profiling

Existing visualization tools for compliance checking lack systematic characterization of analytical tasks, hindering rigorous effectiveness evaluation. This paper introduces the first multidimensional task taxonomy specifically designed for compliance checking, modeling core trace-to-model alignment tasks in process mining along six dimensions: objective, method, constraint type, data characteristics, data target, and cardinality. Crucially, this taxonomy explicitly links the semantic requirements of compliance checking with established visual analytics design principles—thereby bridging the semantic gap between process mining and visual analytics. It provides a reusable theoretical framework to rigorously define visualization purposes, evaluate tool effectiveness, and support co-design of analysis systems. As a result, the interpretability and practical utility of complex compliance analysis outcomes are significantly enhanced.

Internet measurement research suffers from an accessibility crisis due to tool fragmentation and high domain expertise requirements—especially during sudden network outages, where manually constructing diagnostic workflows (e.g., topology discovery, routing analysis, dependency modeling) is time-consuming and heavily reliant on expert knowledge. This paper introduces ArachNet, the first LLM-based agent system that automatically generates expert-level measurement workflows. Methodologically, it employs a novel four-role collaborative agent architecture enabling problem decomposition, tool orchestration, multi-framework integration, and closed-loop reasoning. Its key contribution lies in empirically uncovering and formalizing compositional regularities in measurement expertise—demonstrating their automation feasibility for the first time. Experiments show that ArachNet-generated workflows match expert quality, reducing complex analyses from days to minutes, thereby significantly improving diagnostic efficiency, reproducibility, and accessibility across the networking research community.

Scientific instrument information is fragmented, heterogeneous, and poorly interlinked, severely impeding data interpretability, experimental reproducibility, and impact assessment. To address this, we propose the first systematic knowledge graph framework for semantically linking scientific instruments with scholarly outputs. Our approach employs an RDF-based ontology for instrument metadata modeling (e.g., specifications, calibration records, usage logs), multi-source entity alignment, semantic annotation, and heterogeneous data fusion—enabling cross-source integration and semantic interoperability among instruments, datasets, publications, and research infrastructures. The resulting prototype knowledge graph spans multidisciplinary instrumentation and supports advanced querying (e.g., instrument–dataset–publication triple retrieval), usage context analysis, and impact pathway tracing. Empirical evaluation demonstrates significant improvements in research transparency, reproducibility, and knowledge discovery capability.

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.

This study addresses the growing challenge posed by the widespread involvement of AI agents in software development, which undermines the long-standing assumption that development artifacts are exclusively produced by human professionals—an assumption underpinning traditional software metrics. The work systematically exposes how AI-generated traces compromise the foundational premises of established software measurement practices, thereby threatening the validity of prior empirical conclusions. To confront this issue, the authors propose an AI-augmented, systematic replication methodology that integrates modern data analytics with empirical software engineering techniques to rigorously re-evaluate key findings. The project advances a dynamic, reproducible, and sustainable measurement paradigm capable of adapting to evolving data ecosystems, offering a robust and timely framework for software metrics in the AI era.

This work addresses a critical limitation in existing root cause analysis methods for anomalies: their failure to distinguish between two fundamentally distinct sources—measurement errors and mechanism shifts—often leading to misdiagnosis. To resolve this, the paper proposes the first causal framework that explicitly models both anomaly types by treating them as implicit interventions on latent “true” variables and observed “measured” variables. A structural causal model (SCM) with latent variables is constructed, and maximum likelihood estimation is employed to simultaneously classify anomaly types and localize root causes. Theoretically, the approach is shown to be identifiable without requiring prior knowledge of the causal graph structure. Empirical evaluations demonstrate state-of-the-art performance in root cause localization, accurate anomaly-type classification, and robustness even when the underlying causal graph is unknown.

This work addresses the challenge that symbolic execution engines involve numerous parameters with complex, interdependent effects, often leading users—due to limited understanding—to rely on suboptimal default configurations, while existing automated tuning approaches lack interpretability. To bridge this gap, the authors propose a human-in-the-loop parameter tuning paradigm and develop Symetra, a visual analytics system that enables dual-perspective overviews of how parameters influence branch coverage. Symetra supports interactive comparison of configuration sets and facilitates pattern recognition. Experimental results demonstrate that expert users leveraging Symetra not only accurately interpret parameter interactions and identify complementary configurations but also achieve significantly higher branch coverage and tuning efficiency compared to fully automated methods, thereby effectively overcoming the interpretability bottleneck in symbolic execution parameter optimization.

This work addresses the methodological fragility and limited verifiability inherent in complex Internet measurement analyses, which traditionally rely on manual orchestration by experts. To overcome these limitations, the authors propose the first multi-agent framework tailored for Internet measurement, capable of collaboratively generating verifiable measurement workflows. The framework encodes five decades of domain knowledge into a reasoning-enabled knowledge graph and integrates a methodology validation engine with a tool registry to automatically recommend and verify technical approaches. Evaluated across four case studies, the system autonomously produces workflows comparable to those crafted by experts, makes sound architectural decisions, effectively tackles novel problems lacking ground truth, and uncovers methodological flaws undetectable by conventional testing practices.

为解决数据分析工程中治理、质量、来源和可重复性的问题，本文提出UnespDataLens-RM参考模型，通过整合技术操作过程和跨领域能力来提高分析流程的可靠性。

This work addresses the problem of implementation drift in evolving distributed systems, where runtime behavior gradually deviates from the original design. To tackle this issue, the paper proposes a design conformance assessment method based on distributed tracing data. It introduces, for the first time in the domain of distributed systems, conformance checking techniques from process mining, leveraging runtime traces collected via the OpenTelemetry standard and automatically comparing them against behavioral models defined at design time to quantify their alignment. The key contribution lies in establishing persistent, monitorable conformance metrics that enable continuous, automated evaluation of deviations between system implementation and design. This approach is readily applicable to modern distributed systems widely adopting OpenTelemetry for observability.