VLDB 2026 Research / reviewers in the wild / expert
Conrad Leonard
dblp:370/5263
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
0000-0002-4131-2065ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › sequence analysis › sequencing read preprocessing
duplicate marking |
0.7 | 1 | 2023 | streammd: fast low-memory duplicate marking using a Bloom filter · Bioinform. 2023 |
Bioinformatics and computational biology
sequence analysis |
0.7 | 1 | 2023 | streammd: fast low-memory duplicate marking using a Bloom filter · Bioinform. 2023 |
Bioinformatics and computational biology › sequence analysis › sequencing data processing
read preprocessing |
0.2 | 1 | 2023 | streammd: fast low-memory duplicate marking using a Bloom filter · Bioinform. 2023 |
Methods — techniques the papers use, named apart from their topics
bloom filter · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | streammd: fast low-memory duplicate marking using a Bloom filterabstractSUMMARY: Identification of duplicate templates is a common preprocessing step in bulk sequence analysis; for large libraries, this can be resource intensive. Here, we present streammd: a fast, memory-efficient, single-pass duplicate marker operating on the principle of a Bloom filter. streammd closely reproduces outputs from Picard MarkDuplicates while being substantially faster, and requires much less memory than SAMBLASTER. AVAILABILITY AND IMPLEMENTATION: streammd is a C++ program available from GitHub https://github.com/delocalizer/streammd under the MIT license. Conrad Leonard |
Bioinform. | 1 |