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Dear Readers,
Welcome to the eighth regular issue in 2017 with one high quality
regular paper and five papers from the focused topic "Big data in
Cross-Disciplinary Research".
As always, I'd like to thank all institutions, reviewers and authors
for their valuable support and work. I'd particularly like to
acknowledge the generous support of the members of the J.UCS
consortium which enables us to continue to offer J.UCS as an open
content journal without publication fees.
In this regular issue, I am very pleased to introduce one accepted
paper in a research collaboration form two different countries, Sweden
and Republic of South Africa. Johanna Björklund, Loek Cleophas and
My Karlsson evaluated in their paper probabilistic lexicalized
tree-insertion grammars on a classification task relevant for
automatic speech recognition.
The editors of the focused topic, Giangiacomo Bravo from the Linnaeus University, Sweden, Mikko Laitinen from the Linnaeus University,
Sweden and the University of Eastern Finland and Magnus Levin, Welf
Löwe and Göran Petersson from the Linnaeus University, Sweden,
write:
"The ubiquity of sensor, computing, communication, and storage
technologies provides us with access to previously unknown amounts of
data - Big Data. Big Data has revolutionized research communities and
their scientific methodologies. It has, for instance, innovated the
approaches to knowledge and theory building, validation, and
exploitation taken in the engineering sciences. The humanities and
social sciences even face a paradigm shift away from data-scarce,
static, coarse-grained and simple studies towards data-rich, dynamic,
high resolution, and complex observations and simulations. The present
focused topic presents investigations from different research fields
in which the focus is either on utilizing Big Data or charting the
benefits of using such evidence in basic research.
The paper "Big Data in Cross-Disciplinary Research" by Giangiacomo
Bravo, Mikko Laitinen, Magnus Levin, Welf Löwe, and Göran
Petersson summarizes the observations of the guest editors of this
focused topic on how the use of Big Data in research has matured over
the last years.
The paper "Utilizing Multilingual Language Data in (Nearly) Real time:
the Case of the Nordic Tweet Stream" by Mikko Laitinen, Jonas
Lundberg, Magnus Levin, and Alexander Lakaw presents a digital
humanities project that downloads Twitter messages and two case
studies illustrating how this corpus could be used as empirical
evidence in studies focusing on the global spread of English.
The paper "Prospects and Challenges for the Computational Social
Sciences" by Giangiacomo Bravo and Mike Farjam illustrates the
developments of computational social sciences since the term was
coined in 2009 and (re-) assesses its potentialities and risks.
The paper "Digitalization Canvas - Towards Identifying Digitalization
Use Cases and Projects" by Andreas Heberle, Welf Löwe, Anders
Gustafsson, and Örjan Vorrei describes an industry project where
digitalization use cases have been identified, evaluated, and
prioritized with respect to benefits and costs leading to a portfolio
of projects, some with quick and easy wins and some others with mid-
to long-term benefits.
The paper "(Big) Data in Library and Information Science: A Brief
Overview of Some Important Problem Areas" by Koraljka Golub and
Joacim Hansson argues for the need to find new ways of organizing
documentation, information and data, and to develop instruments for
research evaluation in a world where the differences between formal
publications, other forms of communication and data sharing are
becoming weaker."
Enjoy reading!
Cordially,
Christian Gütl, Managing Editor
Graz University of Technology, Graz, Austria
Email: cguetl@iicm.edu
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