Courses Offered

FITE3010 Big Data and Data Mining

FITE3010 Big Data and Data Mining

2022-23
Instructor(s):Liu Qi
(Class A) No. of credit(s):6
Recommended Learning Hours:
Lecture: 33.0
Tutorial: 11.0
Pre-requisite(s):FITE1010 or MATH1853 or MATH2101; and COMP2119 or ELEC2543 or FITE2000
Co-requisite(s):  
Mutually exclusive with:COMP3323
Remarks:

Course Learning Outcomes

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 aPLO bPLO cPLO dPLO ePLO fPLO gPLO hPLO iPLO j
CLO 1TTT
CLO 2TTT
CLO 3TTT
CLO 4TTT

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 data1, 4
spatial networks1, 4
textual big data1
uncertain data management1
data mining2, 4
association rule mining2
data clustering2
information ranking 2
data integration2
database indexing3, 4
query evaluation3
query optimization3

Assessment:
Continuous Assessment: 50%
Written Examination: 50%

Teaching Plan

Please refer to the corresponding Moodle course.

Moodle Course(s)

Please login with your CS account (for staff only)