data analytics

Business process optimization remains challenging due to fragmented methodologies across process mining, predictive process monitoring, and process-aware recommendation—each operating in isolation without a unified theoretical foundation or integration framework. Method: This paper proposes a closed-loop optimization framework that systematically integrates Alpha algorithm/Inductive Miner for process discovery, LSTM/Transformer for runtime prediction, collaborative filtering/graph neural networks for action recommendation, and explainable AI (XAI) for interpretability—enabling automated bottleneck identification, anomaly forecasting, and prescriptive optimization from event logs. Contribution/Results: We establish the first unified conceptual boundary, evolutionary taxonomy, and synergy paradigm across the three domains; construct a comprehensive classification schema covering 120+ studies; clarify application scopes and standardized evaluation benchmarks; and deliver an industrially actionable methodology selection guide with validated deployment pathways.

Despite growing AI adoption in HR digital transformation, a coherent theoretical framework and empirical synthesis for systematically applying AI to talent decision-making remain lacking. Method: We propose the first AI technology taxonomy tailored to HR contexts—the “Talent Management–Organizational Management–Labor Market” tri-dimensional framework—clarifying data-task-model mappings. Integrating machine learning, natural language processing, graph neural networks, and causal inference, we align methods with core HR tasks including resume parsing, performance prediction, and attrition forecasting. We further identify interpretability, fairness, and real-time processing as critical technical challenges. Contribution/Results: Based on a systematic review of 300+ scholarly articles, we construct the most comprehensive AI-for-talent-analytics research map to date. This work establishes a methodological foundation for academia and delivers an actionable, implementation-oriented roadmap for intelligent HR systems in industry.

This study addresses the challenge of extracting business-interpretable item association rules from retail transaction data to support precision marketing, shelf-space optimization, and inventory management. To bridge the gap between statistical discoverability and operational actionability, we propose a novel rule filtering and prioritization framework that jointly considers statistical significance (via support, confidence, and lift) and managerial feasibility (through domain-specific semantic mapping). Our method integrates Apriori and FP-Growth algorithms, incorporates a three-dimensional rule evaluation scheme, and enables interactive rule visualization. Evaluated on a real-world supermarket dataset, the framework identified 327 high-value, actionable association rules. Deployment yielded an 18.6% increase in cross-buying rate and a 22.3% improvement in promotional response rate, empirically validating its practical effectiveness and scalability for retail analytics.

Business professionals—non-technical domain experts—lack appropriate tools and methodologies for effective what-if analysis (WIA), hindering data-informed decision-making. Method: We conducted a two-phase mixed-methods user study—comprising contextual interviews and in-situ task-based evaluations—to systematically characterize their analytical behaviors for the first time. Contribution/Results: Based on empirical findings, we propose three domain-grounded design principles: business-contextual data preparation, risk-aware assessment, and domain-knowledge integration. We implemented and validated these principles in an interactive visual analytics prototype. The study identifies three critical support gaps, empirically confirms that six classes of what-if techniques significantly improve decision efficiency and confidence, and yields eight actionable design guidelines for commercial business intelligence systems. This work bridges a key theoretical and practical gap in WIA research concerning non-technical users.

To address the challenge of detecting Advanced Persistent Threats (APTs) that evade traditional rule-based engines, this paper proposes a lightweight, interpretable predictive analytics framework integrating logistic regression and K-means clustering. Designed for low-resource settings with small-scale security event data (Kaggle dataset, *n* = 2,000), it enables real-time threat detection and response. Methodologically, it is the first to synergistically combine these two models in resource-constrained environments and employs SPSS-based statistical tests to validate feature significance. Compared to baseline rule engines, the framework achieves significantly improved threat alert sensitivity (+23.6%) and reduces average response time by 41%, while preserving high model interpretability. It thus delivers actionable, proactive defense decision support for Security Operations Centers (SOCs).

Financial cluster analysis faces three key challenges: difficulty in modeling dynamic temporal dependencies, high ambiguity in heterogeneous business knowledge sources, and poor interpretability due to exhaustive pairwise comparisons. To address these in the context of thematic stock investment, this paper proposes a three-stage collaborative clustering paradigm—“dynamic generation, knowledge exploration, and correlation validation.” It integrates time-series similarity metrics with domain-specific knowledge graph embeddings to construct a multi-view interactive clustering framework that jointly quantifies performance and qualifies semantic relationships. The framework incorporates heatmap-, relational-graph-, and trajectory-based visualizations and supports user-driven iterative refinement. Experiments demonstrate significant improvements in clustering validity and interpretability; domain experts highly endorse its effectiveness in thematic stock construction, risk-hedging portfolio identification, and emerging investment theme discovery.

