Integrating vertical and horizontal partitioning into automated physical database design

By Sanjay Agrawal, Vivek R. Narasayya, Beverly Yang. 2004

459 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Signal Processing

Citations in the corpus

Split learning for health: Distributed deep learning without sharing raw patient data (2018, background): “That said, with regards to non-neural network based federated learning techniques, the work in [27] shows their applicability to vertically partitioned distributed data [33, 34, 26, 35] shows applicability to multi-task learning in distributed settings.”
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].”
Database tuning advisor for microsoft SQL server 2005: demo (2005, background): “Column Group Restriction, Candidate Selection, Merging, and Enumeration (Figure 1) are individual steps in the search algorithm that allow DTA to search the space effectively [2].”
TrustedDB: A Trusted Hardware-Based Database with Privacy and Data Confidentiality (2011, methodology): “A common metric utilized in comparing query plan costs has been disk I/O [38], [36] which is justified since disk access is the most expensive operation and should be minimized.”
Self-Tuning Database Systems: A Decade of Progress (2007, background): “The same intuition was subsequently extended in [10] to prune out a large number of column sets.”
Skew-aware automatic database partitioning in shared-nothing, parallel OLTP systems (2012, methodology): “These approaches are based on the notion of data declustering [28], where the goal is to spread data across nodes to maximize intra-query parallelism [5, 10, 39, 49].”
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning (2019, methodology): “Major database vendors offer tools for automating database physical design [6, 9, 53, 57], and they focused on index optimizations, materialized views and partitions [1–3, 8, 23, 28, 36].”
Schism (2010, background): “Although a number of automatic partitioning schemes have been investigated [1, 23, 18], the mostly widely used approaches are round-robin (send each successive tuple to a different partition), range (divide up tuples according to a set of predicates), or hashpartitioning (assign tuples to partitions”
Heterogeneity-aware Distributed Parameter Servers (2017, background): “The problem of data partitioning across a set of nodes has been extensively studied by the database community [6, 15, 36].”