[SIGCIS-Members] HICSS-57 Big Data and Analytics Minitrack CFP

[STEPHEN KAISLER]

Dear Members:

Here is the CFP for our minitrack - on behalf of myself, Frank Arnour, Alberto Espinosa, and Derrick Cogburn.

Call For Papers

HICSS-57 Decision Analytics and Service Systems Minitrack

http://www.hicss.org/.

January 3-6, 2024

Hilton Hawaian Village, Honolulu, Oahu, Hawai’i


Title: Big Data  and Analytics: Pathways to Maturity

“The first and original Big Data minitrack at HICSS”

Co-Chairs: Stephen H. Kaisler; Frank Armour; J. Alberto Espinosa; Derrick L. Cogburn


NEWS!! Derrick Cogburn’s Text Analytics Minitrack has merged with the Big Data and Analytics Minitrack. This merger brings with it the opportunity for accepted papers to be considered for the HICSS Fast Track journal Data & Policy, published by Cambridge University Press. We have also added new topics of explainability and bias in analytics to our CFP.

NEWS!! HICSS is the most cited conference for academic citations!

NEWS!! Long-awaited return to the island of Oahu, Hawai’i.

This minitrack focuses on the use of big data and analytics to enable businesses and organizations to optimize their operational practices, improve their decision-making, and better understand and provide more effective services to their customers and clients. We seek papers in all areas of big data and analytics, including storage, management, education, usage case studies, innovative applications, and enabling technology.  Relevant papers on the development of strategy for deploying big data and analytics in distributed organizations, the effects of big data and analytics on organizational behavior, the governance and management of big data, the evaluation of Big Data’s contribution to business operations, the development of big data analytics are sought. Papers are sought on developing an analytic cadre, including curriculum concepts, skills and training, and metrics and measurement.

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 Big Data and Business Analytics 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.). To advance knowledge in big data and analytics, we must develop active research agendas in all four layers. We seek research papers on any of these four layers and also those that address the structure and evaluation of curriculum design, implementation and evaluation for big data and analytics.


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
* Advances in 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.
* Enhanced explainability in analytics (particularly AI/ML) relative to results and the detection and mitigation of bias and variance in analytics.
* Text analytics approaches in quantitative and qualitative techniques in natural language processing.
* Supervised and Unsupervised machine learning approaches in text analytics.

Papers presenting case studies, infrastructure and technology advances, theoretical perspectives and emerging concepts at the petascale and beyond, are also sought.

A fast track publishing opportunity ahs been arranged with the Data and Policy journal. We are exploring other opportunities as well.

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. If you wish to discuss a paper concept on text analytics before submitting, please contact Derrick Cogburn. Text Analytics researchers may also wish to participate in the linked afternoon workshop on Advanced Text Analytics and HICSS Textathon. We will be happy to discuss your paper concept with you.


Kindly,

Minitrack Co-Chairs:

Stephen H. Kaisler, D.Sc. (Skaisler1 at comcast.net mailto:Skaisler1 at comcast.net ), SHK & Associates (primary)  

Frank Armour, Ph.D. (farmour at american.edu mailto:farmour at american.edu ), Kogod School of Business, American University

Alberto Espinosa, Ph.D. (alberto at american.edu mailto:alberto at american.edu ), Kogod School of Business, American University

Derrick L. Cogburn, Ph.D. (dcogburn at american.edu mailto:dcogburn at american.edu ), School of International Service and Kogod School of Business, American University

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