based assessment scores each practice across Approach, Deployment, and Results, and a 7.0 advancement gate governs progression between levels. In the case study, level means range from 7.04 to 7.52 and exceed the gate at every readiness level. The expert review supports the clarity, coverage, and practical applicability of the model.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.2014647</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.2014647</link>
        <title><![CDATA[Correction: Machine-assisted adaptive feedback consensus method for large-scale group decision making based on reinforcement learning]]></title>
        <pubdate>2026-10-26T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Hongyu Yu</author><author>Xuanhua Xu</author><author>Weiwei Zhang</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1942887</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1942887</link>
        <title><![CDATA[How transport coverage shapes access to opportunities: a national-scale isochrone analysis of bus network accessibility in England]]></title>
        <pubdate>2026-10-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sam Townley</author><author>Federico Botta</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1933830</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1933830</link>
        <title><![CDATA[A retrieval-augmented multi-stage prompting framework for legal analysis in the Indian judicial system]]></title>
        <pubdate>2026-10-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kanishkavardhan P.</author><author>Gowtham S.</author><author>Maheswari N.</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1977331</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1977331</link>
        <title><![CDATA[Managing quality-oriented big data: a conceptual architecture for acquisition, fusion, and analytics in data-driven organizations]]></title>
        <pubdate>2026-10-16T00:00:00Z</pubdate>
        <category>Conceptual Analysis</category>
        <author>Shahlokhon Gafurova</author><author>Shukhrat Rahmatov</author><author>Umida Zakhidova</author><author>Dilnoza Rakhimova</author><author>Gulshan Kabilova</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1911540</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1911540</link>
        <title><![CDATA[Reliability-aware ensemble recommendation for open-network commerce: a dual-metric framework for jointly evaluating ranking accuracy and serving correctness]]></title>
        <pubdate>2026-10-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rish Praveen S</author><author>Vani Rajasekar</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1866430</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1866430</link>
        <title><![CDATA[DermArtifactDB: a multi-label dataset for artifact annotation in public dermoscopic image repositories]]></title>
        <pubdate>2026-10-08T00:00:00Z</pubdate>
        <category>Data Report</category>
        <author>Vanesa Gómez-Martínez</author><author>David Chushig-Muzo</author><author>Isabel Polimón-Olabarrieta</author><author>Cristina Soguero-Ruiz</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1995917</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1995917</link>
        <title><![CDATA[Editorial: Responsible and robust evaluation for real-world recommendation and search systems]]></title>
        <pubdate>2026-10-05T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Alejandro Bellogín</author><author>Pablo Sánchez</author><author>Laura Sebastiá</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1948398</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1948398</link>
        <title><![CDATA[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]]></title>
        <pubdate>2026-10-05T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Khurram Shahzad</author><author>Asfa Muhammed Din Javeed</author><author>Shakil Ahmad</author><author>Mujahid Latif</author><author>Kashaf Saleem</author>
        <description><![CDATA[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.Systematic review registration:https://osf.io/wjrm6.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1938137</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1938137</link>
        <title><![CDATA[Controlled knowledge updating in memory-enabled AI agents: beyond model editing and recall]]></title>
        <pubdate>2026-09-28T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Gabriel Chavira Juárez</author><author>Eder Jahir Gonzalez Bravo</author><author>Guadalupe Esmeralda Rivera García</author><author>Javier A. Arcos Espinosa</author><author>Salvador W. Nava-Díaz</author>
        <description><![CDATA[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?]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1921037</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1921037</link>
        <title><![CDATA[Machine-assisted adaptive feedback consensus method for large-scale group decision making based on reinforcement learning]]></title>
        <pubdate>2026-09-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hongyu Yu</author><author>Xuanhua Xu</author><author>Weiwei Zhang</author>
        <description><![CDATA[IntroductionTraditional 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.MethodsFirst, 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.ResultsThe 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.Discussion/ConclusionThese 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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1931695</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1931695</link>
        <title><![CDATA[Anticipating user desires: predicting software product feature demand through consumer behavioral analytics]]></title>
        <pubdate>2026-09-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Abdullah A. Aldaeej</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1942458</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1942458</link>
        <title><![CDATA[Evaluating the reach and equity of public spaces in Mexico City using mobility data: the case of the Utopías in Iztapalapa]]></title>
