Introductory description

This module builds on the CS258 Database Systems module to provide a theoretical explanation of how queries are processed by Database Management Systems, and how such techniques can be extended to working with Big Data.

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 (15%)
Seminars	8 sessions of 1 hour (4%)
Private study	112 hours (57%)
Assessment	45 hours (23%)
Total	195 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 D5

	Weighting	Study time	Eligible for self-certification
Report	30%	45 hours	No
Written report based on a programming task. This assessment is worth more than 3 CATS and is, therefore, ineligible for self-certification.
In-person Examination	70%		No
CS346 Exam Answerbook Pink (12 page)

Assessment group R2

	Weighting	Study time	Eligible for self-certification
In-person Examination - Resit	100%		No
CS346 resit exam Answerbook Pink (12 page)

Feedback on assessment

Written feedback on each assignment.

Past exam papers for CS346

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)
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)

CS346-15 Advanced Databases