|
| 1.
| [Recent Development in Big Data]
Able to understand the background and knowledge of some contemporary topics in Big Data; typical topics are spatial big data, spatial networks, textual big data, and uncertain data management. |
| 2.
| [Recent Development in Data Mining]
Able to understand the background and knowledge of some contemporary topics in data mining, typical topics are association rule mining, clustering, information ranking, data integration.
|
| 3.
| [Advanced Topics in Database Systems]
Able to understand the background and knowledge of some advanced topics in large database systems; typical topics are indexing, query evaluation, and query optimization.
|
| 4.
| [Application Development]
Able to implement some practical application modules based on selected advanced Big Data techniques
|
Mapping from Course Learning Outcomes to Programme Learning Outcomes
| | PLO a | PLO b | PLO c | PLO d | PLO e | PLO f | PLO g | PLO h | PLO i | PLO j |
| CLO 1 | T | T | T | | | | | | | |
| CLO 2 | T | T | T | | | | | | | |
| CLO 3 | T | T | T | | | | | | | |
| CLO 4 | T | T | T | | | | | | | |
T - Teach, P - Practice
For BEng(CompSc) Programme Learning Outcomes, please refer to
here.
|
| Syllabus |
|
Calendar Entry:
To study some important topics and techniques in big data and data mining. The teaching and learning will focus on the algorithmic and system aspects of these topics. Survey on recent development and progress in selected areas will also be included.
The course will study some advanced topics and techniques in big data, with a focus on the algorithmic and system aspects. It will also survey the recent development and progress in selected areas. Topics include: spatial-spatiotemporal data management, textual big data, uncertain data management, indexing, query evaluation and optimization, and data mining.
|
|
Detailed Description:
| Course Content |
Mapped to CLOs
|
| spatial big data | 1, 4 |
| spatial networks | 1, 4 |
| textual big data | 1 |
| uncertain data management | 1 |
| data mining | 2, 4 |
| association rule mining | 2 |
| data clustering | 2 |
| information ranking | 2 |
| data integration | 2 |
| database indexing | 3, 4 |
| query evaluation | 3 |
| query optimization | 3 |
|
|
Assessment:
Continuous Assessment:
50%
Written Examination:
50%
|
| Teaching Plan |
|
Please refer to the corresponding Moodle course.
|
| Moodle Course(s) |
|
|
