Spring 2004 Data and Information Ph.D. Qualifying Examination

• Athman Bouguettaya
• Edward Fox
• Chang-Tien Lu

• Since students vary in their abilities regarding written and oral communication, and since doctoral students are expected to have some skill with each media type, students will explain their solutions both in writing and orally. Solutions will be graded based on their clarity as a result of the union of these modes of expression.
• Students are expected to have studied all works in the reading list. Any pre-requisite or background knowledge required to understand the works in the reading list are also expected to be acquired by the student.
• Students are expected to understand those works at the level of a doctoral student who has taken the equivalent of courses such as CS5604 and CS5614.
• Students are expected to be able to understand a real situation/context/problem in the information/data area, to be able to synthesize/apply the findings of multiple papers from the reading list to such problems, and to be able to formulate an answer outlining how they would approach and solve that problem.

• The examination will be a takehome examination and is expected to be administered in the beginning of 2004.
• At the beginning of the examination period, all students will receive a document that contains three questions.
• By the end of the examination period, each student must turn in a written solution to one of those questions, i.e., the student must choose one out of two. It is expected that the solutions (to both questions, taken together) will be no longer than about 15 double spaced typed pages (excluding references) at 11 point or larger. Specific details about format and length will be provided along with the questions.
• Also at this time, each student must turn in a PowerPoint presentation or equivalent that will be used for an oral explanation of the written solution. Oral explanations, lasting no longer than 30 minutes, will be scheduled as soon after the end of the exam week as feasible, using VTEL or equivalent as needed to ensure coverage by students and/or faculty in either Blacksburg or N. Virginia.
• Written solutions might be expected to have the following approximate format (although detailed guidelines will be provided during the exam):
  • a motivation section making clear the context of the problem/situation
  • a clear statement of the problem in terms of concepts and terminology in the information/data area, that addresses the situation/context
  • a review of related literature, drawn mostly from multiple relevant works in the reading list
  • a statement of how the problem can be approached
  • a description of the approach to solve the problem
  It is important that any assumptions made be clearly stated in the written solution.
  
• Oral presentations must follow what is given in the previously turned-in PowerPoint file or equivalent. They must be completed within a 30 minute period, in which roughly 20 minutes are for presentation and 10 minutes for answering questions posed by faculty examiners.
• Each solution will be graded by at least 2 faculty members. A combined grade will then be assigned for each student based on all faculty input by the area committee, on a scale of 0-3, as is called for by GPC policies.

Presentation Tips

• U of Waterloo , U. of Wisconsin-Madison , U of Wisconsin-Madison , Brown U.
• University of Bradford , University of Bradford , University of Kansas

Oral Examination Schedule

• 2/25(W) 9:00 - 9:40AM (NVC 113,VT Dur 463) : Choudhry M. Zaki Malik
• 2/25(W) 9:40 - 10:20:AM (NVC 113,VT Dur 463): Qi Yu
• 2/25(W) 10:20 - 11:00AM (NVC 113,VT Dur 463): Xumin Liu
• 2/25(W) 12:00 - 12:40PM (NVC 113,VT Dur 463): Mohammad Salman Akram
• 2/26(Th) 2:00 - 2:40PM (NVC 113,VT Bur 123A): Xiaoyan Yu
• 2/26(Th) 2:40- 3:20PM (NVC 113,VT Bur 123A): Bing Liu

Reading List

(Note: Some of the hyperlinks below lead to web pages maintained by the respective publishers. You may or may not be able to download the articles directly from these web pages - this depends on the host computer from which the access is made. To access the articles, we recommend that you go through the VT-subscribed ACM digital library or IEEE Explore interface).

• A. Arasu, J. Choo, H. Garcia-Molina, A. Paepcke, and S. Raghavan, Searching the Web, ACM Transactions on Internet Technology, Vol. 1, No. 1, pages 2-42, Aug 2001.
• S.A. McIlraith, T.C. Son, and H. Zeng, Semantic Web Services, IEEE Intelligent Systems, Vol. 16, No. 2, March-April 2001.
• J.G. Hayes, E. Peyrovian, S. Sarin, M.T. Schmidt, K.D. Swenson, and R. Weber, Workflow interoperability standards for the Internet, IEEE Internet Computing, Vol. 4, No. 3, pages 37-45, May-June 2000.
• A. Tsalgatidou and T. Pilioura, An Overview of Standards and Related Technology in Web Services, Distributed and Parallel Databases, Vol. 12, No. 2, pages 135-162, Sep-Nov 2002.
• D. Kossmann, The State of the Art in Distributed Query Processing, ACM computing Surveys, Vol. 32, No. 4, pages 384-421, December 2000.
• A. Sheth and J. Larson, Federated Database Systems for Managing Distributed, Heterogeneous, and Autonomous Databases, ACM Computing Surveys, Vol.22, No.3, pages 183-236, Sep 1990.
• Brian Babcock, Shivnath Babu, Mayur Datar, Rajeev Motwani, Jennifer Widom, Models and Issues in Data Stream Systems, ACM Symposium on Principles of Database Systems(PODS), pages1-16, 2002.
• S. Shekhar, P. Schrater, W. Raju, and W. Wu, “Spatial Contextual Classification and Prediction Models for Mining Geospatial Data”, IEEE Transactions on Multimedia, 4(2): 174-188, 2002.
• Sudipto Guha, Adam Meyerson, Nina Mishra, Rajeev Motwani, Liadan O'Callaghan, Clustering Data Streams: Theory and Practice, IEEE Transactions on Knowledge and Data Engineering, Vol. 15, No.3, pages 515-528, May/June 2003.
• W.G. Teng, M.S. Chen, P.S. Yu, A Regression-Based Temporal Pattern Mining Scheme for Data Streams, Proceedings of 29th International Conference on Very Large Data Bases, pages 93-104, 2003.
• H. Wang, W. Fan, P.S. Yu, J. Han, Mining Concept-Drifting Data Streams Using Ensemble Classifiers, Proceedings of the ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 226-235, 2003.