Pacific Asia Conference on Information Systems (PACIS) 2014
Business Intelligence and Big Data Analytics Track
Chengdu, China, June 24-28, 2014

http://pacis2014.org/track_bi.php

Track Co-Chairs:
Roger H.L. Chiang, University of Cincinnati, USA, roger.chiang@uc.edu
Shu Schiller, Wright State University, USA, shu.schiller@wright.edu
Zhe (Jay) Shan, University of Cincinnati, USA, zhe.shan@uc.edu
Harry Jiannan Wang, University of Delaware, USA, hjwang@udel.edu

Track Description:
Business intelligence (BI) technologies have gained increasing attention in

recent years, which provide historical, current, and predictive views of

business operations based on advanced data collection, extraction, and

analysis of large data sets to improve business decision making. In recent

decade, Web 2.0 has created an abundance of user-generated contents from

online social media such as forums, online groups, web blogs, social

networking sites, social multimedia sites, and even virtual worlds. It has

begun to usher in a new and exciting era of Business Intelligence research.

More recently “Big Data” and “Big Data Analytics” have been used to

describe the data sets and analytical techniques in applications that are

so large (from terabytes to exabytes) and complex (from sensor to social

media data) that they require advanced and unique data storage, management,

analysis, and visualization technologies. Advanced information extraction,

topic identification, opinion mining, and time-series analysis techniques

can be applied to traditional business information and new BI contents for

various accounting, finance, and marketing applications, such as enterprise

risk assessment and management, credit rating and analysis, corporate event

analysis, stock and portfolio performance prediction, viral marketing

analysis, etc. By designing and evaluating IT artifacts within the

organizational and managerial context, much can be learned about BI

technologies, practices, and challenges. In this track, we are interested

in innovative technologies, methodologies, and theories in business

intelligence and big data analytics, which are not limited to a design

science approach, but include rigorous and relevant research using

management science (analytical modeling and simulation).

Topics of interest include but not limited to:
E-Commerce and Market Intelligence
* Recommender systems
* Social media analytics
* Opinion mining and sentiment analysis
* Crowd-sourcing systems
* Social and virtual games
* Web mining and analytics for Web 2.0
* Big data analytics in business applications

Smart Enterprise Systems
* Innovative data warehousing, ETL, and OLAP in BI
* Visual interface and HCI for BI
* Data and text mining for emerging BI applications
* Business process mining and intelligence

E-Government and Politics 2.0
* Ubiquitous government services
* Equal access and public services
* Citizen engagement and participation
* Political campaign and e-polling

Smart Health and Wellbeing
* Human and plant genomics
* Healthcare decision support
* Patient community analysis

Security and Public Safety
* Crime analysis
* Computational criminology
* Terrorism informatics
* Open-source intelligence
* Cyber security

Financial Services Analytics and Intelligence
* Intelligent financial process risk monitoring and management
* Agent-based modeling and analysis for financial applications
* Business intelligence applications for finance
* Financial network modeling and analysis
* Data-mining for financial applications
* Knowledge management for financial organizations


PACIS2014 Submission Website: http://pacis2014.org/initial.php
Paper submission deadline: March 3rd, 2014
Acceptance notification: April 20th, 2014
Final version due: May 1st, 2014