BEST: A Unified Business Process Enactment via Streams and Tables for Service Computing
- URL: http://arxiv.org/abs/2501.14848v1
- Date: Fri, 24 Jan 2025 13:59:41 GMT
- Title: BEST: A Unified Business Process Enactment via Streams and Tables for Service Computing
- Abstract summary: Business process models are essential for the representation, analysis, and execution of organizational processes.<n>We propose an execution semantics based on the Continuous Query Language (CQL), where CQL statements respond dynamically to streams of events.<n>By defining all executions around a unified event model, we achieve cross-language and cross-paradigm process enactment.
- Score: 0.10923877073891444
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
Related papers
- ContextFlow: In-Context Flow Matching for Robot Manipulation [91.60992853468242]
We introduce ContextFlow, a conditional flow-matching model that learns continuous action distributions for in-context imitation learning.<n>On LIBERO, ContextFlow outperforms ICRT by 35 percentage points in average success rate on unseen task configurations.<n>On real robots, it generalizes to unseen configurations of both single-arm and bimanual tasks, achieving 40% success on a new pen-uncapping configuration.
arXiv Detail & Related papers (2026-09-06T21:53:58Z) - Formal Verification of Agentic Systems over Operational Data [59.98246281888422]
Agentic systems driven by large language models (LLMs) are increasingly deployed in real-world where they act on persistent operational data.<n>Existing approaches do not provide such system-level guarantees.<n>We study here the verification of agentic systems comprising a single LLM and a tool orchestration harness over relational operational data.
arXiv Detail & Related papers (2026-08-04T13:01:30Z) - VADAOrchestra: Neurosymbolic Orchestration of Adaptive Reasoning Workflows [47.70653885737167]
Decision-making in real-world settings rarely follows a fixed script. Instead, it unfolds as a dynamic reasoning process.<n>We introduce VADAOrchestra, a neurosymbolic framework that models complex as evolving reasoning processes.
arXiv Detail & Related papers (2026-06-21T13:10:48Z) - ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows [17.525508735432147]
ProfiliTable is an autonomous multi-agent framework centered on dynamic profiling.<n>It constructs and iteratively refines a unified execution context through interactive exploration, knowledge-augmented synthesis, and feedback-driven refinement.
arXiv Detail & Related papers (2026-05-12T16:42:38Z) - Configurable Runtime Orchestration for Dynamic Data Retrieval in Distributed Systems [0.45835414225547183]
This paper presents a configuration-driven runtime orchestration framework for dynamic data retrieval in distributed systems.<n>The framework generates execution graphs dynamically from configuration at request time, enabling low-latency orchestration without redeploying workflow code when integrations evolve.<n>The paper describes the architecture, execution model, and operational tradeoffs of this framework, and presents a representative enterprise case study for Customer 360 retrieval.
arXiv Detail & Related papers (2026-03-07T01:45:18Z) - FlowSteer: Interactive Agentic Workflow Orchestration via End-to-End Reinforcement Learning [49.369614288007334]
FlowSteer is an end-to-end reinforcement learning framework that takes a lightweight policy model as the agent and an executable canvas environment.<n>We show that FlowSteer significantly outperforms baselines across various tasks.
arXiv Detail & Related papers (2026-02-02T05:30:42Z) - ContextNav: Towards Agentic Multimodal In-Context Learning [85.05420047017513]
ContextNav is an agentic framework that integrates the scalability of automated retrieval with the quality and adaptiveness of human-like curation.<n>It builds a resource-aware multimodal embedding pipeline, maintains a retrievable vector database, and applies agentic retrieval and structural alignment to construct noise-resilient contexts.<n> Experimental results demonstrate that ContextNav achieves state-of-the-art performance across various datasets.
arXiv Detail & Related papers (2025-10-06T07:49:52Z) - DyFlow: Dynamic Workflow Framework for Agentic Reasoning [79.19799197382478]
DyFlow is a dynamic workflow generation framework that adaptively constructs and adjusts reasoning procedures based on task requirements and real-time intermediate feedback.<n>We systematically evaluate DyFlow across diverse domains, including social reasoning, biomedical tasks, mathematical problem solving, and code generation.<n>Results demonstrate that DyFlow significantly outperforms existing baselines, achieving substantial Pass@k improvements and exhibiting robust generalization across diverse domains.
arXiv Detail & Related papers (2025-09-30T10:36:23Z) - Executable Ontologies: Synthesizing Event Semantics with Dataflow Architecture [51.56484100374058]
We demonstrate that integrating semantic event semantics with a dataflow architecture addresses the limitations of traditional Business Process Management systems.<n>The boldsea-engine's architecture interprets semantic models as executable algorithms without compilation.<n>It enables the modification of event models at runtime ensures transparency, and seamlessly merges data and business logic within a unified semantic framework.
arXiv Detail & Related papers (2025-09-11T18:12:46Z) - Business as Rulesual: A Benchmark and Framework for Business Rule Flow Modeling with LLMs [11.320505704072186]
We introduce a novel annotated Chinese dataset, BPRF, which contains 50 business process documents with 326 explicitly labeled business rules across multiple domains.<n>We propose ExIde, a framework for automatic business rule extraction and dependency relationship identification using large language models (LLMs)<n>Our results demonstrate the effectiveness of ExIde in extracting structured business rules and analyzing their interdependencies for current SOTA LLMs.
arXiv Detail & Related papers (2025-05-24T06:13:35Z) - Turning Conversations into Workflows: A Framework to Extract and Evaluate Dialog Workflows for Service AI Agents [65.36060818857109]
We present a novel framework for extracting and evaluating dialog from historical interactions.
