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Semantic Flow Graph: A Framework for Discovering Object Relationships in Flow Fields

  • Jun Tao*
  • , Chaoli Wang
  • , Nitesh V. Chawla
  • , Lei Shi
  • , Seung Hyun Kim
  • *此作品的通讯作者
  • University of Notre Dame
  • CAS - Institute of Software
  • Ohio State University

科研成果: 期刊稿件文章同行评审

摘要

Visual exploration of flow fields is important for studying dynamic systems. We introduce semantic flow graph (SFG), a novel graph representation and interaction framework that enables users to explore the relationships among key objects (i.e., field lines, features, and spatiotemporal regions) of both steady and unsteady flow fields. The objects and their relationships are organized as a heterogeneous graph. We assign each object a set of attributes, based on which a semantic abstraction of the heterogeneous graph is generated. This semantic abstraction is SFG. We design a suite of operations to explore the underlying flow fields based on this graph representation and abstraction mechanism. Users can flexibly reconfigure SFG to examine the relationships among groups of objects at different abstraction levels. Three linked views are developed to display SFG, its node split criteria and history, and the objects in the spatial volume. For simplicity, we introduce SFG construction and exploration for steady flow fields with critical points being the only features. Then we demonstrate that SFG can be naturally extended to deal with unsteady flow fields and multiple types of features. We experiment with multiple data sets and conduct an expert evaluation to demonstrate the effectiveness of our approach.

源语言英语
期刊论文编号8118164
页(从-至)3200-3213
页数14
期刊IEEE Transactions on Visualization and Computer Graphics
24
12
DOI
出版状态已出版 - 1 12月 2018
已对外发布

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