Exploratory search paradigm assists users who do not have a clear search intent and are unfamiliar with the underlying data space. Query formulation evolves iteratively in this paradigm as a user becomes more familiar with the content. Although exploratory search has received significant attention recently in the context of structured data, scant attention has been paid for graph-structured data. An early effort for building exploratory subgraph search framework on graph databases suffers from efficiency and scalability problems. In this paper, we present a visual exploratory subgraph search framework called ferrari, which embodies two novel index structures called vaccine and advise, to address these limitations. vaccine is an offline, feature-based index that stores rich information related to frequent and infrequent subgraphs in the underlying graph database, and how they can be transformed from one subgraph to another during visual query formulation. advise, on the other hand, is an adaptive, compact, on-the-fly index instantiated during iterative visual formulation/reformulation of a subgraph query for exploratory search and records relevant information to efficiently support its repeated evaluation. Extensive experiments and user study on real-world datasets demonstrate superiority of ferrari to a state-of-the-art visual exploratory subgraph search technique.

中文翻译：

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FERRARI：图形数据库中可视化探索子图搜索的有效框架

探索性搜索范例可为没有明确搜索意图并且不熟悉基础数据空间的用户提供帮助。随着用户对内容的熟悉，查询表述在这种范式中不断发展。尽管最近在结构化数据的背景下探索性搜索受到了极大的关注，但对图结构化数据却很少关注。在图数据库上建立探索性子图搜索框架的早期工作受到效率和可伸缩性问题的困扰。在本文中，我们提出了一个可视化的探索性子图搜索框架，称为法拉利，其中体现了两个新颖的索引结构，即疫苗和建议，以解决这些局限性。疫苗是一种基于特征的离线索引，可在基础图数据库中存储与频繁和不频繁的子图相关的丰富信息，以及在可视化查询制定过程中如何将它们从一个子图转换为另一个子图。提醒，在另一方面，是一种自适应，结构紧凑，在即时索引探索性搜索和记录相关信息的子图查询迭代视觉制剂/再形成过程中实例化以高效地支持它的重复评估。大量实验和对真实数据集的用户研究证明了法拉利的优越性 最新的视觉探索子图搜索技术。