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Big Data and Analytics: Pathways to Maturity
Co-Chairs: Stephen H. Kaisler; Frank Armour; J. Alberto Espinosa
This first and original Big Data and Analytics (BD&A)
minitrack at HICSS focuses on research on BD&A as an enabler
of businesses and organizations to optimize their operational
practices, improve their decision-making, and better understand
and provide more effective services to their clients and
stakeholders. In our book titled: "Obtaining Value from Big Data
for Service Delivery, 2nd Edition" we presented a knowledge
framework to serve as a basis for grounding BD&A curricula.
The framework is composed of key knowledge layers, including:
basic foundations (e.g., mathematics, statistics, software
programming, big data architectures); analytics/big data (e.g.,
descriptive, predictive, prescriptive and visual analytics);
functional domain of analysis (e.g., marketing, healthcare,
accounting forensics, etc.); and managerial, strategic and
organizational aspects (e.g., data governance, security, privacy,
human resource management, metrics for the business value of big
data and analytics investments, etc.).
We seek papers in all areas of BD&A, particularly papers on
the development of strategy for deploying BD&A in distributed
organizations, the effects of BD&A on organizational behavior,
the governance and management of BD&A, the evaluation of
related contributions to business operations, and the development
of BD&A capabilities, including curriculum, skills, training
and metrics.
Papers are solicited in several areas, including, but not limited
to the following:
* Managerial, governance, lifecycle, strategic and organizational
aspects of big data and analytics, big data repositories and
projects, including data governance
* Graph analytics - both syntactic and semantic - that play a big
role in the exploitation of social media data
* Advanced analytics emphasizing specific functional domains -
business, scientific, and social science, visual analytics and
non-numeric analysis models and their implementation
* Modeling issues, including computational methods, programming
languages and automated machine learning
* Advances in big data technology - processing, storage, analytics
- for the Exabyte/ExaFLOP Age
* Scalable semantic annotation and reasoning across big data
stores
* Metrics to assess the impact of big data in business,
scientific, and government decision-making.
* Educational and body of knowledge frameworks on big data,
analytics and data science.
Papers presenting case studies, infrastructure and technology
advances, theoretical perspectives, and emerging concepts at the
petascale and beyond, are also sought. An expanded CfP is
available from Steve Kaisler. We especially encourage graduate
students to submit papers as well. If you wish to discuss a paper
concept before submitting a paper, please contact Steve Kaisler or
Frank Armour. We will be happy to discuss your paper concept with
you.
Kindly,
Minitrack Co-Chairs:
Stephen H. Kaisler, D.Sc.
(
Skaisler1@comcast.net<mailto:Skaisler1@comcast.net>), SHK
& Associates (primary)
Frank Armour, Ph.D.
(
farmour@american.edu<mailto:farmour@american.edu>), Kogod
School of Business, American University
J. Alberto Espinosa, Ph.D.
(
alberto@american.edu<mailto:alberto@american.edu>), Kogod
School of Business, American University
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