This study addresses the high learning barrier and conceptual abstraction inherent in AI–big data interdisciplinary education. Methodologically, it proposes a pedagogical paradigm integrating *conceptual simplification*, *intuitive visualization*, and *full-stack technical integration*. It systematically unifies core deep learning architectures (CNNs, ResNet, YOLO, Transformers), pre-trained models (BERT, GPT), and big data technologies (SQL/NoSQL, Hadoop, Spark), delivering knowledge coherence through principled explanations, dynamic visualizations, and cross-modal case studies (NLP, image recognition, autonomous driving). Its key contribution is the first unified teaching framework spanning neural network fundamentals, transfer learning with pre-trained models, and big data–enabled AI deployment. Empirical evaluation demonstrates that the paradigm significantly accelerates beginner onboarding, improves downstream task accuracy by 15–30%, and reduces AI application development cycles by over 40%.

Contemporary BI dashboards lack a structured, iterative optimization framework, hindering their evolution from exploratory tools to robust decision-support systems. Method: This study proposes a feedback-driven, gap-analysis–informed four-stage iterative methodology, integrating a six-element data narrative framework—encompassing goals, context, insights, evidence, actions, and impact—and implements it in Power BI via DAX metric optimization and collaborative peer review. Contribution/Results: The framework demonstrably enhances narrative coherence and explanatory power. Empirical application uncovered critical issues: significantly lower gross margin for furniture (6.94% vs. 13.99% for technology), profitability erosion beyond a 20% discount threshold, and $1.35M in unrecovered freight costs—substantially improving decision accuracy. This work makes the first contribution of embedding structured narrative design directly into the BI dashboard iteration lifecycle, yielding a reusable, methodologically grounded framework.

Process mining has increasingly emphasized technical dimensions while neglecting human and organizational factors, leading to a growing disconnect between analytical insights and practical implementation. Method: Grounded in a sociotechnical perspective, this paper proposes “process analytics” as a novel paradigm, developing a multidimensional framework that integrates analytical processes, organizational context, and stakeholder engagement. Through an inductive–deductive conceptual modeling approach, the framework is theoretically validated and contextualized using real-world enterprise cases. Contribution/Results: This work provides the first explicit, structured definition of process analytics, overcoming traditional process mining’s algorithmic bias and governance neglect. It emphasizes the co-evolution of analytical activities and organizational practices. The resulting scalable framework has been empirically validated in large-scale enterprise process automation initiatives, demonstrating both theoretical rigor and practical applicability.

Retail demand data are often plagued by strong seasonality, irregular spikes, and noise, which undermine the accuracy of traditional forecasting methods and hinder effective supply chain decision-making. To address this challenge, this work proposes an end-to-end three-stage framework: it begins with exploratory data analysis, followed by a systematic evaluation of deep time series models—specifically N-BEATS and N-HiTS—to identify the best-performing predictor. The superior forecast from N-BEATS is then integrated into an integer linear programming (ILP) model that generates feasible delivery plans minimizing total distribution time under constraints on budget, capacity, and service level. By combining high-accuracy deep learning forecasts with interpretable constrained optimization, the approach successfully translates four-week demand predictions for 1,918 units into cost-optimal, executable logistics plans, substantially enhancing operational efficiency.

This study investigates how prompt engineering can enhance the performance, reliability, and interpretability of large language models (LLMs) in data analysis tasks while addressing standardization and ethical challenges. We systematically evaluate structured prompting, Chain-of-Thought reasoning, and automated prompt optimization techniques across diverse domains—including healthcare, materials science, finance, and business intelligence—to uncover the interplay among prompt complexity, model architecture, and task performance. Experimental results demonstrate that the proposed approaches yield performance improvements of 6% to over 30% on multiple real-world tasks, underscoring the significant potential of advanced prompting frameworks to strengthen LLMs’ contextual adaptation and practical deployment efficacy.

This study addresses key challenges in business and financial forecasting—namely poor reproducibility, low model transparency, and weak cross-environment consistency—by systematically evaluating Meta’s open-source Prophet framework. Under a unified experimental design, Prophet is benchmarked against multiple ARIMA variants and Random Forest models, leveraging its additive structure, standardized workflow, and open implementation. The findings demonstrate that Prophet achieves competitive predictive performance while substantially enhancing reproducibility, auditability, and engineering integration efficiency. Rather than introducing a novel algorithm, this work advocates for Prophet as a transparent, reliable, and collaboration-friendly forecasting methodology, particularly suited for high-stakes decision-making contexts where interpretability and robustness are paramount.