Connecting the dots in visual analysis
Connecting the dots in visual analysis
复制标题
连接视觉分析中的点
DOI：
10.1109/vast.2009.5333023
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Jie Lu
中科院分区：
文献类型：
--
作者：
Y. Shrinivasan;D. Gotz;Jie Lu
在可视化分析过程中，用户必须经常将在不同时间点发现的见解联系起来。这个过程通常被称为“连接点”。当分析师在多个会话中交互式探索复杂数据集时，他们可能会发现大量发现。因此，他们往往很难回忆起与他们目前的调查最相关的过去的见解、观点和概念。在协作分析任务中，这一挑战更加困难，因为他们需要找到自己的发现与他人发现的见解之间的联系。在本文中，我们描述了一个基于上下文的检索算法，以确定笔记，视图和概念，从用户的过去的分析是最相关的一个视图或一个笔记的基础上，他们的查询线。然后，我们描述了一个相关的笔记推荐功能，表面最相关的项目，因为他们的工作基于此算法的用户。我们已经实现了这种推荐功能的收获，基于Web的视觉分析系统。我们通过案例研究评估HARVEST的相关笔记推荐功能，并讨论我们方法的影响。
During visual analysis, users must often connect insights discovered at various points of time. This process is often called “connecting the dots.” When analysts interactively explore complex datasets over multiple sessions, they may uncover a large number of findings. As a result, it is often difficult for them to recall the past insights, views and concepts that are most relevant to their current line of inquiry. This challenge is even more difficult during collaborative analysis tasks where they need to find connections between their own discoveries and insights found by others. In this paper, we describe a context-based retrieval algorithm to identify notes, views and concepts from users' past analyses that are most relevant to a view or a note based on their line of inquiry. We then describe a related notes recommendation feature that surfaces the most relevant items to the user as they work based on this algorithm. We have implemented this recommendation feature in HARVEST, a web based visual analytic system. We evaluate the related notes recommendation feature of HARVEST through a case study and discuss the implications of our approach.