Module Descriptors
DISTRIBUTED PROCESSING
COIS71173
Key Facts
Digital, Technology, Innovation and Business
Level 7
15 credits
Contact
Leader: Euan Wilson
Email: E.D.Wilson@staffs.ac.uk
Hours of Study
Scheduled Learning and Teaching Activities: 36
Independent Study Hours: 114
Total Learning Hours: 150
Assessment
- PRACTICAL weighted at 50%
- REPORT (1500 WORDS APPROX) weighted at 50%
Module Details
ADDITIONAL ASSESSMENT DETAILS
Practical element weighted at 50%
Covers learning outcome 1
Report weighted at 50%: 1500 word Management style report, based on a scenario, which identifies the issues involved with the realms of distributed data processing. This covers learning outcomes 2 & 3
Report: a Management style report, based on a scenario, which identifies the issues involved with the realms of distributed data processing. This covers learning outcomes 2 & 3.
Covers learning outcome 1
Report weighted at 50%: 1500 word Management style report, based on a scenario, which identifies the issues involved with the realms of distributed data processing. This covers learning outcomes 2 & 3
Report: a Management style report, based on a scenario, which identifies the issues involved with the realms of distributed data processing. This covers learning outcomes 2 & 3.
INDICATIVE CONTENT
This module focuses on Distributed Data Processing. The module will cover parallel processing
The following will have an emphasis on distributed data processing
Business Case Evaluation
Data Identification (external and internal)
Data Acquisition & Filtering
Data Extraction
Data Validation & Cleansing
Data Aggregation & Representation
Data Analysis
Data Visualization
Utilisation of Analysis Results (alerts, application, business process optimisation)
Other related areas include:
Clusters File Systems & Distributed File Systems
NoSQL
Distributed Data Processing
Parallel Data Processing
Processing Workloads (batch and transactional)
Cloud Computing
The following will have an emphasis on distributed data processing
Business Case Evaluation
Data Identification (external and internal)
Data Acquisition & Filtering
Data Extraction
Data Validation & Cleansing
Data Aggregation & Representation
Data Analysis
Data Visualization
Utilisation of Analysis Results (alerts, application, business process optimisation)
Other related areas include:
Clusters File Systems & Distributed File Systems
NoSQL
Distributed Data Processing
Parallel Data Processing
Processing Workloads (batch and transactional)
Cloud Computing
LEARNING STRATEGIES
The module uses 12 hours of formal lectures, 12 hours of seminar style presentations and 12 hours of practical work. Extensive use is made of the VLE and of formative assessment.
RESOURCES
MS Office
Internet
NoSQL Datastores (MongoDB, CouchDB)
Hadoop Framework
Internet
NoSQL Datastores (MongoDB, CouchDB)
Hadoop Framework
TEXTS
(note that all the text listed here are available through the university e-books service. It is not expected that students will buy these texts. The nature of the subject means that these texts will be constantly updated.)
Achari, 2015 Hadoop Essential Packt Publishing
Achari, 2015 Hadoop Essential Packt Publishing
LEARNING OUTCOMES
1) Design, build and critically evaluate a proof of concept that would solve a complex issues for organisation with respect to distributed data processing issue. (Application, Analysis, Problem Solving).
2) Critically discuss distributed processes with respect to a distributed data processing issue. (Knowledge and Understanding, Learning, Enquiry).
3) Critically evaluate options to enable organisations to solve the complex issues with respect to distributed processing. (Communication, Reflection).
2) Critically discuss distributed processes with respect to a distributed data processing issue. (Knowledge and Understanding, Learning, Enquiry).
3) Critically evaluate options to enable organisations to solve the complex issues with respect to distributed processing. (Communication, Reflection).