Research

INSITE Research

INSITE: Center for Business Intelligence and Analytics is a research center that addresses the ever-growing volume, velocity, and variety of big data being generated by social media and Web 2.0.

This innovative center focuses on predictive analytics through the use of data generated from social media, internal transactional, sensor, and other emerging big data to provide analytics across multiple social media platforms. It provides visualization and real-time network analysis of interaction patterns gleaned from big data.

Current Research Projects

A Data Science Platform and Mechanisms for Its Sustainability
This is a research project using cutting-edge natural language processing and other analytic tools to provide a platform for scholars, citizens, environmental professionals and agency staff to answer a host of critical questions about how we impact the environment and how the environment impact us.

BRIDGE: Biomedical Research Innovation through Dynamic Graph Engineering
INSITE Center for Business Intelligence and Analytics is developing graph algorithms for biomedical big data. Creating the capacity to transform how biomedical data are stored, analyzed and visualized towards an integrated biomedical knowledge environment.

Chronic Disease Trends in Arizona Counties
This is an analysis for chronic disease trends in Arizona Counties. We explore the hospital inpatient discharge data of Arizona maintained by the Arizona department of health services (ADHS). Three dashboards are conducted for population-level exploratory analysis of patient visit count, average visit cost, and a combination of both elements.

CyberSW: A Data Synthesis and Knowledge Discovery System for Long-term Interdisciplinary Research on Southwest Social Change

CyberSW will result in the data integration of millions of objects from tens of thousands of settlements spread across the U.S. Southwest that were inhabited between ca.A.D. 800 to 1550, making it one of the largest archaeological databases in the world.

Disease Co-Occurrence

Comorbidity or disease co-occurrence is the simultaneous presence of one or more diseases. Application of network analysis to understand disease comorbidity is an active research area.

Large-Scale Network Analysis for News Propagation on Social Media

Sharing news articles using 140 characters: a diffusion analysis on Twitter.

Large-Scale Network Analytics for Online Social Brand Advertising

An audience selection framework for on-line brand advertising based on a large amount of user historical activities on social media platforms.

Predictive Modeling in Health Care

Exploring the use of Twitter data to predict health outcomes, including utilization and disease detection.

Prediction Modeling and Analytics for Chronic Health Conditions

Developing prediction models to understand chronic health conditions with a combination of machine learning, natural language processing and network science.

Smart Cities

Analyses of the public transportation systems in Fortaleza, Brazil to understand their usage in addressing the challenges of overcrowding, delays, and public safety.
Social Media Monitoring and Surveillance for Vector Borne Diseases 
This is a research project for developing a strategy and infrastructure to use social media to monitor topics for the purpose of public health surveillance in Pima County, Arizona.

Temporal and Spatial Analysis in Education for Building a Smart Campus

Combining university ID card data with student records, and potentially social media, to develop broader patterns of student behavior and engagement, service, and facility utilization.

Tracking Digital Traces to Help Students Succeed

Tracking the digital Catcard traces of students to see what they reveal about students’ routines and relationships — and their likelihood of returning to campus after their first year.
Wellbuilt for Wellbeing
Using big data to investigate the effects of the physical work environment on physiological health outcomes

Past Projects