EDBT 2026 Demo / reviewers in the wild / expert
David Sauerwein
dblp:327/8089
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0009-0006-9191-8395ORCID · reported
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.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Storage systems · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Distributed and cloud data management · 50% Indexing and storage engines · 50% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Indexing and storage engines
columnar storage |
0.7 | 1 | 2023 | BtrBlocks: Efficient Columnar Compression for Data Lakes · Proc. ACM Manag. Data 2023 |
Distributed and cloud data management
data lake |
0.7 | 1 | 2023 | BtrBlocks: Efficient Columnar Compression for Data Lakes · Proc. ACM Manag. Data 2023 |
Storage systems › data management › database storage
columnar storage |
0.7 | 1 | 2023 | BtrBlocks: Efficient Columnar Compression for Data Lakes · Proc. ACM Manag. Data 2023 |
Storage systems
data compression |
0.7 | 1 | 2023 | BtrBlocks: Efficient Columnar Compression for Data Lakes · Proc. ACM Manag. Data 2023 |
Methods — techniques the papers use, named apart from their topics
lightweight encoding schemes · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | BtrBlocks: Efficient Columnar Compression for Data LakesabstractAnalytics is moving to the cloud and data is moving into data lakes. These reside on object storage services like S3 and enable seamless data sharing and system interoperability. To support this, many systems build on open storage formats like Apache Parquet. However, these formats are not optimized for remotely-accessed data lakes and today's high-throughput networks. Inefficient decompression makes scans CPU-bound and thus increases query time and cost. With this work we present BtrBlocks, an open columnar storage format designed for data lakes. BtrBlocks uses a set of lightweight encoding schemes, achieving fast and efficient decompression and high compression ratios. Maximilian Kuschewski, David Sauerwein, Adnan Alhomssi, Viktor Leis |
Proc. ACM Manag. Data | 2 |