Wubai Zhou

Florida International University

Lecture Information:
  • October 4, 2017
  • 9:30 AM
  • ECS 349
Photo of Zhou Wubai

Speaker Bio

Wubai Zhou is a Ph.D. candidate in the School of Computing and Information Sciences at Florida International University. He received his B.Sc. degree in the School of Computer Science and Technology in 2012 from Wuhan University, China. Wubai joined FIU in Fall 2012, under the supervision of Dr. Tao Li and Dr. Shu-Ching Chen, and received an M.S. degree in 2015 from FIU. His research interests include system oriented data mining, system management, and machine learning.


More than ever, information delivery online and storage heavily rely on text. Billions of texts are produced every day in the form of documents, news, logs, search queries, ad keywords, tags, tweets, messenger conversations, social network posts, etc. Text understanding is a fundamental and essential task involving broad research topics, and contributes to many applications in the areas of text summarization, search engine, recommendation systems, online advertising, conversational bot and so on. However, understanding text for computers is never a trivial task, especially for noisy and ambiguous text such as logs and search queries. The breadth and depth of text understanding indicates the impossibility to coverage all related topics, and this thesis mainly focus on tasks derived from two domains, i.e., disaster management and IT service management that mainly utilizing textual data as an information carrier.


Improving situation awareness in disaster management and alleviating human efforts involved in IT service management dictates more intelligent and efficient solutions to understand the textual data acting as the main information carrier in the two domains. From the perspective of data mining, four directions are identified and considered to be helpful for text understanding: (1) Intelligently generate a storyline summarizing the evolution of a hurricane from relevant online corpus; (2) Automatically recommending resolutions according to the symptom description in a ticket; (3) Gradually adapting the resolution recommendation system for time correlated features derived from text; (4) Efficiently learning distributed representation for possibly short and lousy ticket symptom descriptions and resolutions. Provided with different types of textual data, data mining techniques proposed in those four research directions successfully address our tasks to understand and extract valuable knowledge from those textual data.


My dissertation will address the research topics outlined above. Concretely, I will focus on designing and developing data mining methodologies to better understand textual information which thus improve situation awareness in disaster management and help system administrators better manage the system and alleviate the human efforts involved in IT Service management, including (1) a storyline generation method for natural hurricanes based on crawled online corpus which summarizing the evolution of the hurricane with temporal and spatial information; (2) a recommendation framework for automated ticket resolution in IT service management; (3) an adaptive recommendation system on time-varying temporal correlated features derived from text; (4) a deep neural ranking model not only successfully recommending resolutions but also efficiently outputting distributed representation for ticket descriptions and resolutions.