COMP7106A - Big data management

Semester 2, 2021-22

Professor
Reynold C.K. Cheng
Teaching assistant
Chenhao Ma
Syllabus The course will study some advanced topics and techniques in Big Data.  It will also survey the recent development and progress in specific areas in big data management and scalable data science.  Topics include but not limited to: large database management techniques, spatial data management and spatial networks, data quality and uncertain databases, top-k queries, graph and text databases, and data analytics.
Introduction by Professor The course will study some advanced topics and techniques in Big Data.  It will also survey the recent development and progress in specific areas in data management and scalable data science.  Topics include but not limited to: database management techniques, spatial databases and spatial networks, uncertain databases, top-k queries, graph and text databases, and data analytics.
Learning Outcomes
Course Learning Outcomes Relevant Programme Learning Outcome
CLO1. Able to understand the background and knowledge of some advanced topics in Big Data that have become key techniques in modern database theory and practices PLO. 4, 5, 6, 7, 8, 9, 10, 11, 14
CLO2. Able to understand the background and knowledge of some contemporary topics in Big Data research (e.g., uncertainty data management, text, and graph databases) PLO. 4, 5, 6, 7, 8, 9, 10, 11, 14
View Programme Learning Outcomes
Pre-requisites The student is expected to have taken a course on introduction to databases (e.g., COMP3278), and has some background in statistics and calculus.
Compatibility Students who have obtained credits for COMP7801 in the academic year 2019-20, 2020-21 are not allowed to take COMP7106.
Topics covered
Course Content No. of Hours Course Learning Outcomes
1. Review of database design, queries, and indexing 3 CLO1
2. Spatial data management 6 CLO1, CLO2
3. Spatial networks 3 CLO1, CLO2
4. Ranking queries 6 CLO1
5. Text Databases 3 CLO1, CLO2
6. Uncertain databases 3 CLO1, CLO2
7. Probabilistic Queries 3 CLO1, CLO2
8. Uncertain data mining 3 CLO2
 
Assessment
Description Type Weighting * Tentative Assessment Period /
Examination Period ^		 Course Learning Outcomes
Assignments and quizzes Continuous Assessment 50% - CLO1, CLO2
Written examination covering all the taught contents in the course. Written Examination 50% 10 - 28 May 2022 CLO1, CLO2
* The weighting of coursework and examination marks is subject to approval
^ The exact examination date uses to be released when all enrolments are confirmed after add/drop period by the Examinations Office.  Students must oblige to the examination schedule. Students should NOT enrol in the course if they are not certain that they will be in Hong Kong during the examination period.  Absent from examination may result in failure in the course. There is no supplementary examination for all MSc curriculums in the Faculty of Engineering.
Course materials

Recommended readings: (no textbook; papers and other references will be given to students when appropriate) 
Session dates
Date Time Venue Remark
Session 1 22 Jan 2022 (Sat) 9:30am - 12:30pm Online Zoom
Session 2 29 Jan 2022 (Sat) 9:30am - 12:30pm Online Zoom
Session 3 12 Feb 2022 (Sat) 9:30am - 12:30pm Online Zoom
Session 4 19 Feb 2022 (Sat) 9:30am - 12:30pm Online Zoom
Session 5 26 Feb 2022 (Sat) 9:30am - 12:30pm Online Zoom
Session 6 5 Mar 2022 (Sat) 9:30am - 12:30pm Online Zoom
Session 7 19 Mar 2022 (Sat) 9:30am - 12:30pm Online Zoom
Session 8 26 Mar 2022 (Sat) 9:30am - 12:30pm Online Zoom
Session 9 2 Apr 2022 (Sat) 9:30am - 12:30pm Online Zoom
Session 10 9 Apr 2022 (Sat) 9:30am - 12:30pm Online Zoom
Add/drop 17 January, 2022 - 31 January, 2022
Maximum class size 100
Back