[AISWorld] Call for Papers: Information Technology & People - Special Issue on, "Perspectives on the values of Big Data sharing"
Gianluigi Viscusi
gianluigi.viscusi at epfl.ch
Wed Feb 27 06:49:11 EST 2019
-- Apologies if you receive multiple calls of this Call for Papers --
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 at epfl.ch
Gianluigi Viscusi, EPFL CDM MTEI CSI ODY 1 04 (Odyssea) - Station 5
CH-1015 Lausanne - Switzerland,
email: gianluigi.viscusi at 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,
5(2), 65–69.
* 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, Concepts,
and Theoretical Approaches BT - New Horizons for a Data-Driven
Economy: A Roadmap for Usage and Exploitation of Big Data in
Europe.” In: J. M. Cavanillas, E. Curry, & W. Wahlster (Eds.), (pp.
29–37). Cham: Springer International Publishing.
* Davenport, T. H., P. Barth and R. Bean. (2012). “How “Big Data” Is
Different.” MIT Sloan Management Review, 54(1), 43–46.
* Dumbill, E. (2013). “Making Sense of Big Data (Editorial).” Big
Data, 1(1), 1–2.
* Goes, P. (2014). “Editor’s Comments: Big Data and IS Research.”
Management Information Systems Quarterly, 38(3), iii–viii.
* 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, 26(3),
191–209.
* Jain, P., M. Gyanchandani and N. Khare. (2016). “Big data privacy: a
technological perspective and review.” Journal of Big Data, 3(1), 25.
* Menon, S. and S. Sarkar. (2016). “Privacy and Big Data: Scalable
Approaches to Sanitize Large Transactional Databases for Sharing.”
MIS Quarterly, 40(4), 963–981.
* 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
Four Case Studies on the Role of Social Innovation to Foster
Resilience of Society. BT - Electronic Participation - 10th IFIP WG
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
https://www.whitehouse.gov/open/documents/open-government-directive
* 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), 125–130.
* Swan, M. (2013). “The quantified self: Fundamental disruption in big
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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