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pro vyhledávání: '"Lehner, Wolfgang"'
The Single Instruction Multiple Data (SIMD) parallel paradigm is a well-established and heavily-used hardware-driven technique to increase the single-thread performance in different system domains such as database or machine learning. Depending on th
Externí odkaz:
http://arxiv.org/abs/2407.18728
Autor:
Gonsior, Julius, Falkenberg, Christian, Magino, Silvio, Reusch, Anja, Thiele, Maik, Lehner, Wolfgang
Despite achieving state-of-the-art results in nearly all Natural Language Processing applications, fine-tuning Transformer-based language models still requires a significant amount of labeled data to work. A well known technique to reduce the amount
Externí odkaz:
http://arxiv.org/abs/2210.03005
Active Learning (AL) is a well-known standard method for efficiently obtaining annotated data by first labeling the samples that contain the most information based on a query strategy. In the past, a large variety of such query strategies has been pr
Externí odkaz:
http://arxiv.org/abs/2208.11636
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 method for efficiently obtaining labeled data by first labeling the samples
Externí odkaz:
http://arxiv.org/abs/2108.07670
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 crucial prerequisite of these systems is time series matching, which still i
Externí odkaz:
http://arxiv.org/abs/2105.14867
Publikováno v:
Datenbank-Spektrum 22 (2022) 1-13
Cardinality estimation is a fundamental task in database query processing and optimization. As shown in recent papers, machine learning (ML)-based approaches can deliver more accurate cardinality estimations than traditional approaches. However, a lo
Externí odkaz:
http://arxiv.org/abs/2005.09367
Autor:
Damme, Patrick, Ungethüm, Annett, Pietrzyk, Johannes, Krause, Alexander, Habich, Dirk, Lehner, Wolfgang
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 using lightweight integer compression algorithms already plays an impor
Externí odkaz:
http://arxiv.org/abs/2004.09350
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 embeddings are increasingly utilized to convert symbolic representatio
Externí odkaz:
http://arxiv.org/abs/1911.12674
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. Traditional approaches to this problem are based either on the materializat
Externí odkaz:
http://arxiv.org/abs/1905.09848