Standardized Coding Systems

December 10, 2018

GHOST DOG

December 11, 2018

Big Data Systems and Analytics

Coursework Exercises on the design of (big data) system architectures length: 2,000 words equivalent)

Exercise 001 Big Data Systems

Each group will define a data-rich topic for which data resources are benchmarked or evidenced availability. The topic should be captured in a problem statement that will describe data resource(s) and data capture, storage and processing challenges.

This will be a combined group exercise with each member of the group taking one system for critical assessment, and then the group will provide a critical review of the state of the art big data solutions relevant for the topic of choice, and a critical evaluation of each individual work into a comparative summary document.

Each group member is required to review one of the (big data) systems available for the big data applications nowadays (such as in the Kovacs comparison

(https://kkovacs.eu/cassandra –vs –mongodb –vs –couchdb –vs –redis ) and relevant for the group topic.

Requirements: A non-exclusive list of areas to be covered would be:

Problem statement and specifications;
Architecture, platforms and availability of the system;
Language(s) it is available in and can work with;
Existing users and use areas;
Critical Review of documentation and support for the system;
Review of others comments/experiences of the system;
Your own experience of downloading/building the system and using it with a data set that shows big data features (with evidence).
Any other issues such as: ethical, security, data protection features and coverage.
Examples and facts could be attached as appendices.

COS7006-B Big Data Systems and Analysis CW001 Marking Scheme

Criteria	A: 70-100%	B: 60-70%	C: 50-60%	D: 40-50%	E: 0-40%
Specifications, Background, Introduction, literature [10]	Excellent report structure, review of the problem domain.	Good structure. Well-performed review of the problem domain.	Sensible structure. review of the problem domain.	Reasonable structure. Some review of the problem domain.	Weak review of the problem domain.
Details of each system [40] Evidencebased system approach [10]	Excellent and consistent critical evaluation of strengths and weaknesses including own and others assessments.	Good and consistent critical evaluation of strengths and weaknesses including own and others assessments.	Some evaluation of strengths and weaknesses including own and others assessments. Less consistent approaches across systems	Some coverage of system design, deployment and any issues. Inconsistent approaches across systems	Poor coverage of system design, deployment and any issues. Inconsistent approaches across systems
Comparative Evaluation [20]	Excellent comparison of the systems.	Good comparison of the systems.	Some comparison of the systems.	Limited comparison of the systems.	Weak or no comparison of the systems.
Other Academic or Industry (White) Paper Writing Features [10]	Excellent conclusion and references.	Good conclusion and references.	Some conclusion and references	Weak conclusion and references.	No conclusion or references.
Other TeamBased Issues [10]	Excellent contribution and engagement.	Good contribution and engagement.	Some contribution and engagement.	Weak contribution and engagement.	No evidence of contribution and engagement.

Data is from kaggle.com and the medical cost personal datasets.

Hadoop

Standardized Coding Systems

GHOST DOG

Exercise 001 Big Data Systems