sensor data analysis

This work addresses the challenge scientists face in efficiently transforming raw sensor data streams into actionable insights across edge-cloud infrastructures, hindered by the need for cross-domain expertise to manage heterogeneous systems and emerging platforms such as DPUs, which impedes rapid prototyping. To overcome this barrier, the authors propose a novel paradigm that integrates pattern-based workflow engineering with AI-assisted development. Implemented on the FABRIC testbed using the Pegasus workflow system and exemplified by the Orcasound hydrophone workflow, this approach enables swift construction of applications for air quality, seismic, and soil moisture monitoring. The framework supports modular extensibility and edge deployment, substantially lowering the barrier for non-expert users to iteratively develop distributed applications. Empirical validation across multiple use cases demonstrates its effectiveness in enhancing development efficiency, accelerating prototyping cycles, and accumulating practical deployment experience.

This work addresses the complexity of developing edge-to-cloud sensor applications, which typically requires cross-domain collaboration and hinders efficient transformation of raw data into actionable insights. To streamline this process, the authors propose an intent-driven, AI-assisted rapid development methodology that integrates reusable workflow patterns with intelligent configuration, enabling seamless edge adaptation and deployment without code rewriting. Built upon the Pegasus workflow system and deployed on the FABRIC testbed, the approach supports heterogeneous edge resources such as BlueField-3 DPUs and Raspberry Pi devices. Users can construct multi-stage sensing applications within 1–1.5 days, and the framework’s robustness and portability have been validated through real-world deployments in air quality, seismic activity, and soil moisture monitoring scenarios.

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.

In industrial settings (e.g., mining, manufacturing), the absence of structured event logs and the semantic ambiguity of raw sensor data hinder process mining. To address this, we propose an AI-driven four-stage pipeline: (1) preprocessing and unsupervised clustering of raw IoT sensor data; (2) domain-aware large language model (LLM) integration with structured prompting to automatically generate semantically rich, business-interpretable activity labels; (3) construction of XES-compliant high-level event logs. To objectively evaluate label quality, we introduce a similarity-weighted accuracy metric. Experiments on real-world LHD mining equipment sensor data demonstrate significant improvements in label consistency and log usability, effectively bridging the semantic gap between low-level sensor data and process mining requirements. Our approach enables robust, interpretable, and standards-compliant event log generation without manual annotation or domain-specific rule engineering.

In industrial and IoT applications, high-ratio compression of real-time/historical process data reduces storage costs and improves efficiency but risks degrading the accuracy of statistical analysis, anomaly detection, and machine learning models. This paper systematically evaluates the impact of mainstream time-series compression algorithms—including PCA, SAX, and Delta encoding—on the preservation of critical data features, integrating theoretical analysis, controlled simulation experiments, and multi-scenario empirical validation. We first quantify the nonlinear relationship between compression ratio and analytical bias, identifying safety thresholds that guarantee analysis fidelity. Building on these findings, we propose a hierarchical compression strategy and engineering best practices that jointly optimize storage efficiency and analytical reliability. Results demonstrate that moderate compression preserves over 95% of model performance, whereas compression beyond the identified thresholds severely distorts statistical metrics and causes sharp declines in prediction accuracy.

This study addresses a critical limitation in traditional reproducible research, where sharing only code and results fails to expose the implicit assumptions, expectations, and premises underlying an analyst’s reasoning—thereby hindering thorough evaluation of analytical quality. To overcome this, the paper proposes a formal modeling framework that explicitly translates the analyst’s tacit reasoning process into structured logical representations, statically capturing the construction logic of the analysis. This approach enables systematic scrutiny of the analytical chain of reasoning, assumption sensitivity, and conclusion robustness—even in the absence of the original data. Empirical validation on representative data analysis tasks demonstrates the framework’s effectiveness, achieving both logical visualization and data-free static assessment of analytical integrity.

Earth system science has long suffered from heterogeneous and decentralized sensor metadata standards, hindering trustworthy environmental data analysis and cross-domain reuse. To address this, we propose the first FAIR-compliant, modular sensor metadata modeling framework and develop an open-source Sensor Management System (SMS) that comprehensively covers the full sensor lifecycle—including devices, platforms, configurations, sites, and dynamic operational history. SMS integrates semantic modeling with established open standards (ISO 19115, Schema.org), persistent identifier (PID) registration, and controlled vocabularies, and is implemented as a microservice-based architecture exposing RESTful APIs. Its key innovation lies in enabling structured, traceable, and interoperable metadata across institutions. Deployed across multiple national Earth observation networks, SMS has significantly improved metadata consistency, long-term sustainability, and reuse efficiency.

Existing evaluation approaches for streaming process mining algorithms predominantly rely on static logs or synthetic event streams, which fail to capture the complexity of real-world event streams in IoT environments—such as out-of-order events, concurrency, incomplete cases, and concept drift. This work addresses this gap by introducing, for the first time, a feature framework from data stream research into streaming process mining. It proposes an intent-oriented event stream generation methodology, extends the conceptual model of event streams, and implements a prototype tool, Stream of Intent. This tool enables customizable configuration of key stream characteristics reflective of real-world scenarios, facilitating the generation of controlled, reproducible, and realistically complex event streams. Consequently, it significantly enhances the relevance and adaptability of algorithm evaluation and development in streaming process mining.

This paper addresses the fragmentation between wearable-device data and process mining. We propose an event log enhancement method for personal behavior modeling, integrating smartwatch data (sleep, heart rate, physical activity) and digital calendar data into process event logs via temporal alignment, multi-source data aggregation, and event derivation. Novel semantic events—such as “deep sleep onset” and “prolonged sitting before meeting”—are introduced at the event, case, and activity levels. Three wearable-data fusion pathways are innovatively designed, relaxing process mining’s traditional reliance on structured business logs. Evaluated on 30 days of real-world, multimodal data from individual users, our approach significantly improves the granularity and interpretability of behavioral pattern discovery. It establishes a scalable, process-mining-based paradigm for personalized productivity optimization and holistic well-being analysis.

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.