Module: Mechanics of Search

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Mechanics of Search
Module Code	CSC1165 (ITS: CA6005I)
Faculty	Computing	School	Engineering & Computing
NFQ level	9	Credit Rating	7.5

Description

The aim of this module is to present the student with the technical background necessary to understand how to effectively develop information retrieval systems and forward-thinking strategies to analyse personal and online data to develop enhanced information access, support and communication strategies. The module will firstly provide a thorough grounding in the mechanics of how information access and retrieval systems operate. The student will be presented with the main theoretical models behind search engines to understand their main limitations and strengths. The module will then explore how to mine knowledge from multimedia and individual data to support next-generation content search, recommendation and personalisation. New devices and methods will be considered here also. The final section of the module explores new directions and challenges in the search and information access landscape. The student will finish the course with a solid understanding of search and personal data, and utilising this information to communicate with the individual.

Learning Outcomes

1. Understand the theory of Information Retrieval and appraise different retrieval models with their differences and similarities.
2. Understand the main components behind search engines, how they work together, how they differ when applied on the Web.
3. Illustrate the main components of a retrieval evaluation and design an experiment for a specific domain of utilisation.
4. Discuss the utilisation of ML in information retrieval and formulate a specific solution applied on a given context and domain.
5. Develop an understanding of the challenged posed by multimedia retrieval and develop specific solutions to tackle with multimedia content, such as image, audio and video.
6. Discuss the role of the user in an information retrieval system and debate the new role played in search engines by social media and personal data.
7. Formulate specific search solutions based on the exploitation of standard retrieval techniques and more advanced use of personal, social and sensory data.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Directed learning	36	Online learning material on FutureLearn
Independent Study	151	No Description
Total Workload: 187

Section Breakdown
CRN	21210	Part of Term	Semester 2
Coursework	0%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	Y
Resit Category	RC1	Best Mark	N
Module Co-ordinator		Module Teacher

Type	Description	% of total	Assessment Date
Assessment Breakdown
Project	Design and Implementation of a textual IR search engine This project assessment will require the student to design, develop and evaluate a desktop search engine by implementing VSM, BM25 and LM retrieval models. The student will also generate a report documenting the whole process explaining the pros/cons of each adopted choice. The student cannot make use of public available API for indexing and retrieval (such as Lucene, Solr, Indri, Lemure, MG4J, etc.). The student will be provided with a small reference text collection with which to do the experiments.	50%	Week 5
Project	Using an existing open-source search engine (e.g Lucene, Solr), implement a multimedia (image search engine) utilizing available metadata and existing APIs for automatic annotation, with appropriate user interface motivated by state-of-the-art multimedia interactive retrieval systems. The student will write a report and demonstrate their search engine.	50%	Week 10

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Indicative Reading List

Books:

Gurrin, Cathal , Smeaton, Alan F. and Doherty, Aiden R.: 2014, LifeLogging: personal big data, 1, Foundations and Trends in Information Retrieval, 1554-0677

Articles:
None

Other Resources

None