TRACE：一种用于快速数据洞察的时间关系近似立方体引擎
TRACE: A Time-Relational Approximate Cubing Engine for Fast Data Insights
AI总结：
本文提出时间关系近似立方体引擎TRACE，通过物化立方体关键部分以低空间和计算成本支持用户定义指标下的交互式数据切片分析。
摘要：
大量数据问题可以被建模为识别由用户定义指标驱动的重要数据切片。本文提出了TRACE，一种时间关系近似立方体引擎，能够以较低的前期成本（在空间和计算方面）对此类切片进行交互式分析。它通过随时间物化立方体中最重要的部分，实现对一大类分析查询的交互式查询，例如：我业务的哪个部分收入增长最高（[子类别=运动器材，性别=女性]），哪些切片在每用户收入方面落后（[州=加利福尼亚，年龄=20-30]）。支持许多用户定义的指标，包括常见聚合如SUM、COUNT、DISTINCT COUNT以及更复杂的如AVERAGE。我们针对多种业务用例实现并部署了TRACE。
英文摘要：
A large class of data questions can be modeled as identifying important slices of data driven by user defined metrics. This paper presents TRACE, a Time-Relational Approximate Cubing Engine that enables interactive analysis on such slices with a low upfront cost - both in space and computation. It does this by materializing the most important parts of the cube over time enabling interactive querying for a large class of analytical queries e.g. what part of my business has the highest revenue growth ([SubCategory=Sports Equipment, Gender=Female]), what slices are lagging in revenue per user ([State=CA, Age=20-30]). Many user defined metrics are supported including common aggregations such as SUM, COUNT, DISTINCT COUNT and more complex ones such as AVERAGE. We implemented and deployed TRACE for a variety of business use cases.
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。
