Newcastle University

Pre Requisite Comment

N/A

Co Requisite Comment

N/A

Aims

This module will provide students with an understanding of information storage and retrieval. This relates to all forms of data, including text and multimedia (image, video and audio) stored on and consumed from the web, amongst other sources. The module covers fundamental techniques and strategies of information storage and retrieval used in a variety of online applications such as web- search engines and business storage and analytics.

Outline Of Syllabus

*       Retrieval, browsing, user information needs, and other core concerns.
*       Notions of structured, unstructured and semi-structured data
*       Data representation (XML, character sets, images, audio/video)
*       Relational databases, SQL
*       A generic architecture for information retrieval
*       Spiders/crawlers, stopwords and keywords, indexing and stemming
*       Query expansion and its relationship with the Semantic Web.
*       Metadata and semantics, faceted classifications, and other "linked data" issues
*       Information models, databases and data normalization for transactional systems (OLTP)
*       Data de-normalization, data marts / data warehouses, star and snowflake schemas, and cubes as support for analytical systems (OLAP) as support to Business Intelligence
*       The challenges presented by "Big Data"
*       NoSQL and Cloud Computing for distributed and scalable treatment of "Big Data".
*       Exemplar applications, including publishing archives, web-based search engines
*       Data Ethics

Intended Knowledge Outcomes

At the end of this module students will be able to
•       explain theories behind search and assess the impacts on search performance inherent in variations in their construction
•       elaborate a range of techniques for analysing, modelling, and retrieving data
•       contrast different kinds of applications, and their integration, in satisfying specific user information needs
•       elaborate, contrast and evaluate information models that support efficient storage, retrieval and browsing, in a variety of applications
•       contrast the need for efficiency of data storage with the needs of batch access to large datasets
•       apply appropriate, standard, metadata sets and semantics to ensure effective data storage and curation
•       identify the important features for storage, retrieval and browsing of different forms of data.

Intended Skill Outcomes

At the end of this module students will be able to apply data storage and retrieval techniques to typical systems encountered in computing, will be able to analyse and design the data needs of systems, and will be able to communicate the technical requirements and analysis. Further practical skills related to this material are developed in the co-requisite modules Portfolio 1 and Portfolio 2.

Teaching Rationale And Relationship

Techniques and theory are presented in lectures. Practical sessions provide experience of designing and building database applications and can be carried out online.
This is a very practical subject, and it is important that the learning materials are supported by hands-on opportunities provided by practical classes, and on the related Programming Portfolio modules.
The new online drop-in/clinic sessions give students additional support and chances to talk to staff members. This will include students who are not present in Newcastle.

Assessment Rationale And Relationship

The written examination in Semester 2 will assess the fundamental knowledge and understanding of Semester 2 taught material.
Semester 1 will be assessed with a piece of coursework allowing the students to apply the theory taught in lectures to a given scenario.
A mock test will take place in Semester 2 to enable the students to prepare for the examination.
The portfolio modules will also use elements from this module enabling further practice for the students.

N.B. This module has both “Exam Assessment” and “Other Assessment” (e.g. coursework). If the total mark for either assessment falls below 35%, the maximum mark returned for the module will normally be 35%.

General Notes

N/A