A Structured Review of Data Management Technology for Interactive Visualization and Analysis

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.