Welcome to the web page of COMP 5118 - Trends in Big Data Management. This is a grad-level course for students in Carleton University and the University of Ottawa. Each year we focus on some research topics in the general field of data management. These research topics change from one course offering to another depending on what's new and hot. This term, we focus on the following topics: Social Media Data, Text-to-SQL, Internet of Things, Entity Matching, Data Lakes, Sentiment Analysis, Anomaly Detection, Time Series, AI Applications in Geospatial Data, AI Applications in Football, AI Applications in Football, AI Applications in Medical Data, Question Answering, Database Tuning, Data Discovery, Knowledge Graphs, and Document Search.. Check the schedule below to see the list of papers that we will discuss 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 rough idea of 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.
The class is on Tuesday from 11:35 am to 2:25 pm. The class will take place in RB2308. If an in-person is not possible for any reason, the class will be held via Zoom.
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 myself) will be discussing these papers. There is also a term-long project, which is worth the biggest chunk of your grade. Following is the grade breakdown:
The project could be any of the following:
The project can be done individually or in groups. However, the assessment will take into consideration how many students are in the group.
The project deliverables will be:
There will be 19 presentations 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 45 minutes long, followed by a 30 to 45 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 their 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.
Here are a few comments to consider when you write your reviews:
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 |
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September 12 | Course Introduction | N/A | Ahmed El-Roby |
September 19 | System Design | Vaishnavi Dinesh. | |
September 26 | Graph Processing Data Analytics |
1. Kishore Vanapalli. 2. Shuvankar Saha. |
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October 3 | AI Applications in Football |
1. Abdel Qayyim. 2. Evan Pierce. |
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October 10 | NO CLASS. |
N/A. |
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October 17 | AI Applications in Football Internet of Vehicles |
1. Jola Ajayi. 2. Ayomide Awonaya. 3. Stephen Akinpelu. |
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October 24 | NO CLASS (Fall Break) |
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October 31 | Knowledge Graphs |
1. Yansong Li. 2. Gurkirat Dhatt. 3. Kailash Balakrishnan. |
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November 7 |
AI Applications in Medical Data AI Applications in Geospatial Data Knowledge Graphs |
1. Sam Serdah. 2. Moses Muwanga. 3. Zabih ur Rehman Bilal. |
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November 14 |
Entity Matching Database Tuning |
1. Xuanyu Su. 2. Mohamed Basyouni. |
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November 21 |
Data Discovery Text-to-SQL Document Search |
1. Kaniz Sinethyah. 2. Amy Wang. 3. Alex Leslie. |
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November 28 |
Anomaly Detection Social Media Data |
1. Owen Brouse. 2. Hashim Awan. |
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December 5 |
Data Lakes Sentiment Analysis |
1. Swapneeth Gorantla. 2. Kamal Chahrour. |
Papers Pool |
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