题 目：Big data Management and Analytics using Map Reduce and Stream Mining
时 间：7月3日（本周三）下午2点30分
地 点：深圳大学科技楼1401
报告人：Latifur Khan 美国University of Texas at Dallas计算机系教授
Cloud computing is rapidly growing in popularity as a solution for processing and retrieving huge amounts of data over clusters of inexpensive commodity hardware. The most common data model utilized by cloud computing software is the NoSQL data model (e.g., Map Reduce). While this Big data model is extremely scalable, it is much more efficient for simple retrievals and scans than for the complex analytical queries typical in a relational database model. In this presentation, we will evaluate emerging cloud computing technologies using a representative use case. Our use case involves analyzing semantic web data for efficient retrieval using map reduce.
Data streams are continuous flows of data. Examples of data streams include network traffic, sensor data, call center records and so on. Their sheer volume and speed pose a great challenge for the data mining community to mine them. In this talk we will present how to find “unknown patterns” from evolving stream data and show its applications such as adaptive malicious code detection, on-line malicious URL detection, evolving insider threat detection and textual stream classification.
This research was funded in part by NASA and Air Force Office of Scientific Research (AFOSR).
Short bio:
Dr. Latifur Khan is currently a full Professor (tenured) in the Computer Science department at the University of Texas at Dallas, USA where he has been teaching and conducting research since September 2000. He received his Ph.D. and M.S. degrees in Computer Science from the University of Southern California (USC) in August of 2000, and December of 1996 respectively. He was a recipient of Chancellor Awards from the President of Bangladesh. Dr. Khan is an ACM Distinguished Scientist. He has received prestigious awards including the IEEE Technical Achievement Award for Intelligence and Security Informatics.
Dr. Khan has published over 170 papers in 40 journals, in peer reviewed conference proceedings, and in three books.