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Course Syllabus: CS591 K1 Data Stream Processing and Analytics (Spring 2021)

Instructor Name: Vasiliki Kalavri
Contact Information: Please use Piazza for all communication.
Class Time & Location: Tue/Thu 9:30-10:45, MCS B31 & Zoom
Office Hours: Mon 9:00-10:00, Fri 9:00-11:00, (Zoom)

Courseware

• We will use the course website to maintain an up-to-date class schedule.
• We will use Piazza for announcements, questions, discussions, and all other communication.
• We will use Gitlab for the hands-on sessions, the semester projects, and homework submission.

Course Description

For a description of topics covered in this class, please see the overview in the course website. During the semester, you will learn about the following three aspects of stream processing:

• Systems
  • Architecture and design
  • Scalability and elasticity
  • Fault-tolerance and processing guarantees
  • State management
• Algorithms
  • Windowing semantics and optimizations
  • Basic data stream mining
  • Probabilistic streaming algorithms
• Streaming applications and use-cases

Course Objectives

The course aims at training students in fundamental and emerging topics of data stream processing. At the end of the course, successful students will have acquired the necessary knowledge in order to:

• know when to use stream processing
• be able to comprehensively compare the features and processing guarantees of streaming systems
• efficiently use Apache Flink and Kafka to build end-to-end, scalable, and reliable streaming applications
• have a solid understanding of how stream processing systems work and what factors affect their performance
• be aware of the challenges and trade-offs one needs to consider when designing and deploying streaming applications

Course Materials

There is no required textbook for this class. After each lecture, slides will be published on the course website, in the “Slides” column of the lecture schedule. Further publicly available resources are listed in the course website, under “Readings”. Parts of the resources listed there will be given as reading assignments during the course. You should be able to access all of these for free when connected to the campus network.

Class Schedule

Please see the schedule on the course website.

Lectures and Office Hours

Lectures will be held during the assigned time slots. The professor will be physically present in the lecture room. We will provide a Zoom link for remote attendance and the lectures will be recorded (links will be posted on Piazza).

All office hours will be held on Zoom and the links will be posted on Piazza. If the designated times do not work for you, please contact the Professor via a private Piazza post to schedule an alternative meeting.

Classroom recordings. All class sessions will be recorded for the benefit of registered students who are unable to attend live sessions (either in person or remotely) due to time zone differences, illness or other special circumstances. Recorded sessions will be made available to registered students ONLY via their password-protected BU account. Students may not share such sessions with anyone not registered in the course and may certainly not repost them in a public platform. Students have the right to opt-out of being part of the class recording. Please consult the following site for further details: https://digital.bu.edu/lfa-classroom-recordings.

Semester project

This class is highly collaborative and research-oriented. During the first week, you will be provided with a list of semester projects and you will be asked to select your top-3 preferences. You will then be assigned to a project team with 3 or 4 students. During the course of the semester, the team will be working together to deliver:

1. A design document outlining (1) the project goals, (2) an implementation and evaluation plan, (3) the task distribution among team members.
2. A midterm project demo. Demos will be presented (live or pre-recorded) during class time on March 9.
3. A final demo and poster to be presented during the last day of class.
4. The project’s gitlab repository, including code, tests, automation and plotting scripts, and documentation.

Paper assignment

Every team will be assigned a research paper to read, review, and present. Paper assignments consist of the following deliverables:

1. A written review, to be completed and submitted individually.
2. A collaborative paper presentation. Every team will present a research paper in class on their assigned date. Presentations will be 40’-45’ long and can be delivered live or in pre-recorded format.

Grading Scheme

The course consists of lectures, hands-on sessions, paper presentations, and a final semester project. There is no formal examination at the end of the course. Your final grade will be determined as follows:

• Participation & effort (10%):
  • In-class discussions.
  • Piazza contributions.
  • Office Hours participation.
  • Git activity.
• Paper assignment (20%):
  • Written review 10%.
  • Oral presentation 10%.
• Semester project (70%) (in teams of 3-4 students):
  • Design document (maximum 3 pages) 10%.
  • Midterm demo 20%.
  • Final demo and deliverables: 40%. The final deliverables include (1) the full code implementing the project tasks as defined in the project design document, (2) auxiliary code for data pre-processing, deployment, and testing, (3) complete supporting documentation.

Individual contributions to collaborative assignments will be assessed by taking into account the following:

• The quality of individual task deliverables outlined in the projet design document.
• The individual’s ability to answer questions about the project during demo presentations and office hours.
• The individual’s performance during the paper and demo presentations.
• The individual’s contribution to the project’s gitlab repository (git history).

Homework submission

All assignments and the project deliverables will be submitted via the course Gitlab. All deliverables are due by latest 11:59pm on the day of the respective deadline.

Late work policy

Students who submit homework late will only be eligible for up to 50% of the original score.

Attendance

Students are expected to attend each class session unless they have a valid reason for being absent. Acceptable excused absences include observing religious holidays and illness. In such cases, students are advised to contact the instructor as soon as possible, so that reasonable accommodations can be provided. Please review the BU attendance policy and the BU Policy on Religious Observance for more information.

Academic Conduct

All assignments must be completed individually, unless instructed otherwise. Discussion with fellow students via Piazza or in-person are encouraged, but presenting the work of another person as your own is expressly forbidden. This includes “borrowing”, “stealing”, copying programs/solutions or parts of them from others. Note that we may use an automated plagiarism checker. Cheating will not be tolerated under any circumstances.

Please review the BU Academic Conduct Code for more information.

Supporting Students With Disabilities

If you are a student with a disability or believe you might have a disability that requires accommodations, please contact the Office for Disability Services (ODS) at (617) 353-3658 or access@bu.edu to coordinate any reasonable accommodation requests. ODS is located at 25 Buick Street on the 3rd floor.