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Gustavo Mezerhane

dblp:219/9707 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Database system architecture and tuning · 87% Query processing and optimization · 13%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Database system architecture and tuning
self-managing database systems		 0.312018
Query-based Workload Forecasting for Self-Driving Database Management Systems · SIGMOD Conference 2018
Database system architecture and tuning
workload forecasting		 0.312018
Query-based Workload Forecasting for Self-Driving Database Management Systems · SIGMOD Conference 2018

Methods — techniques the papers use, named apart from their topics

machine learning · 0.3
YearPublicationVenuePosition
2018 Query-based Workload Forecasting for Self-Driving Database Management Systems
abstract
The first step towards an autonomous database management system (DBMS) is the ability to model the target application's workload. This is necessary to allow the system to anticipate future workload needs and select the proper optimizations in a timely manner. Previous forecasting techniques model the resource utilization of the queries. Such metrics, however, change whenever the physical design of the database and the hardware resources change, thereby rendering previous forecasting models useless.
Lin Ma 0006, Dana Van Aken, Ahmed Hefny, Gustavo Mezerhane, Andrew Pavlo, Geoffrey J. Gordon
 SIGMOD Conference4