3 citations · 3 across the 2 of their papers we have counts for
4 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…
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…
A Cost-based Storage Format Selector for Materialization in Big Data Frameworks
Rana Faisal Munir, Alberto Abelló, Oscar Romero +2
Modern big data frameworks (such as Hadoop and Spark) allow multiple users to do large-scale analysis simultaneously. Typically, users deploy Data-Intensive Workflows (DIWs) for th…