Data Science
With the coming era of "big data", the ability to collect, analyze, and use data to inform decisions is critical to both industry and academia. In our lab we employ different approaches to transform data into actionable knowledge, such as information scraping, data analytics or simulation tools to inform how we can use "big data" to make better decisions.
People
Navid Ghaffarzadgean
Ran Xu
Sarah Mostafavi
Work in Progress
Data- Intensive Services
Data-intensive services are defined as high tech services that utilize massive datasets to provide customized experience for users, examples being Google Map, Amazon, Airbnb, YouTube, Yelp, and Pandora. Value generation in these services depends on collecting and analyzing behavioral data on users’ interests and preferences, browsing pattern, device usage, as well as past choices, opinion, and location. Here, we model and examine long term performance of data-intensive services in the presence of the data-value cycle and compare it with the conventional model of service where value creation is not very sensitive to customer data.
Escalation Project
While simple operational problems have solutions, there are problems that are complex to fix and may get escalated. As performance parameters improve with new releases of a software product and old problems are solved, new problems also occur. The oscillating behavior of related data brings in new research questions about the dynamics of such systems. This project is aimed at understanding such dynamics and creating a smart diagnosis system based on data for major operational problems to help improve the system's performance.