Automated Selection of Materialized Views and Indexes in SQL Databases

By Sanjay Agrawal, Surajit Chaudhuri, Vivek R. Narasayya. Very Large Data Bases Conference, 2000

718 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/eab5b35c52177b4f20c80b346ac9b623264d910e

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Citations in the corpus

Processing Analytical Queries over Encrypted Data (2013, methodology): “Given a particular physical design, M ONOMI uses a planner to choose the best split client/server execution plan, when a new query is issued by the application, as explained in §6.”
Database tuning advisor for microsoft SQL server 2005: demo (2005, methodology): “The tool relies on interfaces provided by Microsoft SQL Server’s query optimizer [ 3 , 4]. First it needs to be able to simulate partitioned tables/indexes and materialized views (referred to as hypothetical indexes and materialized views) that do not exist in the current database.”
Self-Tuning Database Systems: A Decade of Progress (2007, background): “One key observation presented in [5] is”
From XML schema to relations: a cost-based approach to XML storage (2002, unknown): “, [1, 21]) in physical optimization for relational DBMSs.”
To Tune or not to Tune? A Lightweight Physical Design Alerter (2006, background): “As noted in previous work [2, 15], automating the physical design of a database is complex because (i) there is a combinatorial explosion of physical structures to consider, and (ii) these structures strongly interact with each other.”
Integrating vertical and horizontal partitioning into automated physical database design (2004, methodology): “As mentioned in introduction, we adopt and extend the architecture described in [2].”
Skew-aware automatic database partitioning in shared-nothing, parallel OLTP systems (2012, methodology): “Indeed, we initially developed an iterative greedy algorithm similar to the one proposed in [4], but found that it obtained poor results for these complex instances because it is unable to escape local minima.”
Automatic Database Management System Tuning Through Large-scale Machine Learning (2017, background): “Much of the previous work on automatic database tuning focused on choosing the best logical or physical design of a database [16, 65], such as selecting indexes [28, 17, 59], partitioning schemes [8, 38, 41, 19], or materialized views [7].”
MINT: Multi-Vector Search Index Tuning (2025, unknown): “An index can be viewed as a special case of a materialized view (MV) [88, 89].”