Information Systems最新文献_第4页

Paper title: Event log imperfection patterns for process mining: Towards a systematic approach to cleaning event logs 论文题目：过程挖掘的事件日志缺陷模式：走向清除事件日志的系统方法

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-11-06 DOI: 10.1016/j.is.2025.102645

Robert Andrews, Moe Thandar Wynn

Process-oriented data mining (process mining) uses algorithms and data (in the form of event logs) to construct models that aim to provide insights into organisational processes. The quality of the data (both form and content) presented to the modeling algorithms is critical to the success of the process mining exercise. Cleaning event logs to address quality issues prior to conducting a process mining analysis is a necessary, but generally tedious and ad hoc task. In this paper we describe a set of data quality issues, distilled from our experiences in conducting process mining analyses, commonly found in process mining event logs or encountered while preparing event logs from raw data sources. We show that patterns are used in a variety of domains as a means for describing commonly encountered problems and solutions. The main contributions of this article are in showing that a patterns-based approach is applicable to documenting commonly encountered event log quality issues, the formulation of a set of components for describing event log quality issues as patterns, and the description of a collection of 11 event log imperfection patterns distilled from our experiences in preparing event logs. We postulate that a systematic approach to using such a pattern repository to identify and repair event log quality issues benefits both the process of preparing an event log and the quality of the resulting event log. The relevance of the pattern-based approach is illustrated via application of the patterns in a case study and through an evaluation by researchers and practitioners in the field.

面向流程的数据挖掘（流程挖掘）使用算法和数据（以事件日志的形式）来构建旨在提供对组织流程的洞察的模型。呈现给建模算法的数据（包括形式和内容）的质量对于流程挖掘练习的成功至关重要。在进行流程挖掘分析之前，清理事件日志以解决质量问题是必要的，但通常是单调乏味的临时任务。在本文中，我们描述了一组数据质量问题，这些问题是从我们进行过程挖掘分析的经验中提炼出来的，通常在过程挖掘事件日志中发现，或者在从原始数据源准备事件日志时遇到。我们展示了在各种领域中使用模式作为描述常见问题和解决方案的手段。本文的主要贡献在于展示了一种基于模式的方法适用于记录常见的事件日志质量问题，阐述了一组用于将事件日志质量问题描述为模式的组件，以及描述了从我们准备事件日志的经验中提炼出来的11种事件日志缺陷模式。我们假设，使用这种模式存储库来识别和修复事件日志质量问题的系统方法对准备事件日志的过程和生成的事件日志的质量都有好处。基于模式的方法的相关性通过案例研究中的模式应用以及该领域的研究人员和实践者的评估来说明。

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引用次数: 0

Nine years later: Reflecting on our article 九年后：反思我们的文章

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-11-04 DOI: 10.1016/j.is.2025.102644

Massimiliano de Leoni , Wil M.P. van der Aalst , Marcus Dees

This contribution revisits our article titled “A General Process Mining Framework for Correlating, Predicting, and Clustering Dynamic Behavior Based on Event Logs”, published in the Information Systems journal in 2016. It reflects on how the proposed general framework for process mining has grown in relevance with the rise of AI, emphasizing its value as a extensible approach to transforming event data into analytical and predictive insights. It also discusses how the framework relevance and the underlying message remains valid, including for emerging research directions such as prescriptive analytics, causal and/or object-centric process mining.

这篇文章回顾了我们2016年发表在《信息系统》杂志上的文章《基于事件日志的动态行为关联、预测和聚类的通用流程挖掘框架》。它反映了拟议的流程挖掘通用框架如何随着人工智能的兴起而增长，强调了其作为将事件数据转换为分析和预测见解的可扩展方法的价值。它还讨论了框架相关性和底层信息如何保持有效性，包括新兴的研究方向，如规定性分析、因果关系和/或以对象为中心的过程挖掘。

引用次数: 0

SOLID-M: An ontology-aware quality framework for conceptual models discovered from event data SOLID-M：一个本体感知的质量框架，用于从事件数据中发现概念模型

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-11-04 DOI: 10.1016/j.is.2025.102641

