Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH scholarly article en De Bie, Tijl; De Raedt, Luc; Hoos, Holger H.; Smyth, Padhraic http://www.dagstuhl.de/dagrep License
when quoting this document, please refer to the following
DOI:
URN: urn:nbn:de:0030-drops-103443
URL:

; ; ;
Weitere Beteiligte (Hrsg. etc.): Tijl De Bie and Luc De Raedt and Holger H. Hoos and Padhraic Smyth

Automating Data Science (Dagstuhl Seminar 18401)

pdf-format:


Abstract

Data science is concerned with the extraction of knowledge and insight, and ultimately societal or economic value, from data. It complements traditional statistics in that its object is data as it presents itself in the wild (often complex and heterogeneous, noisy, loosely structured, biased, etc.), rather than well-structured data sampled in carefully designed studies. It also has a strong computer science focus, and is related to popular areas such as big data, machine learning, data mining and knowledge discovery. Data science is becoming increasingly important with the abundance of big data, while the number of skilled data scientists is lagging. This has raised the question as to whether it is possible to automate data science in several contexts. First, from an artificial intelligence perspective, it is interesting to investigate whether (data) science (or portions of it) can be automated, as it is an activity currently requiring high levels of human expertise. Second, the field of machine learning has a long-standing interest in applying machine learning at the meta-level, in order to obtain better machine learning algorithms, yielding recent successes in automated parameter tuning, algorithm configuration and algorithm selection. Third, there is an interest in automating not only the model building process itself (cf. the Automated Statistician) but also in automating the preprocessing steps (data wrangling). This Dagstuhl seminar brought together researchers from all areas concerned with data science in order to study whether, to what extent, and how data science can be automated.

BibTeX - Entry

@Article{debie_et_al:DR:2019:10344,
  author =	{Tijl De Bie and Luc De Raedt and Holger H. Hoos and Padhraic Smyth},
  title =	{{Automating Data Science  (Dagstuhl Seminar 18401)}},
  pages =	{154--181},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{8},
  number =	{9},
  editor =	{Tijl De Bie and Luc De Raedt and Holger H. Hoos and Padhraic Smyth},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2019/10344},
  URN =		{urn:nbn:de:0030-drops-103443},
  doi =		{10.4230/DagRep.8.9.154},
  annote =	{Keywords: artificial intelligence, automated machine learning, automated scientific discovery, data science, inductive programming}
}

Keywords: artificial intelligence, automated machine learning, automated scientific discovery, data science, inductive programming
Seminar: Dagstuhl Reports, Volume 8, Issue 9
Issue date: 2019
Date of publication: 2019


DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI