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 VLDB 2026 / reviewers in the wild / expert

Savva Khalaman

dblp:117/4310 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 2012
—ORCID · none

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

Databases, data management, data science and information retrieval · 1 · 1 first-author

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
Information retrieval · 92% Data mining · 8%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval
distributed information retrieval		 0.112012
Utilizing inter-document similarities in federated search · SIGIR 2012
Information retrieval › distributed information retrieval
federated search		 0.112012
Utilizing inter-document similarities in federated search · SIGIR 2012
Information retrieval › distributed information retrieval
result merging		 0.112012
Utilizing inter-document similarities in federated search · SIGIR 2012
Data mining › clustering
document clustering		 0.012012
Utilizing inter-document similarities in federated search · SIGIR 2012
Information retrieval › similarity measure
inter-document similarity		 0.012012
Utilizing inter-document similarities in federated search · SIGIR 2012

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

cluster-based merging · 0.1
YearPublicationVenuePosition
2012 Utilizing inter-document similarities in federated search
abstract
We demonstrate the merits of using inter-document similarities for federated search. Specifically, we study a results merging method that utilizes information induced from clusters of similar documents created across the lists retrieved from the collections. The method significantly outperforms state-of-the-art results merging approaches.
Savva Khalaman, Oren Kurland
 SIGIR1