2024/01/12 by Suharsh Sivakumar, Sivakumar, Suharsh, Jonathan Shen +3
Computer Science · #Advanced Database Systems and Queries #Data Management and Algorithms #Data Mining Algorithms and Applications #Databases (cs.DB) #FOS: Computer and information sciences #H.3.3 #Information Retrieval (cs.IR)
paper · pdf · doi:10.48550/arxiv.2401.06336
openalex publication_date 2024/01/12 · openalex created_date 2024/01/16 · openalex updated_date 2026/07/28
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.