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Tobias Frey

dblp:135/4701 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

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

Security and privacy · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1

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
Database system architecture and tuning · 50% Indexing and storage engines · 50%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Cloud and datacenter computing · 100%

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

TopicWeightPapersLastEvidence papers
Indexing and storage engines
index construction		 0.212013
Mosquito: Another One Bites the Data Upload STream · Proc. VLDB Endow. 2013
Database system architecture and tuning › database design
physical database design		 0.212013
Mosquito: Another One Bites the Data Upload STream · Proc. VLDB Endow. 2013
Cloud and datacenter computing › cluster computing framework
hadoop		 0.212013
Mosquito: Another One Bites the Data Upload STream · Proc. VLDB Endow. 2013

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

adaptive indexing · 0.3
YearPublicationVenuePosition
2023 Multi-level fine-tuning, data augmentation, and few-shot learning for specialized cyber threat intelligence
Markus Bayer, Tobias Frey, Christian Reuter 0001
 Comput. Secur.2
2013 Mosquito: Another One Bites the Data Upload STream
abstract
Mosquito is a lightweight and adaptive physical design framework for Hadoop. Mosquito connects to existing data pipelines in Hadoop MapReduce and/or HDFS, observes the data, and creates better physical designs, i.e. indexes, as a byproduct. Our approach is minimally invasive, yet it allows users and developers to easily improve the runtime of Hadoop. We present three important use cases: first, how to create indexes as a byproduct of data uploads into HDFS; second, how to create indexes as a byproduct of map tasks; and third, how to execute map tasks as a byproduct of HDFS data uploads. These use cases may even be combined.
Stefan Richter 0007, Jens Dittrich, Stefan Schuh, Tobias Frey
 Proc. VLDB Endow.4