Andrei Tour , Artem Polyvyanyy , Anna Kalenkova

In Process Mining (PM), “high-level” conceptual models of business processes, in the form of directly-follows graphs, Petri nets, and finite-state automata, are discovered from “low-level” event data recorded by information systems. The quality of the discovered models is usually assessed by measures that depend on assumptions made by discovery algorithms; for example, they often assume that sequences of activities recorded in the event data do not interfere. Models produced by recent discovery algorithms consider domain knowledge and relax these assumptions, making traditional PM measures less suitable for evaluating their quality. This paper proposes an ontology-aware framework, called SOLID-M, for analyzing the quality of conceptual models discovered from event data generated by systems. SOLID-M relies on domain knowledge and provides guidelines for introducing quality measures for models constructed by process discovery algorithms that go beyond the traditional PM assumptions. In addition, the paper describes an instantiation of the framework for assessing the quality of Multi-Agent System models discovered using Agent System Mining techniques, hence addressing a growing demand for data-driven analysis of business processes emerging in interactions of human and artificial intelligence agents.

在流程挖掘（Process Mining， PM）中，从信息系统记录的“低级”事件数据中发现业务流程的“高级”概念模型，其形式为直接跟随图、Petri网和有限状态自动机。所发现模型的质量通常通过依赖于发现算法所做的假设的度量来评估；例如，它们通常假设记录在事件数据中的活动序列不会相互干扰。由最近的发现算法产生的模型考虑了领域知识并放宽了这些假设，使得传统的PM度量不太适合评估它们的质量。本文提出了一个本体感知框架，称为SOLID-M，用于分析从系统生成的事件数据中发现的概念模型的质量。SOLID-M依赖于领域知识，并为超越传统PM假设的过程发现算法构造的模型提供了引入质量度量的指导方针。此外，本文还描述了一个框架的实例，用于评估使用代理系统挖掘技术发现的多代理系统模型的质量，从而解决了对人类和人工智能代理交互中出现的业务流程的数据驱动分析的日益增长的需求。

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引用次数: 0

Implementing a declarative query language for high level machine learning application design 实现用于高级机器学习应用程序设计的声明式查询语言

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-11-03 DOI: 10.1016/j.is.2025.102637

Hasan H. Rahman , Hasan M. Jamil

The rising popularity of data science and machine learning (ML) across diverse domains, often driven by users with limited computational expertise, reflects the growing commoditization of ML tools. However, the advanced technical and mathematical knowledge demanded by current ML frameworks poses a formidable barrier for non-experts, preventing them from fully exploiting these powerful platforms.

In response, we introduce MQL, a novel declarative query language for ML application design, alongside its corresponding query processing engine. We demonstrate that abstracting ML concepts – similarly to SQL – can preserve both processing efficiency and analytical fidelity. Our implementation defines MQL semantics through a semantics-preserving mapping to widely understood ML code fragments. By leveraging task-specific meta-features, heuristic knowledge, and standard assessment methods, our system ranks candidate ML libraries, selects optimal algorithms, and frees users from these choices.

We introduce mapping algorithms to ensure that each MQL program retains its intended semantics and present experimental evaluations demonstrating that MQL’s algorithmic selections not only match but surpass human-engineered solutions in terms of performance and model accuracy. By offering declarative queries as a high-level alternative to traditional coding, MQL significantly reduces the complexity of data analysis pipeline construction, thereby democratizing machine learning application design.

数据科学和机器学习（ML）在不同领域的日益普及，通常是由计算专业知识有限的用户驱动的，这反映了ML工具日益商品化。然而，当前机器学习框架所要求的先进技术和数学知识对非专家构成了巨大的障碍，使他们无法充分利用这些强大的平台。作为回应，我们介绍了MQL，一种用于ML应用程序设计的新型声明性查询语言，以及相应的查询处理引擎。我们证明了抽象ML概念——类似于SQL——可以同时保持处理效率和分析保真度。我们的实现通过保持语义的映射来定义MQL语义，映射到广泛理解的ML代码片段。通过利用任务特定的元特征、启发式知识和标准评估方法，我们的系统对候选ML库进行排名，选择最优算法，并将用户从这些选择中解放出来。我们引入映射算法，以确保每个MQL程序保留其预期的语义，并提出实验评估，证明MQL的算法选择不仅匹配，而且在性能和模型精度方面超过了人类工程解决方案。通过提供声明性查询作为传统编码的高级替代方案，MQL显着降低了数据分析管道构建的复杂性，从而使机器学习应用程序设计民主化。

