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BEST: A Unified Business Process Enactment via Streams and Tables for Service Computing

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arxiv 2501.14848 v1 pith:W2D565KF submitted 2025-01-24 cs.SE

BEST: A Unified Business Process Enactment via Streams and Tables for Service Computing

classification cs.SE
keywords processexecutionserviceseventstasksbusinessenactmentimplement
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Business process models are essential for the representation, analysis, and execution of organizational processes, serving as orchestration blueprints while relying on (web) services to implement individual tasks. At the representation level, there are two dominant paradigms: procedural (imperative) notations that specify the sequential flows within a process and declarative notations that capture the process as a set of constraints. Although each notation offers distinct advantages in representational clarity and cognitive effectiveness, they are seldom integrated, leading to compatibility challenges. In this paper, we set aside the imperative-declarative dichotomy to focus on orchestrating services that execute the underlying tasks. We propose an execution semantics based on the Continuous Query Language (CQL), where CQL statements respond dynamically to streams of events. As events unfold, these CQL statements update the execution state (tables) and can generate new events, effectively triggering (web) services that implement specific process tasks. By defining all executions around a unified event model, we achieve cross-language and cross-paradigm process enactment. We showcase how industrial process modeling languages, such as BPMN and DCR graphs, can be enacted through CQL queries, allowing seamless orchestration and execution of services across diverse modeling paradigms.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. MultiFluxAI Enhancing Platform Engineering with Advanced Agent-Orchestrated Retrieval Systems

    cs.AI 2025-08 reject novelty 4.0

    The authors claim their MultiFluxAI orchestration framework achieves 95% accuracy and 0-10 ms responses by combining rule-based routing, caching, and graph knowledge stores for multi-service RAG queries.