CS346-15 Advanced Databases
Introductory description
The aim of the module is to introduce students to current techniques, methods and results from the active field of database systems and data management. Typical topics include query planning and optimisation; transaction processing and concurrency control; big management; data warehousing and OLAP; theory of databases.
Module aims
The aim of the module is to introduce students to current techniques, methods and results from the active field of database systems and data management. Typical topics include query planning and optimisation; transaction processing and concurrency control; big management; data warehousing and OLAP; theory of databases.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
The topics will be drawn from core conceptual topics in advanced databases, and current ideas in database systems. This will be drawn from:
- Refresher on databases and modelling
- Relational algebra, tuple relational calculus, SQL, and equivalences between them
- Query planning, evaluation and optimisation
- Transaction processing, concurrency, ACID rules, OLTP
- Online analytical processing (OLAP), data warehouses
- Data storage and indexing, B-trees and hashing
- NoSQL to relax ACID rules; consistency, availability, partition tolerance
- Parallel databases, hardware and software
- Big data, MapReduce, PigLatin
- Special purpose databases, e.g. temporal, spatial, or multimedia databases
Learning outcomes
By the end of the module, students should be able to:
- - Demonstrate understanding of issues surrounding concurrency control and parallelism in data management.
- - Express queries in different forms (relational algebra, SQL, etc).
- - Devise appropriate ways to store and index data.
- - Show understanding of modern data processing paradigm such as NoSQL and MapReduce/PigLatin.
- - Explain methods suitable for particular types of data such as temporal, multimedia or spatial data.
Indicative reading list
Please see Talis Aspire link for most up to date list.
View reading list on Talis Aspire
Research element
The students do some critical thinking and research on how to compare different big data systems in terms of efficiency, scalability, and usability. This enhances the depth of their understanding of the latest technologies.
Subject specific skills
Concurrency control and parallelism in data management. Data management design and modelling.
Devising appropriate ways to store and index data.
Show understanding of modern data processing paradigms such as NoSQL and MapReduce.
Explain methods suitable for particular types of data such as temporal, multimedia or spatial data.
Analytical and computational thinking.
Methods and tools for the above
Transferable skills
Teamwork
Time management
Organisation
Problem solving
Study time
| Type | Required |
|---|---|
| Lectures | 30 sessions of 1 hour (20%) |
| Seminars | 8 sessions of 1 hour (5%) |
| Private study | 112 hours (75%) |
| Total | 150 hours |
Private study description
Background reading
Systems and programming practices
Solving problem sheets
Revision
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Students can register for this module without taking any assessment.
Assessment group D4
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Project Report | 15% | No | |
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Project report - Group work. This assignment is group work and is not, therefore, eligible for self-certification. |
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| Project Software output | 15% | No | |
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Project Software output - Group work. This assignment is group work and is not, therefore, eligible for self-certification. |
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| In-person Examination | 70% | No | |
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CS346 Exam
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Assessment group R1
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| In-person Examination - Resit | 100% | No | |
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CS346 resit exam
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Feedback on assessment
Written feedback on each assignment.
Pre-requisites
Students must have studied CS258 Database Systems or be able to show that they have studied equivalent relevant content.
Courses
This module is Optional for:
- Year 3 of UCSA-G4G1 Undergraduate Discrete Mathematics
- Year 3 of UCSA-G4G3 Undergraduate Discrete Mathematics
- Year 4 of UCSA-G4G4 Undergraduate Discrete Mathematics (with Intercalated Year)
- Year 4 of UCSA-G4G2 Undergraduate Discrete Mathematics with Intercalated Year
This module is Option list A for:
- Year 4 of UCSA-G504 MEng Computer Science (with intercalated year)
- Year 3 of UCSA-G500 Undergraduate Computer Science
- Year 4 of UCSA-G502 Undergraduate Computer Science (with Intercalated Year)
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UCSA-G503 Undergraduate Computer Science MEng
- Year 3 of G500 Computer Science
- Year 3 of G503 Computer Science MEng
- Year 3 of USTA-G302 Undergraduate Data Science
- Year 3 of USTA-G304 Undergraduate Data Science (MSci)
- Year 4 of USTA-G303 Undergraduate Data Science (with Intercalated Year)
This module is Option list B for:
- Year 3 of UCSA-G406 Undergraduate Computer Systems Engineering
- Year 3 of UCSA-G408 Undergraduate Computer Systems Engineering
- Year 4 of UCSA-G407 Undergraduate Computer Systems Engineering (with Intercalated Year)
- Year 4 of UCSA-G409 Undergraduate Computer Systems Engineering (with Intercalated Year)