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引用次数: 0

Enhancing next activity prediction in process mining with Retrieval-Augmented Generation 利用检索增强生成增强流程挖掘中的下一个活动预测

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-11-03 DOI: 10.1016/j.is.2025.102642

Angelo Casciani , Mario Luca Bernardi , Marta Cimitile , Andrea Marrella

Next activity prediction is one of the main tasks of Predictive Process Monitoring (PPM), enabling organizations to forecast the execution of business processes and respond accordingly. Deep learning models are effective at predictions, but with the price of intensive training and feature engineering, rendering them less generalizable across domains. Large Language Models (LLMs) have been recently suggested as an alternative, but their capabilities in Process Mining tasks are still to be extensively investigated. This work introduces a framework leveraging LLMs and Retrieval-Augmented Generation to enhance their capabilities for predicting next activities. By leveraging sequential information and data attributes from past execution traces, our framework enables LLMs to make more accurate predictions without additional training. We evaluate the approach on a wide range of event logs and compare it with state-of-the-art techniques. Findings show that our framework achieves competitive performance while being more adaptable across domains. Moreover, we assess early prediction capabilities, validate the significance of observed differences through statistical testing, and explore the impact of fine-tuning. Despite these advantages, we also report the framework’s limitations, mainly related to interleaving activity sensitivity and concept drifts. Our findings highlight the potential of retrieval-augmented LLMs in PPM while identifying the need for future research into handling evolving process behaviors and the development of standard benchmarks.

下一个活动预测是预测性流程监控（PPM）的主要任务之一，它使组织能够预测业务流程的执行并做出相应的响应。深度学习模型在预测方面是有效的，但由于密集训练和特征工程的代价，使得它们在跨领域的泛化性较差。大型语言模型（llm）最近被建议作为一种替代方案，但它们在过程挖掘任务中的能力仍有待广泛研究。这项工作引入了一个框架，利用llm和检索增强生成来增强它们预测下一个活动的能力。通过利用来自过去执行轨迹的顺序信息和数据属性，我们的框架使法学硕士能够在没有额外培训的情况下做出更准确的预测。我们在各种事件日志上评估该方法，并将其与最先进的技术进行比较。研究结果表明，我们的框架在实现竞争性性能的同时，更具有跨领域的适应性。此外，我们评估了早期预测能力，通过统计检验验证了观察到的差异的显著性，并探讨了微调的影响。尽管有这些优点，我们也报告了框架的局限性，主要涉及交叉活动敏感性和概念漂移。我们的发现强调了在PPM中检索增强llm的潜力，同时确定了对处理不断发展的过程行为和标准基准开发的未来研究的需要。

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引用次数: 0

KAVA-PM: Knowledge-assisted visual process mining KAVA-PM：知识辅助视觉过程挖掘

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-11-01 DOI: 10.1016/j.is.2025.102638

Daniel Schuster , Wolfgang Aigner , Chiara Di Francescomarino , Cagatay Turkay , Francesca Zerbato

This article aims to foster a collaborative environment between the visual analytics and process mining communities by bringing together analysis methods, techniques, and tools from the process mining and visual analytics domains to devise a new knowledge-assisted, human-in-the-loop approach to process mining. Building on recent advances in methods emphasizing the role of human knowledge in analysis, we introduce knowledge-assisted interactive visual process mining (KAVA-PM) as a framework where analysts’ tacit knowledge and the externalizations of this knowledge play a key role. To achieve this, we extend an established conceptual model of KAVA that combines interactive visualizations and automated methods to support a richer process mining analysis practice that has human experts and their knowledge at its core. The paper outlines the key components of KAVA-PM as a conceptual model and its relations, proposes key analytical patterns, and demonstrates the use and validity of the patterns through usage scenarios. We then present challenges and open problems which we validate through a survey with experts. We anticipate that along with the conceptual model, these challenges will bring the VA and PM communities together along a shared research agenda where the role of humans and their knowledge is better established.

