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pro vyhledávání: '"MILO, TOVA"'
Data exploration is a challenging process in which users examine a dataset by iteratively employing a series of queries. While in some cases the user explores a new dataset to become familiar with it, more often, the exploration process is conducted
Externí odkaz:
http://arxiv.org/abs/2406.05107
In data exploration, executing complex non-aggregate queries over large databases can be time-consuming. Our paper introduces a novel approach to address this challenge, focusing on finding an optimized subset of data, referred to as the approximatio
Externí odkaz:
http://arxiv.org/abs/2401.17059
When exploring a new dataset, Data Scientists often apply analysis queries, look for insights in the resulting dataframe, and repeat to apply further queries. We propose in this paper a novel solution that assists data scientists in this laborious pr
Externí odkaz:
http://arxiv.org/abs/2209.06260
We present a framework for creating small, informative sub-tables of large data tables to facilitate the first step of data science: data exploration. Given a large data table table T, the goal is to create a sub-table of small, fixed dimensions, by
Externí odkaz:
http://arxiv.org/abs/2203.02754
Publikováno v:
2019 IEEE 35th International Conference on Data Engineering (ICDE), Macao, Macao, 2019, pp. 1988 -- 1991
The interpretability of complex Machine Learning models is coming to be a critical social concern, as they are increasingly used in human-related decision-making processes such as resume filtering or loan applications. Individuals receiving an undesi
Externí odkaz:
http://arxiv.org/abs/2007.04453
Akademický článek
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Akademický článek
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Designing a reliable natural language (NL) interface for querying tables has been a longtime goal of researchers in both the data management and natural language processing (NLP) communities. Such an interface receives as input an NL question, transl
Externí odkaz:
http://arxiv.org/abs/1808.04614
Autor:
Abiteboul, Serge, Arenas, Marcelo, Barceló, Pablo, Bienvenu, Meghyn, Calvanese, Diego, David, Claire, Hull, Richard, Hüllermeier, Eyke, Kimelfeld, Benny, Libkin, Leonid, Martens, Wim, Milo, Tova, Murlak, Filip, Neven, Frank, Ortiz, Magdalena, Schwentick, Thomas, Stoyanovich, Julia, Su, Jianwen, Suciu, Dan, Vianu, Victor, Yi, Ke
In April 2016, a community of researchers working in the area of Principles of Data Management (PDM) joined in a workshop at the Dagstuhl Castle in Germany. The workshop was organized jointly by the Executive Committee of the ACM Symposium on Princip
Externí odkaz:
http://arxiv.org/abs/1701.09007
Many practical scenarios make it necessary to evaluate top-k queries over data items with partially unknown values. This paper considers a setting where the values are taken from a numerical domain, and where some partial order constraints are given
Externí odkaz:
http://arxiv.org/abs/1701.02634