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Tim Findling

dblp:415/9940 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—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 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 · 56% Query processing and optimization · 44%

Topics — the 1 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data models and query languages › SQL
window functions
0.312025
Democratize MATCH_RECOGNIZE! · Proc. VLDB Endow. 2025

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

transpilation · 0.9
YearPublicationVenuePosition
2025 Democratize MATCH_RECOGNIZE!
abstract
Row pattern matching in terms of the MATCH_RECOGNIZE clause is a powerful and relatively recent feature in SQL that allows users to define regular patterns over ordered rows in a table. As of today, few database systems offer support for match recognize , making it unaccessible to a wide range of users. We demonstrate the implementation of a transpiler that translates match recognize into a plain SQL query executable by any database system that supports window functions and recursive common table expressions—no changes to the underlying database systems are required. We evaluate the performance of this approach on the running example to show that the transpiler generates code competitive with contemporary database systems that implement row pattern matching natively. The on-site demonstration is based on DuckDB.
Louisa Lambrecht, Tim Findling, Samuel Heid, Marcel Knüdeler, Torsten Grust
Proc. VLDB Endow.2