F-IVM: Analytics over Relational Databases under Updates
Ahmet Kara, Milos Nikolic, Dan Olteanu, Haozhe Zhang

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,…
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Taxonomy
TopicsData Mining Algorithms and Applications · Bayesian Modeling and Causal Inference · Advanced Database Systems and Queries