        <pubdate>2026-09-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ollin D. Langle-Chimal</author><author>Natalia Cadavid-Aguilar</author><author>Alberto Meouchi-Vélez</author><author>Roberto Ponce-López</author>
        <description><![CDATA[Cities invest in social infrastructure to improve well-being, but the benefits depend on who can use these spaces and who actually does. Access is therefore not only a matter of whether people live close enough to reach a facility, but also of who travels to it and from how far. We examine this realized access using high-resolution human mobility data from Location-Based Services (LBS) for the Utopías, a network of thirteen community centers offering free recreation, culture, and care in Iztapalapa, an underserved borough of 1.8 million residents in eastern Mexico City. We combine six months of mobility traces with public transit data (GTFS), a georeferenced crime registry, and a catalog of facility activities to identify the factors associated with each site's visitors. Three factors capture distinct dimensions of reach. Transit connectivity tracks the number of visitors a site draws. Facilities with more distinctive activities, such as a planetarium or a dinosaur park, show a greater increase in visitor travel distance at weekends. By contrast, a higher share of violent crime in the surrounding area is associated with more localized and lower-income visitor catchments. Together, these findings show that the reach of social infrastructure varies not only with proximity, but also with transport, the distinctiveness of what facilities offer, and local safety.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1923373</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1923373</link>
        <title><![CDATA[Humans as infrastructure: is AI-initiated steganography in social media possible?]]></title>
        <pubdate>2026-09-18T00:00:00Z</pubdate>
        <category>Opinion</category>
        <author>Daniele Ortu</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1776172</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1776172</link>
        <title><![CDATA[From crisis to new routine: shifts in urban shopping mobility and socioeconomic inequality during and after the COVID-19 shock]]></title>
        <pubdate>2026-09-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yilun Xu</author><author>Mohsen Bahrami</author><author>Alex Pentland</author>
        <description><![CDATA[IntroductionThis study examines the impact of the COVID-19 pandemic on revealed shopping-location patterns and their persistence during the early recovery period. It focuses on urban shopping mobility in New York City and investigates whether observed store-selection patterns returned to, or remained different from, their pre-pandemic baseline by 2021 across different socioeconomic communities.MethodsWe use large-scale mobility and place datasets together with census information to analyze visits to general merchandise and department stores in New York City. A modified Huff gravity model and a metaheuristic calibration method are used to quantify temporal shifts in store-selection patterns. We also use unsupervised learning techniques and statistical inference models to examine heterogeneity across socioeconomic communities and evaluate changes before, during, and after the COVID-19 shock.ResultsThe proposed model captures the dynamics of shopping-location decisions and temporal visit patterns under changing pandemic-era constraints. The results suggest that New Yorkers' revealed store-selection patterns changed substantially during the COVID-19 shock, with increased emphasis on store area, chain loyalty, and nearby points of interest, and reduced sensitivity to customer-store distance. These patterns did not fully revert to their 2019 baseline by 2021, although 2021 is interpreted as an early recovery and reopening period rather than a fully post-pandemic equilibrium.DiscussionThe findings highlight the societal dimension of urban shopping mobility disruptions during crises and show that mobility-based store-selection patterns vary across socioeconomic communities. The results can help urban planners, managers, and marketers better understand heterogeneous shifts in shopping mobility and adapt strategies under crisis and recovery conditions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1763520</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1763520</link>
        <title><![CDATA[An empirical test of antecedents and perceived performance of big data analytics in telecommunication companies]]></title>
        <pubdate>2026-09-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ibrahim Magboul</author><author>Diaeldin Osman</author><author>Fadi Herzallah</author><author>Alnour Nadir Osman</author><author>Dexter Gittens</author>
        <description><![CDATA[The emergence of Data concepts, sources, types, and analysis mechanisms has changed dramatically in recent years. Nowadays, big data analytics (BDA) dominates the attention of scholars and practitioners. Since this emergence, different business sectors have started deploying BDA in various endeavors to help improve decision-making and gain a competitive advantage. Recently, some organizations reported that BDA has not yielded the expected business value. In addition, researchers have focused so far on BDA usage in developed countries. Antecedents of BDA adoption have been widely researched; nevertheless, this study is among the first to collate selected antecedents and perceived performance of BDA adoption in one integrated and validated model in the telecommunication sector. Using a purposive sampling technique, thestudy deployed structural equation modeling (SEM) to analyze 201 observations. The findings reveal that six of the eight antecedents of BDA significantly impact BDA, while two showed no significant effect. Furthermore, this research finds that BDA has a positive impact on perceived performance, thus contributing to the technology adoption literature by offering valuable insights for researchers and practitioners in a developing country like Sudan.]]></description>
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