本文旨在通过将过程挖掘和可视化分析领域的分析方法、技术和工具结合起来，为过程挖掘设计一种新的知识辅助的、人在循环中的方法，从而在可视化分析和过程挖掘社区之间培育一个协作环境。在强调人类知识在分析中的作用的方法的最新进展的基础上，我们介绍了知识辅助交互式可视化过程挖掘（kva - pm）作为一个框架，其中分析师的隐性知识和这种知识的外部化发挥了关键作用。为了实现这一点，我们扩展了已建立的KAVA概念模型，该模型结合了交互式可视化和自动化方法，以支持以人类专家及其知识为核心的更丰富的过程挖掘分析实践。本文概述了作为概念模型的kva - pm的关键组成部分及其关系，提出了关键的分析模式，并通过使用场景演示了这些模式的使用和有效性。然后，我们提出挑战和开放的问题，我们通过与专家的调查来验证。我们预计，随着概念模型的建立，这些挑战将使VA和PM社区沿着一个共享的研究议程走到一起，在这个议程中，人类的角色和他们的知识将得到更好的确立。

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引用次数: 0

Unsupervised and semi-supervised clustering via density and distance-based label propagation and assignment 基于密度和距离的标签传播和分配的无监督和半监督聚类

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-10-28 DOI: 10.1016/j.is.2025.102639

Zhen Jiang, Bolin Niu, Jinxin Gua, Yuping Xing

Density-based clustering is capable of identifying clusters of arbitrary shapes without the need to predefine the number of clusters or their distributions. However, it suffers from varying density and parameter sensitivity. To tackle these challenges, we present the Density and Distance-Based Clustering (DDBC) algorithm, which performs clustering from the backbone to the foliage. Based on the “K_cutoff” neighborhoods of core points, DDBC constructs the cluster backbone through label propagation and subcluster aggregation. Subsequently, we construct cluster prototypes and leverage point-prototype distances to help assign points located outside the backbone. The proposed method effectively mitigates issues related to varying density. Furthermore, we propose a semi-supervised version of DDBC, termed SS-DDBC, which utilizes a few labeled data to guide label propagation and subcluster aggregation. It provides a safe and adaptive approach to leverage class information for semi-supervised clustering. Moreover, we propose automated parameter optimization approaches for DDBC and SS-DDBC, thus addressing the issue of parameter sensitivity. In both unsupervised and semi-supervised settings, we conducted experimental comparisons of DDBC and SS-DDBC with ten state-of-the-art algorithms across a range of benchmark datasets. Both algorithms consistently outperform their competitors in terms of average performance and achieve superior results on the majority of datasets. These experimental results demonstrate the effectiveness of our proposed methods. The source codes for our algorithms are accessible at https://github.com/nblnbl/DDBC.

基于密度的聚类能够识别任意形状的簇，而不需要预先定义簇的数量或它们的分布。然而，它受到不同密度和参数灵敏度的影响。为了解决这些挑战，我们提出了基于密度和距离的聚类（DDBC）算法，该算法执行从主干到叶子的聚类。DDBC基于核心点的“K_cutoff”邻域，通过标签传播和子集群聚合构建集群骨干。随后，我们构建集群原型并利用点原型距离来帮助分配位于主干之外的点。所提出的方法有效地缓解了与密度变化有关的问题。此外，我们提出了一种半监督版本的DDBC，称为SS-DDBC，它利用少量标记数据来指导标签传播和子聚类聚合。它提供了一种安全和自适应的方法来利用类信息进行半监督聚类。此外，我们提出了DDBC和SS-DDBC的自动参数优化方法，从而解决了参数敏感性问题。在无监督和半监督设置中，我们在一系列基准数据集上使用十种最先进的算法对DDBC和SS-DDBC进行了实验比较。这两种算法在平均性能方面始终优于竞争对手，并且在大多数数据集上取得了优异的结果。实验结果证明了所提方法的有效性。我们的算法的源代码可以在https://github.com/nblnbl/DDBC上访问。

