A Structured Review of Data Management Technology for Interactive Visualization and Analysis
A Structured Review of Data Management Technology for Interactive Visualization and Analysis
复制标题
用于交互式可视化和分析的数据管理技术的结构化回顾
DOI：
10.1109/tvcg.2020.3028891
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发表时间：
2020-10
影响因子：
5.2
通讯作者：
L. Battle;C. Scheidegger
中科院分区：
文献类型：
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作者：
L. Battle;C. Scheidegger
在过去的二十年里，交互式可视化和分析已经成为数据驱动决策的核心工具。与数据可视化的贡献同时，数据管理的研究产生了直接有利于交互式分析的技术。在这里，我们贡献了30年的工作，在这个相邻的领域的系统回顾，并强调技术和原则，我们认为是低估了可视化工作。我们沿着沿着两个轴来构建我们的评论。首先，我们使用可视化文献中的任务分类来构建通常系统中的交互空间。其次，我们创建了一个数据管理工作的分类，在特殊性和一般性之间取得了平衡。具体而言，我们贡献了一个表征的131篇研究论文沿着这两个轴。我们发现，在数据管理场所的五个概念适合交互式可视化系统：物化视图，近似查询处理，用户建模和查询预测，多查询优化，沿袭技术和索引技术。此外，我们发现物化视图和近似查询处理的工作占优势，大多数针对我们使用的分类中的交互任务的有限子集。这表明在可视化和数据管理方面的未来研究的自然途径。我们的分类既改变了可视化研究人员设计和构建系统的方式，也突出了未来工作的必要性。
In the last two decades, interactive visualization and analysis have become a central tool in data-driven decision making. Concurrently to the contributions in data visualization, research in data management has produced technology that directly benefits interactive analysis. Here, we contribute a systematic review of 30 years of work in this adjacent field, and highlight techniques and principles we believe to be underappreciated in visualization work. We structure our review along two axes. First, we use task taxonomies from the visualization literature to structure the space of interactions in usual systems. Second, we created a categorization of data management work that strikes a balance between specificity and generality. Concretely, we contribute a characterization of 131 research papers along these two axes. We find that five notions in data management venues fit interactive visualization systems well: materialized views, approximate query processing, user modeling and query prediction, muiti-query optimization, lineage techniques, and indexing techniques. In addition, we find a preponderance of work in materialized views and approximate query processing, most targeting a limited subset of the interaction tasks in the taxonomy we used. This suggests natural avenues of future research both in visualization and data management. Our categorization both changes how we visualization researchers design and build our systems, and highlights where future work is necessary.