[论文解读] Process Query Language: Design, Implementation, and Evaluation

Artem Polyvyanyy, Arthur H. M. ter Hofstede+3 more|arXiv (Cornell University)|Sep 20, 2019

Business Process Modeling and Analysis被引用 4

一句话总结

本文提出过程查询语言（PQL），一种基于任务间行为关系（如顺序、并发或互斥）的领域特定语言，用于查询过程模型仓库。该语言使用经过实证验证的行为谓词，并通过形式化证明确保可判定性，且在大规模模型集合上实现近实时性能，从而通过行为感知查询实现有效的审计与自动化。

ABSTRACT

Organizations can benefit from the use of practices, techniques, and tools from the area of business process management. Through the focus on processes, they create process models that require management, including support for versioning, refactoring and querying. Querying thus far has primarily focused on structural properties of models rather than on exploiting behavioral properties capturing aspects of model execution. While the latter is more challenging, it is also more effective, especially when models are used for auditing or process automation. The focus of this paper is to overcome the challenges associated with behavioral querying of process models in order to unlock its benefits. The first challenge concerns determining decidability of the building blocks of the query language, which are the possible behavioral relations between process tasks. The second challenge concerns achieving acceptable performance of query evaluation. The evaluation of a query may require expensive checks in all process models, of which there may be thousands. In light of these challenges, this paper proposes a special-purpose programming language, namely Process Query Language (PQL) for behavioral querying of process model collections. The language relies on a set of behavioral predicates between process tasks, whose usefulness has been empirically evaluated with a pool of process model stakeholders. This study resulted in a selection of the predicates to be implemented in PQL, whose decidability has also been formally proven. The computational performance of the language has been extensively evaluated through a set of experiments against two large process model collections.

研究动机与目标

• 解决现有过程查询语言侧重于过程模型结构而非行为方面的不足。
• 基于任务间的行为关系，实现对过程模型仓库的高效且语义有意义的查询。
• 通过利益相关者研究，实证验证行为谓词在过程模型查询中的相关性。
• 通过形式化证明确保行为谓词的可判定性，提升查询评估的可靠性。
• 评估PQL在真实和合成过程模型集合上的性能，以证明其可行性和可扩展性。

提出的方法

• 设计具有类似SQL语法的PQL，以提升可用性并支持复杂的行为查询。
• 基于与过程模型利益相关者的实证评估，定义一组核心行为谓词（例如：can-execute、never-execute、concurrent）。
• 通过形式化证明确保每个行为谓词的可判定性，以保证查询评估的正确性和一致性。
• 实现一个查询引擎，使用优化算法在大规模过程模型仓库上评估PQL查询。
• 支持可配置阈值的标签相似度匹配，以处理过程任务中的命名差异。
• 使用真实世界和合成的过程模型集合评估性能，以测量查询执行时间和可扩展性。

Figure 3. Abstract syntax tree of the example PQL query Q3 in Section 2 . The numbers in parenthesis are used in Section 4.5 to explain the meaning of the query.

实验结果

研究问题

• RQ1 哪些过程任务间的行为关系在查询过程模型仓库时最为相关和有用？
• RQ2 行为谓词能否通过形式化证明确保可判定性，以保证可靠的查询评估？
• RQ3 基于行为谓词的查询语言在大规模过程模型集合上的性能如何？
• RQ4 PQL如何通过标签相似度和行为感知语义支持直观且高效的查询？
• RQ5 PQL在多大程度上能够支持复杂搜索意图，例如基于通配符的模式查询？

主要发现

• 实证研究识别出一组在行为上具有意义的谓词，这些谓词对过程模型查询最为相关，构成了PQL的基础。
• 所有核心PQL谓词均通过形式化证明确保可判定性，保证了正确性，并支持可靠的查询评估。
• PQL查询引擎在大规模过程模型集合上实现了近实时响应时间，证明了其实际可行性。
• 具有可配置阈值的标签相似度匹配有效处理了模型间命名差异，提升了检索准确性。
• 性能评估证实，PQL可扩展至数千个模型，且响应时间可接受，支持工业用例。
• 该语言支持复杂搜索模式，包括基于通配符的执行模板，从而支持表达性强的行为查询。

Figure 6. A system obtained via unification of the set of labels $\{\texttt{a},\texttt{a}^{\prime}\}$ in the workflow system in Figure 4 .