论文标题
通过元组气泡大概查询处理
Approximate Query Processing via Tuple Bubbles
论文作者
论文摘要
我们提出了一种多功能方法，通过创建大量原始元素（即造成的气泡）的紧凑但精确的表示，来轻巧，近似查询处理。然后，查询处理没有使用元组工作表，而是在气泡上运行，但在概念上留下了传统的查询处理范式。我们认为，这是一种自然且可行的方法，可以在分解的云设置或资源有限的方案中呈现大量数据可行的查询处理。气泡对于封闭的元素的紧凑性以及统计的粒度及其实例化的粒度是可调的。为了提高准确性，我们提出了第一个工作解决方案，该解决方案代表通过贝叶斯网络，每张桌子或沿外国钥匙连接的气泡。为了支撑该方法的生存能力，我们在考虑最先进的竞争者的实验评估中报告了一个实验评估，在评估估计准确性，执行时间和所需磁盘空间时，我们会在其中显示出明显的好处。
We propose a versatile approach to lightweight, approximate query processing by creating compact but tunably precise representations of larger quantities of original tuples, coined bubbles. Instead of working with tables of tuples, the query processing then operates on bubbles but leaves the traditional query processing paradigms conceptually applicable. We believe this is a natural and viable approach to render approximate query processing feasible for large data in disaggregated cloud settings or in resource-limited scenarios. Bubbles are tunable regarding the compactness of enclosed tuples as well as the granularity of statistics and the way they are instantiated. For improved accuracy, we put forward a first working solution that represents bubbles via Bayesian networks, per table, or along foreign-key joins. To underpin the viability of the approach, we report on an experimental evaluation considering the state-of-the-art competitors, where we show clear benefits when assessing the estimation accuracy, execution time, and required disk space.
