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 representative use cases. Our use cases involve analyzing semantic web data for efficient retrieval using map reduce, and geo-spatial query processing over geospatial dataset.

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 in a scalable manner and show its applications such as adaptive malicious code detection, evolving insider threat detection and textual stream classification. In addition, we will show how this framework can be augmented with change point detection to detect anomalies over stream data.

This research was funded in part by NSF, NASA, Air Force Office of Scientific Research (AFOSR) and Raytheon.

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 in August of 2000, and December of 1996 respectively. 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 180 papers in prestigious journals, and in peer reviewed conference proceedings.