In recent years, digital twins have been proposed and implemented in various
fields with potential applications ranging from prototyping to maintenance.
Going forward, they are to enable numerous efficient and sustainable
technologies, among them autonomous cars. However, despite a large body of
research in many fields, academics have yet to agree on what exactly a digital
twin is -- and as a result, what its capabilities and limitations might be. To
further our understanding, we explore the capabilities of digital twins
concerning diagnosis in the field of transportation. We conduct a systematic
mapping study including digital twins of vehicles and their components, as well
as transportation infrastructure. We discovered that few papers on digital
twins describe any diagnostic process. Furthermore, most existing approaches
appear limited to system monitoring or fault detection. These findings suggest
that we need more research for diagnostic reasoning utilizing digital twins.

数字孪生在交通领域的诊断能力有限，需要进一步研究以实现诊断推理。

交通诊断数字孪生系统的系统性映射研究

A Systematic Mapping Study of Digital Twins for Diagnosis in  Transportation

Logs play a crucial role in system monitoring and debugging by recording
valuable system information, including events and states. Although various
methods have been proposed to detect anomalies in log sequences, they often
overlook the significance of considering relations among system components,
such as services and users, which can be identified from log contents.
Understanding these relations is vital for detecting anomalies and their
underlying causes. To address this issue, we introduce GLAD, a Graph-based Log
Anomaly Detection framework designed to detect relational anomalies in system
logs. GLAD incorporates log semantics, relational patterns, and sequential
patterns into a unified framework for anomaly detection. Specifically, GLAD
first introduces a field extraction module that utilizes prompt-based few-shot
learning to identify essential fields from log contents. Then GLAD constructs
dynamic log graphs for sliding windows by interconnecting extracted fields and
log events parsed from the log parser. These graphs represent events and fields
as nodes and their relations as edges. Subsequently, GLAD utilizes a
temporal-attentive graph edge anomaly detection model for identifying anomalous
relations in these dynamic log graphs. This model employs a Graph Neural
Network (GNN)-based encoder enhanced with transformers to capture content,
structural and temporal features. We evaluate our proposed method on three
datasets, and the results demonstrate the effectiveness of GLAD in detecting
anomalies indicated by varying relational patterns.

GLAD 是一个基于图的日志异常检测框架，结合日志语义、关系模式和顺序模式，能够有效地检测系统日志中的异常关系模式。