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Call for Papers
Information Technology & People - Special Issue
"Perspectives on the values of Big Data sharing"
Call for Papers link:
http://www.emeraldgrouppublishing.com/products/journals/call_for_papers.htm?id=8391
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Special issue editors:
Christopher Tucci, EPFL CDM MTEI CSI ODY 1 04 (Odyssea) - Station
5 CH-1015 Lausanne - Switzerland,
email:
christopher.tucci@epfl.ch
Gianluigi Viscusi, EPFL CDM MTEI CSI ODY 1 04 (Odyssea) - Station
5 CH-1015 Lausanne - Switzerland,
email:
gianluigi.viscusi@epfl.ch
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Timeline for the special issue:
Deadline for the submission of papers: April 15th 2019
Reviews returned: June 15th 2019
Revised papers submitted: September 15th 2019
Final papers due: October 15th 2019
Special issue published: December 15th 2019
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Submission instructions
Please submit your manuscript via our review website:
http://mc.manuscriptcentral.com/itp
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Big Data has been first subject to industry hype (Davenport, Barth
and Bean, 2012) with a consequent growing interest by academics
(Buhl, Röglinger, Moser and Heidemann, 2013; Goes, 2014; Batini,
Rula, Scannapieco and Viscusi, 2015; Abbasi, Sarker and Chiang,
2016; Rai, 2016; Günther, Rezazade Mehrizi, Huysman and Feldberg,
2017). The current common understanding of big data can be
summarized by the following definition that appeared in 2013 in
the first issue of Big Data, one of the first journals on the
topic published by Mary Ann Liebert, Inc: "Big data is data that
exceeds the processing capacity of conventional database systems.
The data is too big, moves too fast, or doesn’t fit the structures
of your database architectures. To gain value from this data, you
must choose an alternative way to process it (Dumbill, 2013)."
Furthermore, the big data hype and phenomenon followed and
overlapped with the public sector interest in open government data
(Bertot et al., 2014), symbolically enforced at global level by
the memoranda and directives signed by Barack Obama in the early
years of his first mandate (Obama, 2009; Chignard, 2013). This
overlapping raised the question of the different values (economic,
public, and social value) that Big Data may have, and the
challenges related to having access and sharing them, such as data
quality and privacy (Batini et al., 2015; Jain, Gyanchandani and
Khare, 2016; Menon and Sarkar, 2016). This Special Issue aims to
provide an outlook on these issues, especially considering the
connection, on one hand, between Big Data, public safety,
security, and quality of life; on the other hand, on the different
paths of business models innovation enforced by Big Data such as
social innovation (Misuraca, Pasi and Viscusi, 2018) and
crowd-driven innovation (Afuah and Tucci, 2012; Afuah, Tucci and
Viscusi, 2018).
Inspired by the rise of Big Data platforms and infrastructure that
handle both structured and unstructured data from a multitude of
domains and data sources (ranging from environmental and weather
data to wearables, passenger vehicle sensors, financial and
insurance institutions data streams, and social web data), the
Special Issue will explore the benefits, advantages as well as the
challenges, limitation and threats (at the data security and
privacy levels) that emerge from the Big Data value chain (Miller
and Mork, 2013; Curry, 2016), delivering “intelligence” to
support operations that surround various aspects of human living.
Special attention will be dedicated but not limited to the
following areas:
- Digital governance and social innovation from Big Data
- Innovative meshed data services and ecosystems
- Intellectual property policies for Big Data
- New sustainable business models for Big Data sharing
- Open innovation, crowdsourcing, and Big Data
- Public safety early warning systems
- Public threat identification, pattern recognition, and risk
mitigation techniques
- Big Data and open science challenges
- Ethical aspects of Big Data
It is worth noting that the Special Issue will investigate the
topic of security from a social rather than technical perspective,
with a specific focus on social value impacts of Big Data-driven
innovation in terms of capabilities and “functionings” enabled by
emergent Big Data ecosystems (Sen, 1992; Nussbaum, 2011). Taking
these issues into account, Big Data and open linked data are a key
resource for enabling capabilities, support decision-making on
these issues, and develop appropriate policies and services, e.g.,
the examples provided by Viscusi et al. (2014). Furthermore, Big
Data-related phenomena of the quantified self as individuals
self-tracking of any kind of biological, physical, behavioral, or
environmental information (Swan, 2013) has been recently
associated with subjects other than human beings, e.g., to cars
and vehicles in general, which are actually able to capture
sensory data about themselves and about their environment, thus
becoming quantified vehicles (Stocker, Kaiser and Fellmann, 2017).
