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Visual Analytics Solutions as 'off-the-shelf' Libraries
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (Skövde Artificial Intelligence Lab (SAIL))
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (Skövde Artificial Intelligence Lab (SAIL))ORCID-id: 0000-0003-2900-9335
2017 (engelsk)Inngår i: 2017 21st International Conference Information Visualisation (IV): Computer Graphics, Imaging and Visualisation. Biomedical Visualization, Visualisation on Built and Rural Environments & Geometric Modelling and Imaging, IEETeL2017 / [ed] Ebad Banissi, Mark W. McK. Bannatyne, Fatma Bouali, Nuno Miguel Soares Datia, Georges Grinstein, Dennis Groth, Weidong Huang, Malinka Ivanova, Sarah Kenderdine, Minoru Nakayama, Joao Moura Pires, Muhammad Sarfraz, Marco Temperini, Anna Ursyn, Gilles Venturini, Theodor G. Wyeld, Jian J. Zhang, IEEE Computer Society, 2017, s. 281-287Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Visual Analytics has brought forward many solutions to different tasks such as exploring topics, understanding user and customer behavior, comparing genomes, or detecting anomalies. Many of these solutions, if not most, are standalone applications with technological contributions which cannot be easily taken for: reuse in other domains, further improvement, benchmarking, or integration and deployment alongside other solutions. The latter can prove specially helpful for exploratory data analysis. This often leads researchers to re-implement solutions and thus to a suboptimal use of skills and resources. This paper discusses further the lack of off-the-shelf libraries for Visual Analytics, and proposes the creation of pluggable libraries on top of existing technologies such as Spark and Zeppelin. We provide an illustrative example of a pluggable, Visual Analytics library using these technologies.

sted, utgiver, år, opplag, sider
IEEE Computer Society, 2017. s. 281-287
Serie
IEEE International Conference on Information Visualisation, ISSN 1550-6037, E-ISSN 2375-0138
Emneord [en]
visual analytics, machine learning, off-the-shelf libraries
HSV kategori
Forskningsprogram
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifikatorer
URN: urn:nbn:se:his:diva-14682DOI: 10.1109/iV.2017.77ISI: 000419271000044Scopus ID: 2-s2.0-85040618455ISBN: 978-1-5386-0832-6 (tryckt)ISBN: 978-1-5386-0831-9 (digital)OAI: oai:DiVA.org:his-14682DiVA, id: diva2:1177340
Konferanse
2017 21st International Conference Information Visualisation (IV), London, United Kingdom, July 11-14, 2017
Prosjekter
NOVA (20140294), Swedish Knowledge Foundation
Forskningsfinansiär
Knowledge FoundationTilgjengelig fra: 2018-01-25 Laget: 2018-01-25 Sist oppdatert: 2018-07-31bibliografisk kontrollert

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