In this paper, we introduce the transition-based feature generator (TFGen) technique, which reads general activity data with attributes and generates step-by-step generated data. The activity data may consist of network activity from packets, system calls from processes or classified activity from surveillance cameras. TFGen processes data online and will generate data with encoded historical data for each incoming activity with high computational efficiency. The input activities may concurrently originate from distinct traces or channels. The technique aims to address issues such as domain-independent applicability, the ability to discover global process structures, the encoding of time-series data, and online processing capability.

翻译：本文提出过渡特征生成器（TFGen）技术，该技术能读取包含属性的通用活动数据并逐步生成数据。这类活动数据可涵盖网络数据包活动、进程系统调用或监控摄像机分类活动。TFGen采用在线处理方式，在保持高计算效率的同时，为每个输入活动生成包含历史数据编码的特征。输入活动可能来自不同轨迹或通道的并发数据源。该技术旨在解决领域无关适用性、全局流程结构发现、时序数据编码以及在线处理能力等关键问题。