The big data challenge is about making sense of large collections of complex and dynamic information, and using them as assets for business intelligence and other forms of analytics-based insight. Example applications include smart advertising and marketing based on user-action logs, intelligent control of complex logistics for traffic and energy, or identifying trends and opinions in social media in the Internet.
Our group is exploring ways to efficiently and effectively mine emerging topics in streams of user-generated Web 2.0 contents and other kinds of interesting patterns in highly dynamic data generated by sensors or in business settings.

News:

• Two new papers at dbsocial@SIGMOD, on event detection in top-k rankings and on scalable, continuous computation of set correlations.
• Busy Beaver Award for the best seminar in computer science for our seminar on Non-Traditional Data Management.
• Upcoming summer term: Lecture on Distributed Data Management at Kaiserslautern University of Technology. See here
• Vision paper on "Automated Educated Guessing" accepted.
• Winter Semester 2012/13: Seminar on Non-Traditional Data Management (NoSQL and more). See here
• New Full Paper at CIKM 2012 on Processing Top-K Queries with Set-Defined Selections.
• New paper on enhancing Locality Sensitive Hashing (LSH) has been accepted at WebDb 2012.
• Our papers on emergent topic detection (enBlogue) and top-k interesting phrases mining got accepted as full papers at EDBT.

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