HorseIR: bringing array programming languages together with database query processing

2018 ◽  
Author(s):  
Hanfeng Chen ◽  
Joseph Vinish D'silva ◽  
Hongji Chen ◽  
Bettina Kemme ◽  
Laurie Hendren
Keyword(s):  
Query Processing ◽  
Programming Languages ◽  
Database Query ◽  
Database Query Processing

HorseIR: bringing array programming languages together with database query processing

2020 ◽  
Vol 53 (8) ◽  
pp. 37-49
Author(s):  
Hanfeng Chen ◽  
Joseph Vinish D'silva ◽  
Hongji Chen ◽  
Bettina Kemme ◽  
Laurie Hendren
Keyword(s):  
Query Processing ◽  
Programming Languages ◽  
Database Query ◽  
Database Query Processing

Technical Perspective

2021 ◽  
Vol 50 (1) ◽  
pp. 59-59
Author(s):  
Marcin Zukowski
Keyword(s):  
Query Processing ◽  
Hash Tables ◽  
Database Query ◽  
Query Performance ◽  
Research Opportunities ◽  
Database Query Processing

Hash tables are possibly the single most researched element of the database query processing layers. There are many good reasons for that. They are critical for some key operations like joins and aggregation, and as such are one of the largest contributors to the overall query performance. Their efficiency is heavily impacted by variations of workloads, hardware and implementation, leading to many research opportunities. At the same time, they are sufficiently small and local in scope, allowing a starting researcher, or even a student, to understand them and contribute novel ideas. And benchmark them. . . Oh, the benchmarks. . . :)

Research on Query Optimization Technology in Distributed Database

2012 ◽  
Vol 433-440 ◽  
pp. 3335-3339
Author(s):  
Bo Zhu Wu
Keyword(s):  
Query Processing ◽  
Query Optimization ◽  
Distributed Database ◽  
Query Time ◽  
Processing Technology ◽  
Database Query ◽  
Processing Program ◽  
Depth Study ◽  
Database Query Processing ◽  
Query Efficiency

Through the in-depth study of the existing distributed database query processing technology, this paper proposes a distributed database query processing program. This program optimizes the existing query processing, stores the commonly used query results according to the query frequency, to be directly used by the subsequent queries or used as intermediate query results, thus avoiding possible transmission of a large number of data, thereby reducing the query time and improving query efficiency.

scholarly journals Turbocharging database query processing and testing

2019 ◽  
Vol 62 (11) ◽  
pp. 48-49
Author(s):  
Jayant R. Haritsa ◽  
S. Sudarshan
Keyword(s):  
Query Processing ◽  
Database Query ◽  
Database Query Processing

scholarly journals Database Query Processing Using Finite Cursor Machines

2008 ◽  
Vol 44 (4) ◽  
pp. 533-560 ◽  
Author(s):  
Martin Grohe ◽  
Yuri Gurevich ◽  
Dirk Leinders ◽  
Nicole Schweikardt ◽  
Jerzy Tyszkiewicz ◽  
...  
Keyword(s):  
Query Processing ◽  
Database Query ◽  
Database Query Processing

An MPSoC for energy-efficient database query processing

2016 ◽  
Author(s):  
Sebastian Haas ◽  
Oliver Arnold ◽  
Benedikt Nöthen ◽  
Stefan Scholze ◽  
Georg Ellguth ◽  
...  
Keyword(s):  
Query Processing ◽  
Energy Efficient ◽  
Database Query ◽  
Database Query Processing

Heterogeneous placement optimization for database query processing

2017 ◽  
Vol 59 (3) ◽  
Author(s):  
Tomas Karnagel ◽  
Dirk Habich
Keyword(s):  
Query Processing ◽  
Database Systems ◽  
Improve Performance ◽  
Database Query ◽  
Placement Decisions ◽  
Placement Optimization ◽  
Database Query Processing ◽  
The Given

AbstractComputing hardware is constantly evolving and database systems need to adapt to ongoing hardware changes to improve performance. The current hardware trend is heterogeneity, where multiple computing units like CPUs and GPUs are used together in one system. In this paper, we summarize our efforts to use hardware heterogeneity efficiently for query processing. We discuss different approaches of execution and investigate heterogeneous placement in detail by showing, how to automatically determine operator placement decisions according to the given hardware environment and query properties.

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