School of Computer Science BCS accreditation 2021 - 2026

Artificial Intelligence MEng (Hons) - COMP38211 Documents and Data on the Web

2.1.1 Knowledge and understanding of facts, concepts, principles & theories

This course unit focuses on principles and techniques that underpin the web, and to investigate how these are applied to provide webs of documents and data. In so doing, the concepts and standards associated with resource identification, access, indexing, classification, publication and search are discussed, with specific focus on scalability.

Assesement : Individual coursework, Lab work

2.1.2 Use of such knowledge in modelling and design

Coursework and labs provide experience of a new programming model for big data (MapReduce), as well as requiring design of systems for indexing and searching on the web for documents and data. Coursework specifically ask to discuss trade-offs between modelling decisions

Assesement : Individual coursework

2.1.3 Problem solving strategies

The course unit discusses specific case studies - indexing a real-world set of documents and data. Students are asked to come up with specification to address the problem and plan how to solve it. For one of the tasks, students are asked to mostly focus on identification of design challenges and come up with a balanced strategy to address these.

Assesement : Individual coursework

2.1.4 Analyse if/how a system meets current and future requirements

Students are asked to critically evaluate what their solution to a problem would support (e.g. what kind of queries), and how would any future requirements fit. They also need to understand and discuss the efficiency of proposed solutions - e.g. in the context of large number of documents to index.

Assesement : Individual coursework

2.1.5 Deploy theory in design, implementation and evaluation of systems

We introduce different MapReduce patterns for developing the methods to implement indexing of documents and data. A variety of standard and emerging methods for evaluation of information retrieval are considered.

Assesement : Individual coursework

2.1.6 Recognise legal, social, ethical & professional issues

A brief discussion on social and ethical issues when it comes to what data to index and how autonomous data representation models should be.

Assesement : Not Assessed

2.1.7 Knowledge and understanding of commercial and economic issues

Regularly covered by a guest lecture from industry, discussing practical issues in maintaining search and indexing systems.

Assesement : Not Assessed

2.2.1 Specify, design or construct computer-based systems

Students are asked to design and construct two computer-based systems for indexing sets of documents and data. These are introduced in workshops.

Assesement : Individual coursework

2.2.2 Evaluate systems in terms of quality and trade-offs

As above - this is a key request for the two coursework pieces: evaluate possible trade-offs in addressing the indexing problem, and illustrate that with real-world data.

Assesement : Individual coursework

2.2.4 Deploy tools effectively

This course unit requires students to understand and implement their solutions in a Hadoop environment, so they need to be able to demonstrate effective deployment.

Assesement : Lab work

2.3.2 Development of general transferable skills

Written reports are part of the coursework.

Assesement : Individual coursework

3.1.3 Knowledge of systems architecture

Students are introduced to Hadoop and related architectures and technologies for distributed computing.

Assesement : Not Assessed

4.1.3 Knowledge and understanding of computational modelling

We introduce different computational models for information retrieval, including language modelling.

Assesement : Individual coursework