INFORMATION RETRIEVAL - 2017/8
Module code: COM2034
GILLAM L Dr (Computer Sci)
Number of Credits
FHEQ Level 5
Module cap (Maximum number of students)
Overall student workload
Independent Study Hours: 120
Lecture Hours: 24
Laboratory Hours: 11
|Assessment type||Unit of assessment||Weighting|
|Examination||EXAM 2 HOURS||80|
Prerequisites / Co-requisites
This module will provide students with an understanding of information retrieval. This relates to multimedia data (principally text, but also image, video and audio) stored for, presented on, and consumed from, the web amongst other sources. The module covers fundamental techniques and strategies of information retrieval used in a variety of online applications such as web-search engines, document matching systems, and business storage and analytics.
Help students to gain an understanding of the current study of information retrieval
Provide practical understanding of how data are represented for storage, analysis and use in particular applications.
|Explain theories behind search and assess the impacts on search performance inherent in variations in their construction||KC|
|Elaborate a range of techniques for analysing, modelling, and retrieving text documents||KCT|
|Contrast different kinds of applications, and their integration, in satisfying specific user information needs||KCT|
|Elaborate, contrast and evaluate information models that support efficient storage, retrieval and browsing, in a variety of applications.||KC|
|Contrast the need for efficiency of data storage with the needs of batch access to large datasets||KCT|
|Apply appropriate, standard, metadata sets and semantics to ensure effective data storage and curation.||KP|
|Identify the important features for storage, retrieval and browsing of non-textual data||KCT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
· Retrieval, browsing, user information needs, and other core concerns.
· Notions of structured, unstructured and semi-structured data
· A generic architecture for information retrieval
· Spiders/crawlers, stopwords and keywords, indexing and stemming.
· Boolean retrieval, ranked retrieval, and vector spaces
· Query expansion and its relationship with the Semantic Web.
· Assessing relevance - precision and recall
· 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”.
· Image and video features and classifications that enable access to other media types
· Exemplar applications, including web-based search engines, organisation-wide archives, business data collections, and media collections.
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
Develop an understanding for the principles and role of information retrieval and closely related applications
The learning and teaching methods include:
· Lectures, including case studies•
· Occasional set reading•
· In-class discussions
· In-class and out-of-class exercises•
· Lab sessions•
The assessment strategy is designed to provide students with the opportunity to demonstrate :
Explaining theories behind search and assess the impacts on search performance inherent in variations in their construction
Elaborating a range of techniques for analysing, modelling, and retrieving text documents
Contrasting different kinds of applications, and their integration, in satisfying specific user information needs
Elaborating, contrasting and evaluating information models that support efficient storage, retrieval and browsing, in a variety of applications.
Contrasting the need for efficiency of data storage with the needs of batch access to large datasets.
Applying appropriate, standard, metadata sets and semantics to ensure effective data storage and curation.
Identifying the important features for storage, retrieval and browsing of non-textual data
Thus, the summative assessment for this module consists of:
A coursework that will involve applying and evaluating various concepts and principles introduced in lectures and tested in lab sessions. Specific software and analytical approaches will be explored in these assessments. Submissions will be made through the VLE, with the deadline towards the end of the module. The coursework may assess against all relevant learning outcomes addressed suitably in advance of the deadline.
2-hour written unseen written examination comprising a mixture of short answer and discussion questions. The examination paper may assess against all learning outcomes.
Formative assessment and feedback
Students will be progressively completing structured lab workbooks where submission of each is necessary to progress to the next. On submission, informative solutions are also provided such that students will be able to gauge their progress as the module progresses.
Reading list for INFORMATION RETRIEVAL : http://aspire.surrey.ac.uk/modules/com2034
Programmes this module appears in
|Computer Science BSc (Hons)||1||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Computing and Information Technology BSc (Hons)||1||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Biomedicine with Data Science BSc (Hons)||1||Optional||A weighted aggregate mark of 40% is required to pass the module|
|Data Science for Health BSc (Hons)||1||Compulsory||A weighted aggregate mark of 40% 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.