F-IVM: Analytics over Relational Databases under Updates

TL;DR

F-IVM introduces a unified, efficient approach for maintaining complex analytics over relational databases under updates, leveraging higher-order incremental view maintenance, factorized computation, and ring abstraction to handle diverse tasks.

Contribution

It presents a novel unified framework, F-IVM, that supports various analytics tasks over relational data with improved efficiency and versatility compared to traditional methods.

Findings

F-IVM outperforms classical view maintenance methods by orders of magnitude.

F-IVM reduces complex analytics maintenance to simple view hierarchies.

F-IVM is versatile across multiple analytical disciplines.

Abstract

This article describes F-IVM, a unified approach for maintaining analytics over changing relational data. We exemplify its versatility in four disciplines: processing queries with group-by aggregates and joins; learning linear regression models using the covariance matrix of the input features; building Chow-Liu trees using pairwise mutual information of the input features; and matrix chain multiplication. F-IVM has three main ingredients: higher-order incremental view maintenance; factorized computation; and ring abstraction. F-IVM reduces the maintenance of a task to that of a hierarchy of simple views. Such views are functions mapping keys, which are tuples of input values, to payloads, which are elements from a ring. F-IVM also supports efficient factorized computation over keys, payloads, and updates. Finally, F-IVM treats uniformly seemingly disparate tasks. In the key space, all tasks require joins and variable marginalization. In the payload space, tasks differ in the definition of the sum and product ring operations. We implemented F-IVM on top of DBToaster and show that it can outperform classical first-order and fully recursive higher-order incremental view maintenance by orders of magnitude while using less memory.