Accordingly, the emergence of different quantified subjects raise
questions on the role of Big Data for public safety and security
as well as the need for understanding the consequent
infrastructural challenges and designing new platforms and
services.
In summary, the Special Issue aims to provide a multidisciplinary
understanding of the impact of Big Data on personal safety,
personal security, and well-being. In addition, the Special Issue
aims to presents solutions and case studies.
The Special Issue dissemination and organization will be supported
by the AEGIS EC H2020 Innovation Action, aiming at creating an
interlinked “Public Safety and Personal Security” Data Value
Chain, and at delivering a novel platform for Big Data curation,
integration, analysis and intelligence sharing.
References
* Abbasi, A., S. Sarker and R. H. L. Chiang. (2016). “Big data
research in information systems: Toward an inclusive research
agenda.” Journal of the Association for Information Systems,
17(2), 3.
* Afuah, A. and C. L. Tucci. (2012). “Crowdsourcing as a solution
to
distant search.” Academy of Management Review, 37(3), 355–375.
* Afuah, A., C. L. Tucci and G. Viscusi. (2018). Creating and
Capturing Value Through Crowdsourcing. Oxford University Press.
* Batini, C., A. Rula, M. Scannapieco and G. Viscusi. (2015).
“From
data quality to big data quality.” Journal of Database Management,
26(1), 60–82.
* Bertot, J. C., U. Gorham, P. T. Jaeger, L. C. Sarin and H. Choi.
(2014). “Big data, open government and e-government: Issues,
policies and recommendations.” Information Polity, 19, 5–16.
* Buhl, H. U., M. Röglinger, F. Moser and J. Heidemann. (2013).
“Big
Data - A Fashionable Topic with(out) Sustainable Relevance for
Research and Practice?” Business & Information Systems
Engineering,
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* Chignard, S. (2013). “A brief history of Open Data.” Retrieved
from
http://parisinnovationreview.com/2013/03/29/brief-history-open-data/
* Curry, E. (2016). “The Big Data Value Chain: Definitions,
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and Theoretical Approaches BT - New Horizons for a Data-Driven
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* Günther, W. A., M. H. Rezazade Mehrizi, M. Huysman and F.
Feldberg.
(2017). “Debating big data: A literature review on realizing value
from big data.” The Journal of Strategic Information Systems,
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* Jain, P., M. Gyanchandani and N. Khare. (2016). “Big data
privacy: a
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* Menon, S. and S. Sarkar. (2016). “Privacy and Big Data: Scalable
Approaches to Sanitize Large Transactional Databases for Sharing.”
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* Miller, H. G. and P. Mork. (2013). “From Data to Decisions: A
Value
Chain for Big Data.” IT Professional, 15(1), 57–59.
* Misuraca, G., G. Pasi and G. Viscusi. (2018). “Understanding the
Social Implications of the Digital Transformation: Insights from
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Resilience of Society. BT - Electronic Participation - 10th IFIP
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8.5 International Conference, ePart 2018, K.”
* Nussbaum, M. C. (2011). Creating Capabilities - The Human
Development Approach. Cambridge (MA): The Belknap Press of Harvard
University Press.
* Obama, B. (2009). “Transparency and open government. Memorandum
for
the heads of executive departments and agencies.” Retrieved from
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* Rai, A. (2016). “Synergies between big data and theory.” MIS Q.,
40(2), iii–ix.
* Sen, A. (1992). Inequality Re-examined. Oxford: Clarendon Press.
* Stocker, A., C. Kaiser and M. Fellmann. (2017). “Quantified
Vehicles.” Business & Information Systems Engineering, 59(2),
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* Swan, M. (2013). “The quantified self: Fundamental disruption in
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data science and biological discovery.” Big Data, 1(2), 85–99.
* Viscusi, G., M. Castelli and C. Batini. (2014). “Assessing
social
value in open data initiatives: a framework.” Future Internet,
6(3),
498–517.
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