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

Marco Fiscato

dblp:28/7636 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2016
—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
Data stream processing · 67% Distributed and cloud data management · 33%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Cloud and datacenter computing · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Distributed and cloud data management › federated database
federated query processing
0.212016
THEMIS: Fairness in Federated Stream Processing under Overload · SIGMOD Conference 2016
Data stream processing
load shedding
0.212016
THEMIS: Fairness in Federated Stream Processing under Overload · SIGMOD Conference 2016
Cloud and datacenter computing › cloud deployment
multi-cloud deployment
0.112016
THEMIS: Fairness in Federated Stream Processing under Overload · SIGMOD Conference 2016

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

query fragmentation · 0.5
YearPublicationVenuePosition
2016 THEMIS: Fairness in Federated Stream Processing under Overload
abstract
Federated stream processing systems, which utilise nodes from multiple independent domains, can be found increasingly in multi-provider cloud deployments, internet-of-things systems, collaborative sensing applications and large-scale grid systems. To pool resources from several sites and take advantage of local processing, submitted queries are split into query fragments, which are executed collaboratively by different sites. When supporting many concurrent users, however, queries may exhaust available processing resources, thus requiring constant load shedding. Given that individual sites have autonomy over how they allocate query fragments on their nodes, it is an open challenge how to ensure global fairness on processing quality experienced by queries in a federated scenario.
Evangelia Kalyvianaki, Marco Fiscato, Theodoros Salonidis, Peter R. Pietzuch
SIGMOD Conference2