Our extraction process consists of two key stages: (1) a retrieval step to select relevant conversations based on key procedural elements, and (2) a structured workflow generation process using a question-answer-based chain-of-thought (QA-CoT) prompting.
arXiv Detail & Related papers (2025-02-24T16:55:15Z) - Learning Task Representations from In-Context Learning [73.72066284711462]
Large language models (LLMs) have demonstrated remarkable proficiency in in-context learning.
We introduce an automated formulation for encoding task information in ICL prompts as a function of attention heads.
We show that our method's effectiveness stems from aligning the distribution of the last hidden state with that of an optimally performing in-context-learned model.
arXiv Detail & Related papers (2025-02-08T00:16:44Z) - Flow: A Modular Approach to Automated Agentic Workflow Generation [53.073598156915615]
Multi-agent frameworks powered by large language models (LLMs) have demonstrated great success in automated planning and task execution.<n>However, the effective adjustment of Agentic during execution has not been well-studied.
arXiv Detail & Related papers (2025-01-14T04:35:37Z) - Direct Encoding of Declare Constraints in ASP [9.599644507730106]
We introduce a novel encoding for Declare constraints that directly models their semantics as ASP rules.<n>We assess the effectiveness of this novel approach on two Process Mining tasks.
arXiv Detail & Related papers (2024-12-13T14:11:33Z) - Flexible Process Variant Binding in Information Systems with Software Product Line Engineering [4.1966208277855745]
Various approaches have been proposed to manage variants in process models.
This paper extends our previous work by allowing for the selection of activity implementations at compile time.
As another challenge different organisation may want to collect and base their decision on different information in a digitised business process.
arXiv Detail & Related papers (2024-10-23T09:10:42Z) - Benchmarking Agentic Workflow Generation [80.74757493266057]
We introduce WorFBench, a unified workflow generation benchmark with multi-faceted scenarios and intricate graph workflow structures.
We also present WorFEval, a systemic evaluation protocol utilizing subsequence and subgraph matching algorithms.
We observe that the generated can enhance downstream tasks, enabling them to achieve superior performance with less time during inference.
arXiv Detail & Related papers (2024-10-10T12:41:19Z) - The Task-oriented Queries Benchmark (ToQB) [0.0]
A standard benchmark for task-oriented queries is not yet available.
Existing benchmarks in the relevant NLP fields have primarily focused on task-oriented dialogues.
arXiv Detail & Related papers (2024-06-05T05:05:41Z) - StateFlow: Enhancing LLM Task-Solving through State-Driven Workflows [23.241520404152904]
StateFlow is a novel task-solving paradigm that conceptualizes complex task-solving processes as state machines.
In StateFlow, we distinguish between "process grounding" (via state and state transitions) and "sub-task solving" (through actions within a state)
Our results show that StateFlow significantly enhances LLMs' efficiency.
arXiv Detail & Related papers (2024-03-17T19:54:16Z) - Code Models are Zero-shot Precondition Reasoners [83.8561159080672]
We use code representations to reason about action preconditions for sequential decision making tasks.
We propose a precondition-aware action sampling strategy that ensures actions predicted by a policy are consistent with preconditions.
arXiv Detail & Related papers (2023-11-16T06:19:27Z) - Predictive Object-Centric Process Monitoring [10.219621548854343]
This thesis shows that a prediction method utilizing Generative Adversarial Networks (GAN), Long Short-Term Memory (LSTM), and Sequence to Sequence models (Seq2seq) can be augmented with the rich data contained in OCEL.
This thesis provides a web interface to predict the next sequence of activities from user input.
arXiv Detail & Related papers (2022-07-20T16:30:47Z) - OPerA: Object-Centric Performance Analysis [0.4014524824655105]
We propose a novel approach to performance analysis by using object-centric Petri nets as formal representations of business processes.
The proposed approach correctly computes existing performance metrics, while supporting the derivation of newly-introduced object-centric performance metrics.
We have implemented the approach as a web application and conducted a case study based on a real-life loan application process.
arXiv Detail & Related papers (2022-04-22T12:23:06Z) - Procedures as Programs: Hierarchical Control of Situated Agents through
Natural Language [81.73820295186727]
We propose a formalism of procedures as programs, a powerful yet intuitive method of representing hierarchical procedural knowledge for agent command and control.
We instantiate this framework on the IQA and ALFRED datasets for NL instruction following.
arXiv Detail & Related papers (2021-09-16T20:36:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.