DATABASE SYSTEMS - 2017/8
Module code: COMM051
MOSCHOYIANNIS S Dr (Computer Sci)
Number of Credits
FHEQ Level 7
Module cap (Maximum number of students)
Overall student workload
Independent Study Hours: 112
Lecture Hours: 22
Laboratory Hours: 16
|Assessment type||Unit of assessment||Weighting|
|Examination||2 HOUR EXAM||50|
Individual coursework instead of the group coursework.
Prerequisites / Co-requisites
A key aspect of business operations today, across sectors almost, has to do with gathering the right type of data and storing it in a way that it can be readily available to the right person at the right time. This course looks into the techniques that allow us nowadays to define and operate on large volumes of data as and when it is created. This paves the way for making more intelligent uses of data, whether this has to do with correctness (reliability and consistency) or informing more strategic decisions of the business so it can better prepare itself for the future.
The main aim of this module is to equip students with the principles and skillset necessary to take an informal textual specification of the requirements of a business and design and develop a database system that fufills those business operational needs. In addition, the module looks at three more advanced areas that build on relational databases, namely transactions and recovery, distributed databases, and business intelligence.
|Model and structure data of a business so it can be stored in a database||KC|
|Design, optimise and implement a database system, given a set of business requirements found in a textual requirements specification document||KCP|
|Develop and apply transaction processing techniques for ensuring consistency of the database in the face of concurrent operations on data||CPT|
|Appreciate key concepts of distributed database sytems and critically evaluate different distributed architectures||KCT|
|Understand business intelligence solutions and the subtle differences between designing database systems that support transactions and designing databases to support BI solutions.|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Introduction to database systems:
The ANSI/SPARC Architecture
Database Management Systems
Relational Database Design – data definition
The Relational Model of data
Database schema and Database instance
Entity Relationship (ER) Modelling
Designing relations from ER Models
Normalisation of a database schema - functional dependency, Normal Forms
Relational Database Design – data manipulation
Implementation – defining and querying data
DDL: implementing a database schema using SQL
Relational algebra operations in SQL
DML: implementing data manipulations using SQL
Defining and running transactions using SQL
Transactions - ACID properties
Recovery (Shadow paging, Rollback)
Concurrency anomalies (lost update, dirty data, inconsistent analysis)
Serialisation and scheduling
Locking schemes – S/X Lock, 2PL
Centralised vs Truly distributed database systems
Business Intelligence – an introduction
Decision making support
Denormalisation – Star and Snowflake database schema
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
Provide students with the key principles and concepts that underline the successful design and implementation of database systems today. The lectures are designed in a way that encourages students to interact with the material and develop a critical evaluation of the various options as well as an analytical thinking of ‘what’, ‘when’ and ‘how’ in database systems. This helps the students develop problem solving skills when it comes to designing and implementing a database system in response to specific requirements.
A number of other modules in the programme, including the MSc Dissertation will most likely require that the student can define and handle (modify, query) data.
Enable students to apply relevant technologies and tools for the development of database systems, transactions and distributed database designs.
The learning and teaching methods include:
Lectures (10 weeks, 2h each week) using detailed lecture slides and hand-on exxercises to enahnve student engagement and gauge student understanding
Labs (7-8 weeks, 2h each week) using database system sheets and computing labs
Students will be expected to distribute the remaining workload on self-study, preparation for lectures and labs, preparation and submission of the group coursework, preparation for the exam.
The assessment strategy is designed to provide students with the opportunity to demonstrate that they have achieved the module learning outcomes.
Thus, the summative assessment for this module consists of:
Group coursework or individual coursework as an alternative form of assessment with a set of theoretical and practical tasks.
This addresses LO1, LO2 and LO3.
An exam with a set of questions that students are required to answer.
This addresses LO1, LO4 and LO5.
Formative assessment and feedback
Lecture slides are used extensively in the lectures with each lecture consisting of a number of slides explaining the theory together with exercises and sample solutions. Possible solutions to lab exercises are discussed witrh each student during the lab session and sample solutions are released the week after on SurreyLearn.
Reading list for DATABASE SYSTEMS : http://aspire.surrey.ac.uk/modules/comm051
Programmes this module appears in
|Information and Process Systems Engineering MSc||2||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Information Security MSc||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Information Systems MSc||2||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2017/8 academic year.