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pro vyhledávání: '"Lehner, Wolfgang"'
The results of the presented work show that machine learning (ML) can be used to support correct training logging in order to improve technical performance in trampoline gymnastics. They indicate considerable potential for expanding mobile applicatio
Query execution techniques in database systems constantly adapt to novel hardware features to achieve high query performance, in particular for analytical queries. In recent years, vectorization based on the Single Instruction Multiple Data parallel
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A91537
https://tud.qucosa.de/api/qucosa%3A91537/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A91537/attachment/ATT-0/
The task of the judge of difficulty in trampoline gymnastics is to check the elements and difficulty values entered on the competition cards and the difficulty of each element according to a numeric system. To do this, the judge must count all somers
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A89530
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https://tud.qucosa.de/api/qucosa%3A89530/attachment/ATT-0/
Cardinality estimation is a fundamental task in database query processing and optimization. As shown in recent papers, machine learning (ML)-based approaches may deliver more accurate cardinality estimations than traditional approaches. However, a lo
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A89177
https://tud.qucosa.de/api/qucosa%3A89177/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A89177/attachment/ATT-0/
The Single Instruction Multiple Data (SIMD) paradigm became a core principle for optimizing query processing in columnar database systems. Until now, only the LOAD/STORE instructions are considered to be efficient enough to achieve the expected speed
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A89288
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https://tud.qucosa.de/api/qucosa%3A89288/attachment/ATT-0/
Hybrid memory systems consisting of DRAM and NVRAM offer a great opportunity for column-oriented data systems to persistently store and to efficiently process columnar data completely in main memory. While vectorization (SIMD) of query operators is s
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A76671
https://tud.qucosa.de/api/qucosa%3A76671/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A76671/attachment/ATT-0/
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
Spreadsheets are very successful content generation tools, used in almost every enterprise to create a wealth of information. However, this information is often intermingled with various formatting, layout, and textual metadata, making it hard to ide
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A82970
https://tud.qucosa.de/api/qucosa%3A82970/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A82970/attachment/ATT-0/
This paper presents DECO (Dresden Enron COrpus), a dataset of spreadsheet files, annotated on the basis of layout and contents. It comprises of 1,165 files, extracted from the Enron corpus. Three different annotators (judges) assigned layout roles (e
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A82977
https://tud.qucosa.de/api/qucosa%3A82977/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A82977/attachment/ATT-0/
To efficiently support analytical applications from a data management perspective, in-memory column store database systems are state-of-the art. In this kind of database system, lossless lightweight integer compression schemes are crucial to keep the
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A82192
https://tud.qucosa.de/api/qucosa%3A82192/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A82192/attachment/ATT-0/