Visual data exploration is ubiquitous in nearly every industry and organization to support discovering data-driven actionable insights. However, unlocking those insights requires analysts to manually construct a prohibitively large number of aggregate queries and visually explore their results, looking for those valuable and insightful visualizations. Such a challenge naturally motivated the development of novel solutions that automate the visual exploration process, and recommend to analysts those particular queries that best visualize their data and reveal interesting actionable insights. In such automated solutions, there is a clear need for providing analysts with a diversified and concise set of recommended visualizations, which cover and represent a large combinatorial high-dimensional space of possible visualizations. However, directly incorporating existing diversification methods leads to a “process-first-diversify-next” approach, in which all possible data visualizations are generated first through executing a large number of aggregate queries. To address this challenge and minimize the incurred query processing costs, in this work, we propose novel optimization techniques for the efficient diversification of recommended insightful visualizations. The key idea underlying our proposed techniques is to identify and eliminate the processing of a large number of low-utility insignificant visualizations. Meanwhile, for the potentially high-utility insightful visualizations, shared multi-query optimization techniques are proposed for further reduction in data processing cost. Our extensive experimental evaluation on real datasets demonstrates the performance gains provided by our proposed techniques, in terms of minimizing the query processing cost (i.e., efficiency), as well as maximizing the quality of recommendations (i.e., effectiveness).

中文翻译：

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山区光学遥感影像地形校正的系统评价


可视化数据探索几乎在每个行业和组织中无处不在，以支持发现数据驱动的可行见解。然而，解锁这些见解需要分析师手动构建大量聚合查询并直观地探索其结果，寻找那些有价值且富有洞察力的可视化。这样的挑战自然激发了新颖解决方案的开发，这些解决方案可以自动化视觉探索过程，并向分析师推荐那些最能可视化其数据并揭示有趣的可操作见解的特定查询。在此类自动化解决方案中，显然需要为分析师提供多样化且简洁的推荐可视化集，这些可视化集覆盖并表示可能的可视化的大型组合高维空间。然而，直接结合现有的多样化方法会导致“先处理，再多样化”的方法，其中所有可能的数据可视化首先通过执行大量聚合查询来生成。为了解决这一挑战并最大限度地减少产生的查询处理成本，在这项工作中，我们提出了新颖的优化技术，以实现推荐的有洞察力的可视化的有效多样化。我们提出的技术的关键思想是识别和消除大量低效、无关紧要的可视化的处理。同时，对于潜在的高实用性洞察可视化，提出了共享多查询优化技术以进一步降低数据处理成本。我们对真实数据集的广泛实验评估证明了我们提出的技术在最小化查询处理成本方面提供的性能增益（即、效率），以及最大化推荐的质量（即有效性）。