DEGREE: BSc Computer Science and Digitisation
Module: Immense Records Analytics the usage of AI
Assignment Title:
Assignment Form: Characterize
Word Restrict: 3000 words (+/- 300)
Weighting: 100%
Anguish Date: 4/9/2025
Submission Date: 30/9/2025
Feedback Date: 21/10/2025
Plagiarism:
When submitting work for review, students wants to be attentive to the InterActive/Canvas steering and regulations in pertaining to plagiarism. All submissions wants to be your comprise, fashioned work. Please designate that you simply might presumably well want gotten to not post the identical project for 2 varied modules inner your route.
You will be pleased to post an electronic reproduction of your work. Your submission will be electronically checked.
Harvard Referencing:
The Harvard Referencing Machine ought to be ragged. The Wikipedia, UKEssays.com or an identical web sites must not be ragged or referenced to your work.
Pupil signature: ______________________ Date: _______________
SmartCity UrbanMobilityAnalysisusingHadoopand Predictive AI
Introduction
Part 2: Implementation & Diagnosis (LO 3) – 50% of Total Grad
LO1. Point to the working out of fashioned ideas of Immense Records, its significance and desire in industry context.
LO2. Be aware the many components of Hadoop and HFDS along with their role within the Immense Records ecosystem.
LO3. Summarize the discovering out on Immense Records analytics the usage of Yarn, HDFS and MapReduce.
Overview Criteria: Weighting 100%
3000 words
Job Description:
You’re a Records Engineer tasked with designing and imposing a proof-of-concept Immense Records analytics resolution for a metropolis’s transport authority.
Scenario:
The metropolis council wants to analyse metropolis mobility patterns the usage of data from road sensors, taxi trips, or public transit records. The blueprint is to name congestion hotspots, realize their causes, and predict future visitors patterns to enable proactive visitors management and better infrastructure planning.
Part 1: Conceptual Originate & Structure (LO 1, LO 2) – 20% of Total Grade
The blueprint of this project is to produce you with hands-on abilities in designing and imposing a Immense Records analytics resolution that comprises a predictive AI component. You are going to tackle a hypothetical tidy metropolis agonize by the usage of the Hadoop ecosystem to process substantial-scale data and derive actionable insights for metropolis planning. This project requires you to create a resolution the usage of Hadoop, HDFS, YARN, and MapReduce to analyse transportation data. The closing step involves the usage of the processed data to practice a straightforward predictive model, thereby connecting Immense Records processing with AI applications. It might presumably presumably encourage you to achieve the pause-to-pause pipeline from raw data to industry intelligence in a most modern context.
Studying Outcomes:
1. Enterprise Context and Anguish Assertion (5%)
• Affirm the tidy metropolis effort, specializing within the challenges of metropolis mobility.
• Interpret a determined agonize explain.
• Be aware how solving this agonize provides tangible payment to the metropolis.
2. Hadoop Ecosystem and Structure (15%)
• Be aware why a Immense Records methodology is serious for this effort.
• Title the roles of HDFS, YARN, and MapReduce to your proposed resolution.
• Interpret your preference of these components for the outlined agonize.
• Make a determined architectural plan illustrating how data flows from provide to HDFS, is processed by MapReduce managed by YARN, and is then ragged for prognosis.
Submission Pointers:
1. Records Acquisition & Preparation (5%)
• Pick out a accurate public dataset representing metropolis mobility.
• Affirm the dataset’s structure, size, and key attributes linked to your agonize explain.
2. Hadoop Environment and Records Ingestion (10%)
• Place of abode up a local single-node Hadoop cluster.
• Doc the principle steps of your setup process.
• Load your chosen dataset into HDFS.
3. Records Processing with MapReduce (20%)
• Write a MapReduce program in Java or Python to process the info.
• Assemble data cleaning and characteristic engineering.
• Be aware the common sense of your Mapper and Reducer courses.
4. Predictive Diagnosis and Visualization (15%)
• Export the processed data from HDFS.
• Use the processed data to practice a straightforward predictive model.
• Analyze and command the output.
• Make necessary visualizations.
Part 3: Reflection and Documentation (LO 1, LO 2, LO 3) – 30% of Total Grade
1. Severe Reflection (10%)
• Replicate on the principle challenges encountered.
• Focus on performance and scalability.
2. Closing Characterize Documentation (20%)
• Assemble an broad document of no more than 3000 words.
• Be obvious correct structure and educational language.
• Embody diagrams, code snippets, commands, and visualizations.
• Embody a bibliography the usage of Harvard referencing fashion.
Doc Layout:
Post your project as a single account following the BSBI project template supplied in Canvas.
Writing Quality:
Be obvious determined and concise writing with correct grammar and spelling.
Visuals:
Embody diagrams, tables, and graphs the put acceptable.
Job Protection:
Take care of every little thing thoroughly.
Implementation Shrimp print:
Present linked examples at the side of code snippets and commands.
Referencing Vogue:
Use Harvard referencing fashion.
Dialogue:
Focus on findings, insights, and implications. Replicate on challenges.
Submission:
Post your project electronically via Canvas.
GUIDANCE ON ASSESSMENT
All materials ought to be successfully referenced under Harvard conventions. The length required is 3000 words with initiatives equally weighted. The writing fashion wants to be formal educational/document writing fashion with in-textual stutter material referencing to enhance your feedback and observations. Originality, quality of argument and appropriate structure are required. The document will be pleased to peaceable point out sound working out and skill to apply data and principle.
Grading Criteria:
Records of contexts, ideas, technologies and processes.
Conception via application of data.
Utility of technical and expert talents.
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