Modern Database Applications

Please note

This document only provides information for the academic year selected and does not form part of the student contract

School:

School of Computing and Engineering

Credit Rating:

Level (including FHEQ):

H (FHEQ Level 6)

Graded or Non Graded:

Graded

Version Valid From:

2022-09-01

Module Leader:

Colin Venters

Version Number

2023.01

Learning Methods

Guided Independent Study

Seminar

Synopsis

The amount of digital data generated across the globe is increasing exponentially. As a result, the information needs of modern organisations require a more flexible approach to data management than that offered by traditional relational databases. This module introduces you to alternative approaches to data modelling including hierarchical, network, object-oriented … For more content click the Read More button below.

Learning Strategy

This module is seminar-based and takes a very practical and applied approach to understanding the topic. Throughout this module students will be supported in acquiring a solid foundation in order to develop the analytical and problem-solving skills required to model complex data problems. Each week the students will be given a problem to analyse using a range of techniques introduced throughout the course of the sessions including materials to read to help them solve the problem. The problem solutions and reading materials will be discussed in sessions summarizing fundamental principles discovered while solving the problems. The emphasis will be on developing the students' practical skills and their ability to communicate the solution with clarity and precision. Students will be provided with a recommended reading list including books, book chapters, journals, conference papers and links to online resources. They will receive weekly guidance on where to focus this reading and advice on how it links to the module content. The University’s Virtual Learning Environment (VLE) will be used to support and supplement individual learners’ requirements.

Outline Syllabus

DataBig DataDatabasesRelational Data ModellingNetwork Data ModellingObject Oriented Data ModellingData ProvenanceData LakesData Warehouses

Learning Outcomes

On successful completion of this module students will

Critically analyse and evaluate competing data models and database technologies.

Critically analyse, synthesise and evaluate material on a variety of advanced topics concerned with database information systems.

Produce effective designs for non-relational databases using an appropriate design language (e.g. UML, ORM, etc.).

Use a non-relational DBMS, and appropriate data languages (e.g. SQL, XML Schema, Relax NG, XQuery, etc.) to implement a non-relational database.

Formative Assessment

Assessment 1: Practical skills assessment

Summative Assessment

Assessment 1: Portfolio

Description:Working individually, design data models for the problem domain producing a written report (around 2000 words) and a presentation (10 minutes), which brings together the theory and practice of advanced data modelling, including a reflection on the Legal, Ethical, Social, Professional, Equality, Diversity and Inclusivity issues.

Assessment Weight:50

Assessment Length:20 Hours (Typical Effort)

Module Learning Outcomes:MLO3, MLO4

Individual or Group:Individual

Final Assessment Component:No

Marked Anonymously:No

Eligible for Tutor Reassessment:No

Assessment 2: In-Class Test

Assessment Criteria

A detailed grading rubric for each assessment point gives a clear indication of how achievement of learning outcomes has been demonstrated, thereby promoting reliability and transparency of assessment including: Identifies the core concepts in the domainOverall quality of the domain analysisOverall quality of the domain modelDemonstrates a technical understanding of … For more content click the Read More button below. The presentation component is assessed based on the level of knowledge of theory and the ability of linking theory to practice.

Identifies the core concepts in the domain
Overall quality of the domain analysis
Overall quality of the domain model
Demonstrates a technical understanding of the method(s)
Clearly identifies the strengths and advantages of the methods
Clearly identifies the disadvantages and limitations of method

The presentation component is assessed based on the level of knowledge of theory and the ability of linking theory to practice.

Course and Module Catalogue