Object-Centric Event Log (OCEL) of the Enron Email Dataset

Description: This dataset provides an object-centric event log (OCEL) representation of the publicly available Enron email corpus. The OCEL format allows for a richer analysis of interconnected processes and objects, making it particularly suitable for advanced process mining techniques, communication pattern analysis, and social network exploration. The event logs were generated from a pre-processed CSV version of the Enron emails using a custom Python script leveraging the PM4Py library. The script parses individual emails to extract key information, including: Timestamps: Derived from the 'Date' field of emails, parsed into timezone-aware datetime objects. Activities: Inferred from email subject prefixes (e.g., "Re:" becomes "Response", "Fw:" becomes "Forwarding", "Invitation:" becomes "Invitation"). Emails without recognized prefixes are assigned a "Default" activity. Objects: Two primary object types are identified: EMAILADDRESS: Extracted from 'From', 'To', and 'Cc' fields. MESSAGEID: Extracted from 'Message-ID', 'In-Reply-To', and 'References' fields, prefixed with "MID_" in the OCEL to ensure unique object identifiers across types. Attributes: Event attributes include the original cleaned subject and content of the email. Relationships: Events (emails) are linked to EMAILADDRESS objects with qualifiers 'FROM', 'TO', or 'CC'. Events are linked to MESSAGEID objects with qualifiers 'MESSAGEID' (for the email's own ID), 'INREPLYTO', or 'REFERENCES' to trace conversational threads. To accommodate various analytical needs and computational resources, the dataset is provided in three distinct checkpoints: Top 10,000 Emails: An OCEL generated from the first 10,000 emails processed. Top 100,000 Emails: An OCEL generated from the first 100,000 emails processed. All Emails: An OCEL generated from all emails processed by the script from the input emails.csv file. Each checkpoint is available in the .jsonocel format (OCEL 2.0 standard), ready for use with PM4Py and other OCEL-compatible process mining tools. This dataset can be valuable for researchers and practitioners seeking to apply object-centric process discovery, conformance checking, and enhancement techniques to a large, real-world communication log. Keywords: Object-Centric Event Log, OCEL, Process Mining, Enron Dataset, Email Analysis, Communication Networks, Social Network Analysis, PM4Py本数据集以面向对象事件日志（object-centric event log, OCEL）的格式，呈现了公开可用的安然邮件语料库。OCEL格式可对互联的流程与对象开展更具深度的分析，使其尤其适用于高级流程挖掘技术、通信模式分析与社交网络探索场景。 本事件日志基于预处理后的安然邮件CSV版本，通过自定义Python脚本结合PM4Py库生成。该脚本会解析单封邮件以提取关键信息，具体包括： 时间戳：源自邮件的"Date"字段，经解析为时区感知的datetime对象。 活动类型：根据邮件主题前缀推断得到（例如，"Re:"对应"回复"，"Fw:"对应"转发"，"Invitation:"对应"邀请"）。未识别到有效前缀的邮件将被分配"默认"活动类型。 对象：共识别出两类核心对象类型： 1. EMAILADDRESS：从"From""To"及"Cc"字段中提取得到。 2. MESSAGEID：从"Message-ID""In-Reply-To"及"References"字段中提取得到，为确保不同类型对象的标识符唯一，在OCEL中为其添加"MID_"前缀。 事件属性：包含邮件经清洗后的原始主题与内容。 关联关系：事件（即邮件）与EMAILADDRESS对象通过限定符"FROM""TO"或"CC"建立关联；事件与MESSAGEID对象则通过限定符"MESSAGEID"（对应邮件自身ID）、"INREPLYTO"或"REFERENCES"建立关联，以追踪对话线程脉络。 为适配多样化的分析需求与计算资源条件，本数据集提供三个不同的检查点版本： 1. 前10,000封邮件子集：基于处理的前10,000封邮件生成的OCEL。 2. 前100,000封邮件子集：基于处理的前100,000封邮件生成的OCEL。 3. 全量邮件子集：基于输入的emails.csv文件中所有邮件经脚本处理后生成的OCEL。 每个检查点子集均采用.jsonocel格式（遵循OCEL 2.0标准）提供，可直接用于PM4Py及其他支持OCEL的流程挖掘工具。本数据集可为希望将面向对象流程发现、一致性检验及增强技术应用于大规模真实通信日志的研究人员与从业者提供重要支撑。 关键词：面向对象事件日志，OCEL，流程挖掘，安然数据集，邮件分析，通信网络，社交网络分析，PM4Py