用于建模用户对大型数据集探索的自适应基准
An Adaptive Benchmark for Modeling User Exploration of Large Datasets
AI总结：
提出可适配任意仪表板设计的 SIMBA 基准，通过有效交互序列生成目标 SQL 查询并检验查询等价性，以模拟用户探索并揭示现有方法遗漏的 DBMS 性能差距。
摘要：
本文提出一种新的 DBMS 性能基准，它能够利用由标准可视化和交互组件构成的任何指定仪表板设计来模拟用户探索。我们的 SImulation-BAsed（即 SIMBA）基准的显著特征是，能够将用户分析目标建模为一组 SQL 查询，这些查询通过有效的用户交互序列生成；同时，它还能通过测试用户先前查询与其目标查询之间的等价性，衡量分析目标的完成情况。通过这种方式，SIMBA 基准可以模拟分析师在探索会话开始时如何机会性地寻找有趣的洞察，并最终在接近尾声时聚焦于特定目标。为证明 SIMBA 基准的多功能性，我们使用它在六种不同仪表板规格下测试四个 DBMS 的性能，并将结果与 IDEBench 进行比较。我们的结果表明，目标驱动的模拟能够揭示现有基准测试方法以及一系列数据探索场景中遗漏的 DBMS 性能差距。
英文摘要：
In this paper, we present a new DBMS performance benchmark that can simulate user exploration with any specified dashboard design made of standard visualization and interaction components. The distinguishing feature of our SImulation-BAsed (or SIMBA) benchmark is its ability to model user analysis goals as a set of SQL queries to be generated through a valid sequence of user interactions, as well as measure the completion of analysis goals by testing for equivalence between the user's previous queries and their goal queries. In this way, the SIMBA benchmark can simulate how an analyst opportunistically searches for interesting insights at the beginning of an exploration session and eventually hones in on specific goals towards the end. To demonstrate the versatility of the SIMBA benchmark, we use it to test the performance of four DBMSs with six different dashboard specifications and compare our results with IDEBench. Our results show how goal-driven simulation can reveal gaps in DBMS performance missed by existing benchmarking methods and across a range of data exploration scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
