Post Conference Workshop

WEDNESDAY, 14 NOVEMBER

9:00 am – 5:00 pm
Big Metadata Analytics: Setting A Research Agenda for the Data-Intensive Future 
Jian Qin, Syracuse University; Chaomei Chen; Drexel University, Jane Greenberg, Drexel University;  Jeff Hemsley, Syracuse University; Dietmar Wolfram, University of Wisconsin – Milwaukee

Big metadata from research data repositories, catalog systems, and indexing databases is a unique data source for studying collaboration, science history, knowledge diffusion, and many other phenomena emerged from the knowledge creation pro-cess and offers opportunities for building theories and methodologies for a new research area. The big metadata’s quality and readiness for analysis, however, is a major obstacle for using this vast data source for research. This workshop will bring together researchers who have used or are using big metadata in their projects to share their research methods and findings. Through group discussions, participants will develop a research agenda for big metadata analytics.

8:00 am – 12:00 pm
Building a Foundation for Integrating AI and Text Analytics
Tom Reamy, KAPS Group, United States of America

While new AI techniques are generating a lot of press, they have some severe limits when applied to text rather than pattern-based perception. These limits can be overcome with the addition of a range of text analytics techniques – text mining, machine learning, noun phrase extraction, auto-categorization, auto-summarization, and social media/sentiment analysis. The essential trick is how to integrate two very disparate fields that barely speak the same language. This workshop, based on the recent book, Deep Text: Using Text Analytics to Overcome Information Overload, Get Real Value from Social Media, and Add Big(ger) Text to Big Data, will take attendees through the entire process of creating a text analytics foundation that provides the means for that integration. The workshop will include exercises designed to deepen the participant’s appreciation for the practical process of building text analytics applications and, at the same time, exemplify some of the key theoretical issues.