Daniel ten Wolde

dblp:351/9539 · DBLP profile ↗
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3ranked-venue papers
2as first author
3since 2021 · last 2025
0009-0008-8502-1148ORCID · reported

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

Databases, data management, data science and information retrieval · 3 · 2 first-author · 3 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
1 paper
Data models and query languages · 46% Graph data management · 46% Database system architecture and tuning · 7%

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

TopicWeightPapersLastEvidence papers
Graph data management
graph pattern matching
0.712023
DuckPGQ: Bringing SQL/PGQ to DuckDB · Proc. VLDB Endow. 2023
Data models and query languages
graph query language
0.712023
DuckPGQ: Bringing SQL/PGQ to DuckDB · Proc. VLDB Endow. 2023
Graph data management › graph algorithms
path finding
0.712023
DuckPGQ: Bringing SQL/PGQ to DuckDB · Proc. VLDB Endow. 2023
Data models and query languages › graph query language
SQL/PGQ
0.712023
DuckPGQ: Bringing SQL/PGQ to DuckDB · Proc. VLDB Endow. 2023
Database system architecture and tuning
extensibility
0.212023
DuckPGQ: Bringing SQL/PGQ to DuckDB · Proc. VLDB Endow. 2023

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

worst-case optimal join · 0.7factorized computation · 0.7
YearPublicationVenuePosition
2025 Adaptive Factorization Using Linear-Chained Hash Tables
Paul Gross 0002, Daniel ten Wolde, Peter Boncz
CIDR2
2023 DuckPGQ: Efficient Property Graph Queries in an analytical RDBMS
Daniel ten Wolde, Tavneet Singh, Gábor Szárnyas, Peter Boncz
CIDR1
2023 DuckPGQ: Bringing SQL/PGQ to DuckDB
abstract
We demonstrate the most important new feature of SQL:2023, namely SQL/PGQ, which eases querying graphs using SQL by introducing new syntax for pattern matching and (shortest) path-finding. We show how support for SQL/PGQ can be integrated into an RDBMS, specifically in the DuckDB system, using an extension module called DuckPGQ. As such, we also demonstrate the use of the DuckDB extensibility mechanism, which allows us to add new functions, data types, operators, optimizer rules, storage systems, and even parsers to DuckDB. We also describe the new data structures and algorithms that the DuckPGQ module is based on, and how they are injected into SQL plans. While the demonstrated DuckPGQ extension module is lean and efficient, we sketch a roadmap to (i) improve its performance through new algorithms (factorized and WCOJ) and better parallelism and (ii) extend its functionality to scenarios beyond SQL, e.g., building and analyzing Graph Neural Networks.
Daniel ten Wolde, Gábor Szárnyas, Peter Boncz
Proc. VLDB Endow.1