Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from EDBT 2026 / reviewers in the wild / expert

Huma Zaidi

dblp:135/4660 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · unresolved

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.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Cloud and datacenter computing · 91% Performance modeling and evaluation · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cloud and datacenter computing › cluster resource management and scheduling
cluster resource management		 0.212013
Optimization Strategies for A/B Testing on HADOOP · Proc. VLDB Endow. 2013
Cloud and datacenter computing
job scheduling		 0.212013
Optimization Strategies for A/B Testing on HADOOP · Proc. VLDB Endow. 2013
Cloud and datacenter computing › cluster computing framework
mapreduce optimization		 0.212013
Optimization Strategies for A/B Testing on HADOOP · Proc. VLDB Endow. 2013
Performance modeling and evaluation
workload characterization		 0.012013
Optimization Strategies for A/B Testing on HADOOP · Proc. VLDB Endow. 2013

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

resource allocation · 0.2inter-query optimization · 0.2cluster load modeling · 0.2
YearPublicationVenuePosition
2013 Optimization Strategies for A/B Testing on HADOOP
abstract
In this work, we present a set of techniques that considerably improve the performance of executing concurrent MapReduce jobs. Our proposed solution relies on proper resource allocation for concurrent Hive jobs based on data dependency, inter-query optimization and modeling of Hadoop cluster load. To the best of our knowledge, this is the first work towards Hive/MapReduce job optimization which takes Hadoop cluster load into consideration. We perform an experimental study that demonstrates 233% reduction in execution time for concurrent vs sequential execution schema. We report up to 40% extra reduction in execution time for concurrent job execution after resource usage optimization. The results reported in this paper were obtained in a pilot project to assess the feasibility of migrating A/B testing from Teradata + SAS analytics infrastructure to Hadoop. This work was performed on eBay production Hadoop cluster.
Andrii Cherniak, Huma Zaidi, Vladimir Zadorozhny
 Proc. VLDB Endow.2