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Pascal Engel

dblp:200/6056 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · unresolved

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 integration and cleaning · 40% Query processing and optimization · 20% Data models and query languages · 20%

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

TopicWeightPapersLastEvidence papers
Database theory › semiring semantics
bag semantics
0.612022
How, Where, and Why Data Provenance Improves Query Debugging: A Visual Demonstration of Fine-Grained Provenance Analysis for SQL · ICDE 2022
Data integration and cleaning
data provenance
0.612022
How, Where, and Why Data Provenance Improves Query Debugging: A Visual Demonstration of Fine-Grained Provenance Analysis for SQL · ICDE 2022
Data integration and cleaning › data provenance
fine-grained provenance
0.612022
How, Where, and Why Data Provenance Improves Query Debugging: A Visual Demonstration of Fine-Grained Provenance Analysis for SQL · ICDE 2022
Query processing and optimization
query debugging
0.612022
How, Where, and Why Data Provenance Improves Query Debugging: A Visual Demonstration of Fine-Grained Provenance Analysis for SQL · ICDE 2022
Data models and query languages
SQL
0.612022
How, Where, and Why Data Provenance Improves Query Debugging: A Visual Demonstration of Fine-Grained Provenance Analysis for SQL · ICDE 2022

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

why-provenance · 0.6where-provenance · 0.6visual interactive analysis · 0.6how-provenance · 0.6
YearPublicationVenuePosition
2022 How, Where, and Why Data Provenance Improves Query Debugging: A Visual Demonstration of Fine-Grained Provenance Analysis for SQL
abstract
Data provenance is meta-information about the origin and processing history of data. We demonstrate the prove-nance analysis of SQL queries and use it for query debugging. How-provenance determines which query expressions have been relevant for evaluating selected pieces of output data. Likewise, Where- and Why-provenance determine relevant pieces of input data. The combined provenance notions can be explored visually and interactively. We support a feature-rich SQL dialect with correlated subqueries and focus on bag semantics. Our fine-grained provenance analysis derives individual data provenance for table cells and SQL expressions.
Tobias Müller 0006, Pascal Engel
ICDE2