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引用次数: 0

The effects of database normalization on decision support system performance 数据库规范化对决策支持系统性能的影响

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-10-28 DOI: 10.1016/j.is.2025.102636

Marin Fotache , Marius-Iulian Cluci , Toni Taipalus , George Talaba

Relational database normalization strives to minimize update anomalies and data redundancy, often at the cost of performance. Transactional systems typically require a higher degree of normalization since data are updated more frequently than in read-intensive decision support systems. While these reasons for and effects of normalization can be considered common knowledge, there are hardly any empirical studies on the query performance implications of various degrees of normalization in decision support systems. That is, it seems that the magnitude of the effects of normalization is not widely understood, even though performance implications are of importance to managers and analysts utilizing decision analytics, and for end-user information needs to be timely satisfied. In this study, the effects of normalization on a decision support database were tested for three popular SQL/relational database servers. The results raise serious concerns about the conventional consensus on the performance gains incurred by the reduced number of table joins. Even for small-sized databases, the penalties due to the extra volume caused by redundancy associated with lower normal forms seem larger than the performance gains due to the reduced number of joins. These results have practical implications on which design principles should be followed for efficient decision support system databases.

关系数据库规范化力求最小化更新异常和数据冗余，这通常是以性能为代价的。事务系统通常需要更高程度的规范化，因为数据更新比读取密集型决策支持系统更频繁。虽然这些归一化的原因和影响可以被认为是常识，但很少有关于决策支持系统中不同程度的归一化对查询性能影响的实证研究。也就是说，尽管绩效影响对利用决策分析的管理人员和分析人员很重要，并且对最终用户的信息需要及时得到满足，但标准化的影响的大小似乎没有被广泛理解。在本研究中，规范化对决策支持数据库的影响在三个流行的SQL/关系数据库服务器上进行了测试。结果引起了人们对减少表连接数量所带来的性能提高的传统共识的严重关注。即使对于小型数据库，由于与较低标准形式相关的冗余而导致的额外容量所带来的损失似乎也大于由于减少连接数量而带来的性能收益。这些结果对有效的决策支持系统数据库应遵循哪些设计原则具有实际意义。

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引用次数: 0

Modern alternatives to hive: A systematic review and single-node benchmark of SQL-on-Hadoop and lakehouse engines hive的现代替代品：对SQL-on-Hadoop和lake - house引擎的系统回顾和单节点基准测试

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-10-08 DOI: 10.1016/j.is.2025.102635

Szoke Mark-Andor

This paper presents a systematic review of modern SQL-on-Hadoop and lakehouse engines as alternatives to Apache Hive and complements it with a new experimental benchmark on a single, common platform. Twelve representative engines are analyzed (selected for architectural diversity, ecosystem relevance, and evidence availability) and a compact feature taxonomy is introduced, covering vectorized execution, whole-stage code generation, LLVM/JIT, federated pushdown, cloud columnar caches, and embedded ML primitives. In addition to the qualitative synthesis, 11 engines (all except Databricks Photon) are benchmarked locally on standardized TPC-H queries at two scale factors (SF = 1 and SF = 10) using the same hardware and software environment. All engines were tested on a single node (4 CPU cores, 8 hardware threads) to provide a common, multi-threaded baseline. A multi-node (cloud) evaluation is outside the present scope and is discussed as future work in Section 6. The results show order-of-magnitude differences between engines: native/vectorized engines (e.g., DuckDB, ClickHouse, Impala, Velox-accelerated Presto) achieve substantially lower runtimes and more efficient scale-up than JVM-only stacks (e.g., Spark SQL, Trino) on a single node. Drill failed at SF = 10 due to out-of-memory errors; Phoenix completed SF = 1 but did not complete SF = 10 on an 8 GB system (one long query aborting). Photon is discussed from the literature because it cannot be executed outside Databricks. All scripts, data, and a runnable artifact for the benchmark are open-sourced to support reproducibility and reuse. Scope: While the primary focus is distributed SQL-on-Hadoop systems, modern single-node engines (e.g., DuckDB, DataFusion) are included where they (i) implement architectural innovations relevant to distributed analytics or (ii) are used in lakehouse deployments. These are explicitly distinguished from distributed systems in the taxonomy and results.

