Matthew Hendricks and Anamaria Berea Receive Honors from Teradata University Network

September 12, 2014 - The Center for Complexity in Business (CCB) would like to congratulate Matthew Hendricks, a CCB researcher, on being awarded as a finalist in the 2014 Teradata University Network Inaugural Student Poster Contest, and Anamaria Berea, a CCB postdoctoral fellow, for being awarded a 2014 Teradata University Network Faculty Scholarship.

Every year, thousands of professionals in the areas of big data, analytics and data driven marketing gather to learn about new technologies and share their unique implementations with each other at the Teradata PARTNERS Conference. This year, the Teradata University Network (TUN) will be a large part of the 29th conference in Nashville on October 19th - 23rd, 2014.  Earlier this year, Teradata University Network announced two separate opportunities for academics to attend the event, a scholarship program and a student poster contest.  The University of Maryland’s Center for Complexity in Business is very proud to have a finalist/winner for both opportunities.

Matthew Hendricks’ winning submission in the poster contest is entitled Big Network Analysis for Influence Identification on Social Networks. The PARTNERS Poster Contest gives students the opportunity to present and discuss their research or application cases related to data warehousing and analytics informally with conference attendees.

Anamaria Berea’s Faculty Scholarship winning submission included an assessment of data science skills and experience, and a statement of purpose for using big data in research and teaching as part of the Teradata University Network.

All finalists and winners will be receiving full conference registration to the event which includes: access to over 200 educational sessions, the Expo with the top technology companies in big data and analytics, and all conference meals and special events including the Gala Event with The Band Perry at the Grand Ole Opry!

Matthew Hendricks’ winning abstract is below –

Big Network Analysis for Influence Identification on Social Networks 
Matthew Hendricks - University of Maryland

In the past few years there has been a dramatic increase in the magnitude of network-based data due to the omnipresent consumption of social media networks.  Online marketing strategies have widely adopted use of such data, but analysis of online social networks poses a significant challenge, due to the volume of data and access restrictions imposed by social media sites.  Due to such restrictions, we must resort to network sampling techniques. In this research, we discuss the challenges related to large network analysis using MapReduce-based cloud services and the efficacy of network crawling techniques as a means for representative sampling.