Welcome to the web page of COMP 5118 - Trends in Big Data Management. This is a grad-level course for MSC and PhD students in Carleton University and the University of Ottawa. Each year we focus on some research areas in the general field of data management. These research areas change from one term to another based on how hot these research areas are. This term, we focus on the following topics: Question Answering, Data Mining, Data Cleaning, Data Integration, Graph Processing, and Blockchain. Check the schedule below to see the papers we review this term. Most of the papers we will be covering during the term are published in top-tier conferences, and are very recent. This should give us a chance to know what the research community of data management is currently working on. Psst, this will also (hopefully) give you ideas for the course project, which you should take very seriously.
Herzberg Laboratories 5433
1125 Colonel By Dr
Ottawa, Ontario K1S 5B6
613-520-2600 ext. 4254
myFirstName.myLastNameWithoutHyphen@carleton.ca
In this course, students will be reading and reviewing papers for each class. During the class, some students will be presenting the papers for the week, they and the rest of the class (including me) will be discussing these papers and our take on them. There is also a term-long project, which is worth the biggest chunk of your grade. Following is the marks breakdown:
The research project could be any of the following:
The project can be done individually or in groups (except the system implementation). However, the assessment will take into consideration how many students are in the group. E.g., if one student demonstrates contributions in her/his project that is equal to the contributions for a team of three students, students should expect a high variance in grades.
The project deliverables will be:
There will be 21 presentation throughout the term. This workload may not be evenly distributed over the students doing this class. Therefore, the student who presents one more presentation than average will get a bonus. Each presentation should be 30 to 35 minutes long, followed by a 30 to 35 minutes of discussion of the paper. The presenter should not only present the details of the paper, but also suggest the discussion points at the end of his/her presentation.
The paper reviews are due at 11:00 AM on the day of the class. The format for the review is fixed: Summary of the paper, three or more strong points, three or more weak points, and any additional comments you may have on this paper. The number of fields required is small, but you are expected to be elaborative. Theoretically, if your review is written in a Word document, it should be at least one page long in 12 pt. Your two worst reviews will not count towards your grade.
This is a seminar-based class, meaning that your participation in the class is essential. You are encouraged to ask questions, answer other students questions, give comments over the papers we discuss, etc.
Date | Topics | Papers | Speakers |
---|---|---|---|
January 8th | Course Introduction & Recent Game Changers in Data Managament | N/A | Ahmed El-Roby |
January 15th | Question Answersing |
1. Ritika Bhatia 2. Razieh Tekieh |
|
January 22nd | Question Answering Data Mining |
1. Yingjun Dai 2. Raghad Rowaida |
|
January 29th | Data Mining |
1. Emmanuel Ayeleso 2. Abdelghny Orogat |
|
February 26th | Data Cleaning |
1. Patrick Killeen 2. Segun Odunade 3. Isabelle Liu |
|
March 4th | Data Integration |
1. Anusha Umesh 2. Alex Gagnon 3. Razieh Tekieh |
|
March 11th | Data Integration Graph Processing Blockchain |
1. Emmanuel Ayeleso 2. Taslimur Rahman 3. Alex Gagnon |
|
March 18th | Graph Processing Blockchain |
1. Raghad Rowaida 2. Fathima Nizwana Yusuf 3. Ziaullah Dawrankhil |
|
March 25th | Blockchain |
1. Fathima Nizwana Yusuf 2. Mostafa Elkaterji 3. Wilfredo Tovar |