INFORMATION RETRIEVAL - 2022/3
Module code: COM2034
In light of the Covid-19 pandemic the University has revised its courses to incorporate the ‘Hybrid Learning Experience’ in a departure from previous academic years and previously published information. The University has changed the delivery (and in some cases the content) of its programmes. Further information on the general principles of hybrid learning can be found at: Hybrid learning experience | University of Surrey.
We have updated key module information regarding the pattern of assessment and overall student workload to inform student module choices. We are currently working on bringing remaining published information up to date to reflect current practice in time for the start of the academic year 2021/22.
This means that some information within the programme and module catalogue will be subject to change. Current students are invited to contact their Programme Leader or Academic Hive with any questions relating to the information available.
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.
BAUER Roman (Computer Sci)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 5
JACs code: I100
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 106
Laboratory Hours: 22
Captured Content: 22
Prerequisites / Co-requisites
· 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.
· Databases, data normalization and de-normalization.
· 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.
|Assessment type||Unit of assessment||Weighting|
|Examination Online||24 HOUR ONLINE EXAM||100|
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.
- 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.
|1||Explain theories behind search and assess the impacts on search performance inherent in variations in their construction||KC|
|2||Elaborate a range of techniques for analysing, modelling, and retrieving text documents||KCT|
|3||Contrast different kinds of applications, and their integration, in satisfying specific user information needs||KCT|
|4||Elaborate, contrast and evaluate information models that support efficient storage, retrieval and browsing, in a variety of applications.||KC|
|5||Contrast the need for efficiency of data storage with the needs of batch access to large datasets||KCT|
|6||Apply appropriate, standard, metadata sets and semantics to ensure effective data storage and curation.||KP|
|7||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
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•
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: COM2034
Programmes this module appears in
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 2022/3 academic year.