4 citations · 9 across the 5 of their papers we have counts for
9 papers
ImitAL: Learning Active Learning Strategies from Synthetic Data
Julius Gonsior, Maik Thiele, Wolfgang Lehner
One of the biggest challenges that complicates applied supervised machine learning is the need for huge amounts of labeled data. Active Learning (AL) is a well-known standard metho…
Accurate and Efficient Time Series Matching by Season- and Trend-aware Symbolic Approximation -- Extended Version Including Additional Evaluation and Proofs
Lars Kegel, Claudio Hartmann, Maik Thiele +1
Processing and analyzing time series data\-sets have become a central issue in many domains requiring data management systems to support time series as a native data type. A crucia…
MorphStore: Analytical Query Engine with a Holistic Compression-Enabled Processing Model
Patrick Damme, Annett Ungethüm, Johannes Pietrzyk +3
In this paper, we present MorphStore, an open-source in-memory columnar analytical query engine with a novel holistic compression-enabled processing model. Basically, compression u…
RETRO: Relation Retrofitting For In-Database Machine Learning on Textual Data
Michael Günther, Maik Thiele, Wolfgang Lehner
There are massive amounts of textual data residing in databases, valuable for many machine learning (ML) tasks. Since ML techniques depend on numerical input representations, word…
Conjunctive Queries with Theta Joins Under Updates
Muhammad Idris, Martín Ugarte, Stijn Vansummeren +2
Modern application domains such as Composite Event Recognition (CER) and real-time Analytics require the ability to dynamically refresh query results under high update rates. Tradi…
Persistent Buffer Management with Optimistic Consistency
Lucas Lersch, Wolfgang Lehner, Ismail Oukid
Finding the best way to leverage non-volatile memory (NVM) on modern database systems is still an open problem. The answer is far from trivial since the clear boundary between memo…