order management

To address coarse-grained customer journey analysis, fragmentation between inventory and sales data, and the difficulty in quantifying RFID value in retail, this paper proposes an end-to-end analytical framework integrating RFID trajectory data with point-of-sale (POS) transaction records. It introduces a lightweight ETL architecture and a behavior–product co-occurrence–oriented logical data model to construct a multidimensional spatiotemporal data warehouse. Furthermore, a Balanced Scorecard (BSC) is adopted to systematically evaluate RFID deployment impact across four dimensions: financial performance, customer experience, internal processes, and learning & growth. Experiments demonstrate that the framework improves inventory count accuracy to 99.2%, reduces shrinkage in high-risk zones by 37%, and identifies three distinct customer path–conversion rate association patterns. The approach delivers a reusable methodology and empirical validation for data-driven, fine-grained retail operations.

This paper addresses the modeling and analysis of valid execution traces in process systems governed by precedence and response constraints. We formalize the constraint set as a partially ordered set (poset) and establish, for the first time, a bijective correspondence between precedence/response constraint systems and linear extensions of their associated posets—thereby enabling a complete combinatorial characterization of feasible traces. Building on this foundation, we develop an exact classification framework for trace sets, supporting quantitative evaluation of process utility. Our approach integrates order theory, constraint satisfaction modeling, and linear extension theory, substantially enhancing the computability and cross-system comparability of constraint-driven processes. The theoretical framework advances process mining and conformance checking by providing rigorous foundations for trace enumeration, constraint verification, and utility-aware process analysis.

Imperative process models (e.g., Petri nets) exhibit semantic and execution-level incompatibility with structured process data in relational databases, hindering data-driven compliance analysis. To address this, we propose an automated model-to-query translation method that maps Petri net models to relaxed SQL queries, incorporating declarative techniques—such as behavioral footprints—to formally encode process constraints. Our approach ensures semantic traceability while unifying model-driven and data-driven analysis. It enables direct generation of executable, verifiable database queries from formal process models, and is empirically validated on real-world industrial datasets. The core contribution is a computationally grounded bridge between imperative process models and relational data, significantly enhancing the reusability and practical applicability of existing process models in data-intensive, compliance-critical scenarios.

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.

Existing business process modeling practices separate process flows from business rules, leading to fragmented mental models and impaired comprehension among expert process workers. Method: This study employs a mixed-methods design integrating eye-tracking and concurrent verbal protocol analysis, coupled with cognitive-behavioral coding, to investigate how domain experts perform sensemaking when interpreting integrated process-rule models. Contribution/Results: We identify fine-grained visual search patterns and cognitive bottlenecks that critically affect comprehension efficiency during information foraging and cognitive processing stages. Based on these findings, we propose empirically grounded design principles for personalized cognitive support targeting knowledge workers. The results provide actionable evidence to enhance integrated modeling languages, tool interfaces, and training strategies—advancing business process modeling from syntactic formalism toward cognitive alignment and human-centered design.

This study addresses the absence of a general principled framework for schema engineering workflows and the difficulty existing systems face in composably representing schema operations. To overcome these limitations, this work proposes a typed logic-based workflow framework. By defining typed representations of fixed components, the approach translates schema operations into composable query chains, thereby decoupling workflow definitions from execution constraints. Furthermore, it designs an LLM agent-driven execution pipeline to enable automated analysis. Experiments conducted on the WIPO dataset successfully execute three categories of query tasks, including exemplar mining. The results validate performance variations across different LLM configurations alongside their error localization capabilities. Ultimately, this research establishes a modular and orchestrable paradigm for schema engineering.

为解决ERP系统中数据集成和流程监控的碎片化问题，本文提出一种企业流程控制塔，通过集成状态观测、语义翻译、机器学习诊断等方法提升IT团队的工作效率。

This study addresses the lack of a systematic synthesis and cross-directional integration of formal grammars in business process management (BPM). Through a systematic literature review of 34 core studies, it identifies and integrates seven distinct application areas of formal grammars in BPM for the first time, revealing their largely isolated development. Leveraging theoretical foundations such as the Bunge-Wand-Weber ontology, process algebras, graph grammars, attribute grammars, and grammar inference, the work constructs a comprehensive taxonomy that clarifies the role of formal grammars across the entire BPM lifecycle—including process design, modeling, execution, verification, and mining. Furthermore, it articulates five corpus-based open challenges, laying the groundwork for a unified syntactic theory and its deeper integration into BPM research and practice.

This work addresses the significant limitations of spreadsheet-based analysis in reproducibility, auditability, version control, and automation. It proposes a migration pathway from Excel to research-grade analytical workflows by leveraging Python’s pandas library as a bridge. The study introduces an innovative set of Excel-to-pandas mapping rules, categorizes nine canonical workflow patterns, and compiles a catalog of common failure modes. Seven end-to-end real-world examples demonstrate the approach in practice. By retaining Excel as a familiar interface for input and output while integrating version control, automated refreshing, and seamless incorporation of statistical and machine learning methods, the proposed framework enables governed, reproducible, and auditable tabular data analysis.

This study addresses the limitations of existing SysML verification approaches, which are often tool-dependent and restricted to performance properties, lacking support for automated validation of behavioral and interface requirements. To overcome these shortcomings, this work proposes a tool-agnostic, automated verification workflow driven by SysML test cases, integrating UML Testing Profile and behavioral diagram constructs to enable unified validation of multidimensional attributes—including behavior, timing, and state responses. The methodology was developed through a mixed-methods research strategy combining literature review and stakeholder interviews, and its efficacy was empirically validated across two independent SysML toolchains. The approach not only transcends the constraints of conventional parametric methods but also enables automatic traceability of verification results back to the original model elements.