business applications

Rigid activity implementation binding in digital business processes hinders adaptation to heterogeneous organizational requirements. Method: This paper proposes a three-level dynamic binding mechanism—operating at compile time, launch time, and runtime—that enables concurrent execution of multiple implementations for the same activity and supports context-aware, dynamic customization of input/output data contracts. Integrating Software Product Line (SPL) engineering with Process-Aware Information Systems (PAIS), we develop a variability modeling and runtime feature configuration framework. Contribution/Results: Our approach achieves, for the first time, end-to-end flexible activity binding across the full process lifecycle. It overcomes the limitations of conventional single-version, static binding by enabling on-demand composition of diverse activity implementations and data interfaces within a unified process model. This significantly enhances the adaptability and configurability of process systems in multi-organizational settings.

To address challenges in commercial management system development—including poor alignment between process models and execution platforms, low model reusability, and suboptimal development efficiency—this paper proposes a metamodel-based Model-Driven Development (MDD) approach. We design an evolvable and extensible business process metamodel framework and introduce a staged model transformation mechanism supporting QVT/ATL, enabling automated adaptation of extended BPMN models to diverse execution platforms. Crucially, we deeply integrate MDD into BPM system construction, establishing business models as the authoritative source governing system behavior. Experimental evaluation demonstrates significant improvements in development productivity and model consistency, robust cross-platform model reuse, and validates the metamodel’s effectiveness and flexibility in extended application scenarios such as resource management and customer relationship management.

This study addresses the profound transformations in user roles, workflows, and collaboration patterns within enterprise software platforms driven by artificial intelligence, which existing role frameworks—such as the BTP user type matrix—struggle to accommodate. Through 20 expert interviews and a participatory design workshop involving 24 participants, the research employs qualitative methods to investigate structural shifts in developer roles on the SAP Business Technology Platform. Findings reveal three key trends: automation of operational tasks, expanded human-AI collaboration, and increased reliance on agent-based systems. In response, the study argues for a necessary reconfiguration of role taxonomies and governance mechanisms, offering both theoretical grounding and practical guidance for designing and governing AI-native enterprise software.

To address performance overhead escalation and transaction boundary degradation arising from process decomposition during monolith-to-microservices migration, this paper proposes a lightweight, trace-based what-if analysis method. The approach comprises three stages: execution trace collection and rewriting, performance-sensitive call-chain simulation, and abstract modeling of transaction boundaries—enabling rapid, quantitative assessment of non-functional property changes induced by service decomposition alternatives. Its core innovation lies in introducing the first trace-rewriting analysis paradigm prioritizing usability and speed, requiring neither source-code modification nor deployment in production-like environments. Evaluated on industrial case studies, the method completes each scenario assessment in seconds—achieving two orders-of-magnitude improvement in analysis efficiency—and thereby significantly facilitates high-frequency, low-friction iteration over service boundaries and informed trade-off decisions.

Legacy systems written in COBOL, PL/I, or Assembly—common in banking and telecommunications—are often undocumented and lack original developers, hindering comprehension and modernization. Method: This paper proposes a multi-language, cross-platform, customizable framework for constructing software knowledge graphs and interactively defining architectural boundaries. It integrates static code analysis, data schema parsing, and custom ontology modeling to enable expert-guided, incremental analysis of source code and data architecture, automatically identifying business- and data-driven logical boundaries and visualizing cross-boundary dependencies. Contribution/Results: The framework introduces the first knowledge-graph-driven approach for progressive modernization path planning and impact analysis. Evaluated on two real-world industrial systems, it significantly improves system understanding efficiency and enhances the accuracy of modernization strategy design.

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.

This paper addresses the incompleteness and insufficient semantic coverage in existing translations from BPMN 2.0 to the AI planning language PDDL. We propose the first semantics-complete mapping method supporting all core BPMN elements—tasks, events, sequence flows, and parallel/inclusive gateways. Our approach establishes an end-to-end pipeline comprising BPMN parsing, semantics-preserving transformation, and non-deterministic PDDL model generation, enabling fully automated derivation of executable PDDL models from BPMN process diagrams. We further integrate a non-deterministic planner to perform execution-path reasoning and generate valid behavioral trajectories. Experimental evaluation confirms that all generated trajectories strictly adhere to BPMN semantics. The method significantly broadens the applicability of AI planning to business process automation, analysis, and optimization. By providing a scalable, formal foundation, it advances process intelligence for modeling, verification, and decision support in enterprise workflows.

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 BPMN+DMN process models lack semantic-level automated verification; mainstream tools support only syntactic validation, while behavioral errors require manual execution and debugging, and model transformations remain opaque. Method: We propose the first end-to-end automated verification framework that (i) formally translates BPMN+DMN models into semantics-preserving Java programs; (ii) synthesizes interactive test plans via symbolic execution and input-domain disambiguation; and (iii) provides structured coverage analysis at both node and edge levels. Results: Evaluated on established benchmark processes from the literature, our approach significantly improves semantic defect detection, achieves an average test coverage of 89.3%, and accelerates verification by over 20× compared to manual methods.

This work addresses the challenges of low quality and poor transparency in build-or-buy decisions within enterprise software development, which often stem from reliance on unstructured experiential knowledge. To overcome these limitations—particularly in cold-start scenarios lacking historical data—the authors propose a structured approach that integrates a decision-factor ontology, rule-based reasoning, and reference-class matching. This method enables transparent, auditable evaluation of alternatives and represents the first application of combined ontology modeling and rule reasoning to build-or-buy decision-making. By revealing critical decision thresholds and supporting traceability, the approach enhances the rationality, transparency, and auditability of choices. Its practical efficacy is demonstrated through a lightweight tool validated in a financial industry case study, showing significant improvements in decision quality.