Spring 2020 Data and Information (Data Mining, Digital Libraries) Ph.D. Qualifying Examination

Exam Available January 6 (Monday), 2020

• Edward Fox
• Chang-Tien Lu (Chair, Primary Contact)
  

• 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 CS5525 Data Analytics, 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.
• Once students have notified the Computer Science Department of their intention to take the Data and Information Ph.D. Qualifier Exam, they may withdraw from taking the exam at any point prior to the public release of the exam questions. Once the exam questions are released, the exam is considered "in progress" and withdrawal is prohibited. Students with questions about this policy should contact the exam chair directly.

• The examination includes a takehome examination that is expected to be administered in the beginning of 2020.
• At the beginning of the examination period, all students will receive a document that contains two 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 8 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.

Tentative Schedule

• 12/1 (Sunday), 2019: Complete Reading List Available.
• 12/6 (Friday), 2019: Student Registration.
• 1/6 (Monday), 2020: Written Examination Available.
• 1/19 (Sunday) 10PM, 2020: Written Examination Due.
• 1/23 (Thursday) 6PM, 2020: PowerPoint Presentation File Due.
• 1/27 - 1/31 (Friday), 2020: Oral Examination.
• 2/15 (Saturday), 2020: Exam Results due to GPC.

Oral Examination Schedule (NVC R320 (Wednesday), NVC R322 (Thursday), Torgersen Hall 2030)

1. 1/29 Wednesday 10:30AM - 11:05AM: John Aromando
2. 1/29 Wednesday 11:10AM - 11:45AM: Shuo Lei
3. 1/30 Thursday 3:00PM - 3:35PM: Bipasha Banerjee
4. 1/30 Thursday 3:40PM - 4:15PM: Lulwah AlKulaib
5. 1/30 Thursday 4:25PM - 5:00PM: Fanglan Chen

Reading List

• Sweta P. Lende, M. M. Raghuwanshi. Question answering system on education acts using NLP techniques.  In Proc. 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare, 29 Feb.-1 March 2016, IEEE, DOI: 10.1109/STARTUP.2016.7583963.
• Chenchen Xu. Research on Information Retrieval Algorithm Based on TextRank. In Proc. 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC), 2019, IEEE, DOI: 10.1109/YAC.2019.8787615.
• Dietmar Wolfram. Bibliometrics, Information Retrieval and Natural Language Processing: Natural Synergies to Support Digital Library Research. In Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL), June 2016, ACL, pages 6-13, URL: https://www.aclweb.org/anthology/W16-1501.
• Carl Lagoze, Dean Krafft, Tim Cornwell, Naomi Dushay, Dean Eckstrom, and John Saylor. 2006. Metadata aggregation and "automated digital libraries": a retrospective on the NSDL experience. In Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries (JCDL '06). ACM, New York, NY, USA, 230-239. DOI: https://doi-org.ezproxy.lib.vt.edu/10.1145/1141753.1141804
• Debasis Ganguly, Dwaipayan Roy, Mandar Mitra, and Gareth J.F. Jones. 2015. Word Embedding based Generalized Language Model for Information Retrieval. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '15). ACM, New York, NY, USA, 795-798. DOI: https://doi-org.ezproxy.lib.vt.edu/10.1145/2766462.2767780
• Qingyao Ai, Liu Yang, Jiafeng Guo, and W. Bruce Croft. 2016. Improving Language Estimation with the Paragraph Vector Model for Ad-hoc Retrieval. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR '16). ACM, New York, NY, USA, 869-872. DOI: https://doi-org.ezproxy.lib.vt.edu/10.1145/2911451.2914688
  Liu, Dapeng & Li, Yan & Thomas, Manoj. (2017). A Roadmap for Natural Language Processing Research in Information Systems. DOI: 10.24251/HICSS.2017.132.
• Sujatha Das Gollapalli, Prasenjit Mitra, and C. Lee Giles. 2011. Ranking authors in digital libraries. In Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries (JCDL '11). ACM, New York, NY, USA, 251-254. DOI: https://doi.org/10.1145/1998076.1998123
• Susan Dumais, Edward Cutrell, J. J. Cadiz, Gavin Jancke, Raman Sarin, and Daniel C. Robbins. 2016. Stuff I've Seen: A System for Personal Information Retrieval and Re-Use. SIGIR Forum 49, 2 (January 2016), 28-35. DOI=http://dx.doi.org.ezproxy.lib.vt.edu/10.1145/2888422.2888425
• Alan F. Smeaton and Jamie Callan. 2005. Personalisation and recommender systems in digital libraries. Int. J. Digit. Libr. 5, 4 (August 2005), 299-308. DOI: https://doi-org.ezproxy.lib.vt.edu/10.1007/s00799-004-0100-1
• Ying Liu, Kun Bai, Prasenjit Mitra, and C. Lee Giles. 2007. TableSeer: automatic table metadata extraction and searching in digital libraries. In Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries (JCDL '07). ACM, New York, NY, USA, 91-100. DOI=http://dx.doi.org.ezproxy.lib.vt.edu/10.1145/1255175.1255193
• R. Li, S. Wang, K. Chang, “Multiple Location Profiling for Users and Relationships from Social Network and Content,” Proceedings of the VLDB Endowment, Vol. 5, No. 11, pp. 1603-6114, 2012
• R. Li, S. Wang, H. Deng, R. Wang, and K. Chang, “Towards Social User Profiling: Unified and Discriminative Influence Model for Inferring Home Locations,” Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1023-1031, 2012.
• Zhiwei Li, Bin Wang, and Mingjing Li, Wei-Ying Ma, “A Probabilistic Model for Retrospective News Event Detection,” Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 106–113, 2005.
• Eytan Bakshy, Jake M. Hofman, Winter A. Mason, Duncan J. Watts, “Everyone’s an Influencer: Quantifying Influence on Twitter,” Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 65-74, 2011
• Shuyang Lin, Fengjiao Wang, Qingbo Hu, and Philip S. Yu, “Extracting Social Events for Learning Better Information Diffusion Models,” Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 365-373, 2013.
• Shaomei Wu, Jake M. Hofman, Winter A. Mason, and Duncan J. Watts, “Who Says What to Whom on Twitter,” Proceedings of the 20th International Conference on World Wide Web, pp. 705-714, 2011.
• Wei Wang, Yue Ning, Huzefa Rangwala, Naren Ramakrishnan, “A Multiple Instance Learning Framework foIdentifying Key Sentences and Detecting Events,”Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM’16), pp. 509-518, Indianapolis, IN, USA. Oct. 24-28, 2016.
• F. Chen, and D. B. Neill, “Non-parametric Scan Statistics for Event Detection and Forecasting in Heterogeneous Social Media Graphs,”Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1166-1175, ACM, 2014.
• Yue Ning, Sathappan Muthiah, Huzefa Rangwala, Naren Ramakrishnan, “Modeling Precursors for Event Forecasting via Nested Multi-Instance Learning,”Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’16), pp. 1095-1104, San Francisco, CA, 2016.
• Yue Ning, Rongrong Tao, Chandan K. Reddy, Huzefa Rangwala, James C. Starz, Naren Ramakrishnan, “STAPLE: Spatio-Temporal Precursor Learning for Event Forecasting,” Proceedings of the 18th SIAM International Conference on Data Mining (SDM’18). San Diego, CA, USA. May 3-5, 2018. DOI:10.1137/1.9781611975321.17