本文系统地回顾了现代SQL-on-Hadoop和lake - house引擎作为Apache Hive的替代品，并在单一通用平台上提供了一个新的实验基准。本文分析了12个具有代表性的引擎（根据架构多样性、生态系统相关性和证据可用性进行了选择），并介绍了一个紧凑的特征分类法，涵盖了向量化执行、全阶段代码生成、LLVM/JIT、联合下推、云列式缓存和嵌入式ML原语。除了定性合成之外，11个引擎（Databricks Photon除外）使用相同的硬件和软件环境，在两个尺度因子（SF = 1和SF = 10）下对标准化TPC-H查询进行本地基准测试。所有引擎都在单个节点（4个CPU内核，8个硬件线程）上进行测试，以提供一个通用的多线程基线。多节点（云）评估不在当前范围内，将在第6节作为未来的工作进行讨论。结果显示了引擎之间的数量级差异：原生/矢量化引擎（例如DuckDB、ClickHouse、Impala、veloxaccelerated Presto）在单个节点上实现了比纯jvm堆栈（例如Spark SQL、Trino）更低的运行时间和更有效的扩展。由于内存不足错误，在SF = 10时钻孔失败；Phoenix在一个8gb的系统上完成了SF = 1，但没有完成SF = 10（一个长查询中止）。Photon是从文献中讨论的，因为它不能在Databricks之外执行。基准测试的所有脚本、数据和可运行工件都是开源的，以支持可再现性和重用性。范围：虽然主要关注的是分布式SQL-on-Hadoop系统，但现代单节点引擎（例如DuckDB， DataFusion）也包括在它们(i)实现与分布式分析相关的架构创新或（ii）在湖边部署中使用的地方。它们在分类法和结果上与分布式系统明显不同。

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引用次数: 0

Graph functional dependencies: Analysis and translation to PG-schema 图功能依赖关系：分析并转换为pg模式

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems

Pub Date : 2025-09-29 DOI: 10.1016/j.is.2025.102633

Maude Manouvrier, Khalid Belhajjame

Officially published as an ISO/IEC standard in April 2024, the Graph Query Language (GQL) aims to establish itself as the standard language for querying graph data, much like SQL is for relational data. The graph database community has also recently introduced additional specifications, such as PG-Key and, later, PG-Schema, to define graph schemas and dependencies. At the same time, several proposals have emerged in the literature to express Functional Dependency constraints in graph data. Given the wide range of Graph Dependencies presented in the literature, the first contribution of this article is a survey of Graph Functional Dependencies in existing proposals, highlighting the most significant ones, their differences, and their relative expressiveness. In a second contribution, we align with the goals of the graph database community by proposing mappings to translate different kinds of Graph Functional Dependencies from the literature into PG-Schema-compliant dependencies. These mappings are implemented within a publicly available tool PG-FD, which to our knowledge, is the first solution capable of transforming Graph Dependencies into the PG-Schema standard while fully preserving their semantics.

图查询语言（GQL）于2024年4月作为ISO/IEC标准正式发布，其目标是将自己建立为查询图数据的标准语言，就像SQL用于关系数据一样。图数据库社区最近还引入了额外的规范，例如PG-Key和后来的PG-Schema，用于定义图模式和依赖关系。与此同时，文献中出现了几种表达图数据中功能依赖约束的建议。鉴于文献中出现的图依赖关系的广泛范围，本文的第一个贡献是对现有提案中的图功能依赖关系进行调查，突出了最重要的，它们的差异，以及它们的相对表达性。在第二个贡献中，我们通过提出映射来将文献中的不同类型的图功能依赖转换为符合pg - schema的依赖，从而与图数据库社区的目标保持一致。这些映射是在一个公开可用的工具PG-FD中实现的，据我们所知，这是第一个能够将Graph Dependencies转换为PG-Schema标准，同时完全保留其语义的解决方案。

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引用次数: 0