Big Data Stream Analytics and Its Applications

Professor Latifur Khan, Department of Computer Science, University of Texas at Dallas, USA

Chaired by
Dr WANG Wei, Assistant Professor, School of Computing

  22 Nov 2019 Friday, 10:00 AM to 12:00 PM

 MR3, COM2-02-26


Data streams are continuous flows of data. Examples of data streams include network traffic, sensor data, call center records and so on. Data streams demonstrate several unique properties that together conform to the characteristics of big data (i.e., volume, velocity, variety, and veracity) and add challenges to data stream mining. In this talk, we will present an organized picture on how to handle various data mining/machine learning techniques in data streams. In addition, we will present a number of stream classification applications such as adaptive website fingerprinting, textual stream analytics (political actor identification over textual stream), attack trace classification using good quality similarity metrics (metric learning) and domain adaptation.

This research was funded in part by NSF, NASA, Air Force Office of Scientific Research (AFOSR), NSA, IBM Research, HPE 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. degree in Computer Science from the University of Southern California (USC) in August of 2000.

Dr. Khan is an ACM Distinguished Scientist and received IEEE Big Data Security Senior Research Award, in May 2019, and Fellow of SIRI (Society of Information Reuse and Integration) award in Aug, 2018. He has received prestigious awards including the IEEE Technical Achievement Award for Intelligence and Security Informatics and IBM Faculty Award (research) 2016.

Dr. Latifur Khan has published over 300 papers in premier journals such as VLDB, Journal of Web Semantics, IEEE TDKE, IEEE TDSC, IEEE TSMC, and AI Research and in prestigious conferences such as AAAI, IJCAI, CIKM, ICDE, ACM GIS, IEEE ICDM, IEEE BigData, ECML/PKDD, PAKDD, ACM Multimedia, ACM WWW, ICWC, ACM SACMAT, IEEE ICSC, IEEE Cloud and INFOCOM. He has been invited to give keynotes and invited talks at a number of conferences hosted by IEEE and ACM. In addition, he has conducted tutorial sessions in prominent conferences such as SIGKDD 2017, 2016, IJCAI 2017, AAAI 2017, SDM 2017, PAKDD 2011 & 2012, DASFAA 2012, ACM WWW 2005, MIS2005, and DASFAA 2007.

Currently, Dr. Khan's research area focuses on big data management and analytics, data mining and its application over cybersecurity, complex data management including geo-spatial data and multimedia data. His research has been supported by grants from NSF, the Air Force Office of Scientific Research (AFOSR), DOE, NSA, IBM, and HPE. More details can be found at www.utdallas.edu/~lkhan/.