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20182025
most citedRETRO: Relation Retrofitting For In-Database Machine Learning on Textual Data

3 citations · 3 across the 3 of their papers we have counts for

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cs.DB2021

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…

cs.DB2019★ 3 cited

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…