In this article we propose a data model and query language for the visualisation and exploration of topic evolution networks representing the research progress in scientific document archives. Our model is independent of a particular topic extraction and alignment method and proposes a set of semantic and structural metrics for characterizing and filtering meaningful topic evolution patterns. These metrics are particularly useful for the visualization and the exploration of large topic evolution graphs. We also present a first implementation of our model on top of Apache Spark and experimental results obtained for four real-world document archives.

中文翻译：

---

用于探索科学发展的分析图数据模型和查询语言

在本文中，我们提出了一种数据模型和查询语言，用于对代表科学文献档案研究进展的主题演化网络进行可视化和探索。我们的模型独立于特定的主题提取和对齐方法，并提出了一组语义和结构度量，用于表征和过滤有意义的主题演化模式。这些指标对于大型主题演化图的可视化和探索特别有用。我们还在 Apache Spark 之上展示了我们模型的第一个实现，以及为四个真实世界的文档档案获得的实验结果。