EDBT 2026 Demo / reviewers in the wild / expert
Joshua Shinavier
dblp:27/3988
· DBLP profile ↗
3ranked-venue papers
0as first author
2since 2021 · last 2025
0000-0002-2250-3245ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 3 · 2 since 2021
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
2 papers |
Query processing and optimization · 56% Database theory · 44% |
Topics — the 2 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Database theory
query complexity |
0.6 | 1 | 2022 | Threshold Queries in Theory and in the Wild · Proc. VLDB Endow. 2022 |
Query processing and optimization
top-k query processing |
0.4 | 2 | 2025 | Threshold queries in theory and in the wild · VLDB J. 2025 Threshold Queries in Theory and in the Wild · Proc. VLDB Endow. 2022 |
Methods — techniques the papers use, named apart from their topics
empirical query log analysis · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Threshold queries in theory and in the wildabstractThreshold queries are an important class of queries that only require computing or counting answers up to a specified threshold value. To the best of our knowledge, threshold queries have been largely disregarded in the research literature, which is surprising considering how common they are in practice. In this paper, we present a deep theoretical analysis of threshold query evaluation and show that thresholds can be used to significantly improve the asymptotic bounds of state-of-the-art query evaluation algorithms. We also empirically show that threshold queries are significant in practice. In surprising contrast to conventional wisdom, we found important scenarios in real-world data sets in which users are interested in computing the results of queries up to a certain threshold, independent of a ranking function that orders the query results. Angela Bonifati, Stefania Dumbrava, George Fletcher 0001, Jan Hidders, Matthias F. J. Hofer, Wim Martens, Filip Murlak, Joshua Shinavier, Slawomir Staworko, Dominik Tomaszuk |
VLDB J. | 8 |
| 2022 | Threshold Queries in Theory and in the WildabstractThreshold queries are an important class of queries that only require computing or counting answers up to a specified threshold value. To the best of our knowledge, threshold queries have been largely disregarded in the research literature, which is surprising considering how common they are in practice. In this paper, we present a deep theoretical analysis of threshold query evaluation and show that thresholds can be used to significantly improve the asymptotic bounds of state-of-the-art query evaluation algorithms. We also empirically show that threshold queries are significant in practice. In surprising contrast to conventional wisdom, we found important scenarios in real-world data sets in which users are interested in computing the results of queries up to a certain threshold, independent of a ranking function that orders the query results. Angela Bonifati, Stefania Dumbrava, George Fletcher 0001, Jan Hidders, Matthias F. J. Hofer, Wim Martens, Filip Murlak, Joshua Shinavier, Slawomir Staworko, Dominik Tomaszuk |
Proc. VLDB Endow. | 8 |
| 2010 | SameAs Networks and Beyond: Analyzing Deployment Status and Implications of owl: sameAs in Linked Data
Li Ding 0001, Joshua Shinavier, Zhenning Shangguan, Deborah L. McGuinness |
ISWC (1) | 2 |