Spring 2008 Data and Information Ph.D. Qualifying Examination

• Edward Fox
• Chang-Tien Lu (Primary Contact)
• Naren Ramakrishnan

• 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 Information Storage and Retrieval, CS5614 Database Management Systems, and CS5984 Introduction to Data Mining.
• 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 includes a takehome examination that is expected to be administered in the beginning of 2008.
• 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 three. It is expected that the solutions will be no longer than 10 pages (excluding references) at 10 point or larger using IEEE 2-column style format.
• 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 25 minutes are for presentation and 5 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.

Schedule

• 11/5 (Monday), 2007: Complete Reading List Available.
• 1/12 (Saturday), 2008 : Written Examination Available.
• 1/25 (Friday) 5PM, 2008: Written Examination Due.
• 1/30 (Wednesday) 5PM, 2008: PowerPoint Presentation Slide Due.
• 2/1-2/8 (Friday), 2008: Oral Examination.
• 2/15 (Friday), 2008: Exam Results due to GPC.

Oral Examination Schedule (2/5, Tuesday, Torgersen Hall 2030)

• (1) 9:00 - 9:35AM: Sheng Guo
• (2) 9:40 - 10:15AM: Yifei Ma
• (3)10:25 - 11:00AM: Yong Ju Cho
• (4)11:05 - 11:40PM: Xiaomo Liu
• (5) 11:50 - 12:25PM: Patrick Butler

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. Ceglar and J.F Roddick, Association Mining, ACM Computing Surveys, Vol. 38, No. 2, article number 5, 2006.
• D. Chakrabarti and C. Faloutsos, Graph Mining: Laws, Generators, and Algorithms, ACM Computing Surveys, Vol. 38, No. 1, article number 2, 2006.
• F. Moerchen, Unsupervised Pattern Mining from Symbolic Temporal Data, ACM SIGKDD Explorations, Vol. 9, No. 1, June 2007, pages 41-55.
  
• W. Buntine, Operations for Learning with Graphical Models, Journal of Artificial Intelligence Research, Vol. 2, pages 159-225, 1994.
• Andreas Paepcke, Chen-Chuan K. Chang, Terry Winograd, Héctor García-Molina, Interoperability for Digital Llibraries Worldwide, April 1998 Communications of the ACM, Volume 41 Issue 4.
• Yves Petinot, C. Lee Giles, Vivek Bhatnagar, Pradeep B. Teregowda, Hui Han, Isaac Councill, Service Applications: A Service-oriented Architecture for Digital Libraries, November 2004, Proceedings of the 2nd international conference on service oriented computing. Publisher: ACM Press.
• Greg Janée, James Frew, Digital Libraries for Spatial Data: The ADEPT Digital Library Architecture, July 2002 Proceedings of the 2nd ACM/IEEE-CS joint conference on digital libraries Publisher: ACM Press.
• Michael G. Christel, David B. Winkler, C. Roy Taylor, Multimedia Abstractions for a Digital Video Library, July 1997 Proceedings of the second ACM international conference on digital libraries Publisher: ACM Press.
• Petros Maniatis, Mema Roussopoulos, T. J. Giuli, David S. H. Rosenthal, Mary Baker, The LOCKSS Peer-to-Peer Digital preservation system, February 2005 ACM Transactions on Computer Systems (TOCS), Volume 23 Issue 1.
• 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.
• 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.
• L. Golab, MT Ozsu., Issues in Data Stream Management, ACM SIGMOD Record, Volume 32, Issue 2, pp. 5-14, 2003.
• C. Heinz and B. Seeger, Towards Kernel Density Estimation over Streaming Data, In Proceedings of International Conference on Management of Data, 2006.
• T. Ghanem, M. Hammad, M. Mokbel, W.  Aref, A. Emagarmid, Incremental Evaluation of Sliding-Window Queries over Data Streams, IEEE Transactions on Knowledge and Data Engineering, Volume 19, No.1, pp. 57-72, January 2007.