Making Sense of Big Data
It would not be an exaggeration to say that big data has transformed many aspects of how we approach problems in information organization and retrieval. In this context, my recent work focuses on two questions:
- How do we build effective and efficient information access systems that help users make sense of massive quantities of data?
- How do we build scalable infrastructure to support these applications?
There is a tension between effective techniques that deliver high-quality results and efficient techniques that are fast and responsive. In this talk, I will discuss a number of case studies drawn from my academic research and industry experiences, and show how we are able to achieve a good balance between effectiveness and efficiency. Underscoring these applications is the importance of having the right infrastructure, both computational and human, to tackle big data challenges. I'll conclude with a discussion of the role that Information Schools should play within this broader intellectual space.
Jimmy Lin is an associate professor and the associate dean of research in the College of Information Studies at the University of Maryland, with a joint appointment in the Institute for Advanced Computer Studies (UMIACS) and an affiliate appointment in the Department of Computer Science. He graduated with a Ph.D. in electrical engineering and computer science from MIT in 2004. Lin’s research lies at the intersection of information retrieval and natural language processing; his current work focuses on large-scale distributed algorithms and infrastructure for data analytics. From 2010–2012, Lin spent an extended sabbatical at Twitter, where he worked on services designed to surface relevant content to users and analytics infrastructure to support data science.
