دورية أكاديمية
Automating Data Science (Dagstuhl Seminar 18401)
| العنوان: | Automating Data Science (Dagstuhl Seminar 18401) |
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| المؤلفون: | De Bie, Tijl, De Raedt, Luc, Hoos, Holger H., Smyth, Padhraic |
| المساهمون: | Tijl De Bie and Luc De Raedt and Holger H. Hoos and Padhraic Smyth |
| بيانات النشر: | Schloss Dagstuhl – Leibniz-Zentrum für Informatik |
| سنة النشر: | 2019 |
| المجموعة: | DROPS - Dagstuhl Research Online Publication Server (Schloss Dagstuhl - Leibniz Center for Informatics ) |
| مصطلحات موضوعية: | artificial intelligence, automated machine learning, automated scientific discovery, data science, inductive programming |
| الوصف: | 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. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| العلاقة: | Is Part Of Dagstuhl Reports, Volume 8, Issue 9 (2019); urn:nbn:de:0030-drops-103443; https://drops.dagstuhl.de/entities/document/10.4230/DagRep.8.9.154Test |
| DOI: | 10.4230/DagRep.8.9.154 |
| الإتاحة: | https://doi.org/10.4230/DagRep.8.9.154Test https://nbn-resolving.org/urn:nbn:de:0030-drops-103443Test |
| حقوق: | https://creativecommons.org/licenses/by/3.0/legalcodeTest |
| رقم الانضمام: | edsbas.3F4573E8 |
| قاعدة البيانات: | BASE |
| DOI: | 10.4230/DagRep.8.9.154 |
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