Assignment 1 1625 Managing a Successful Computing Project Greenwich 2022

Assignment 1 1625 Managing a Successful Computing Project đại học Greenwich 2022, điểm chuẩn Pass. Project initialization, Main aim of project, List of Objectives to achieve the aim, Project management plan, Scope, Time, Gantt chart, Communication, Resources, Risks, Cost estimation, WBS, Primary research, List of interview question, Summary about interview , List of survey question , Summary about survey, Secondary research, By 2025, the amount of data will reach 175ZB, which is a huge number. Imagine the computer hard drive is only 1TB then the difference will be obvious. As data increases, the number of terminals such as hard drives and phones also increases, which consumes a lot of power as well as resources, which is a reason for environmental pollution. The main reason for the increase in data is that the old data is still stored and the data that is not needed or is no longer useful is kept.

Qualification BTEC Level 5 HND Diploma in Computing

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2.1

Contents

I Introduction 1

II Project initialization 1

1 Main aim of project 1

2 List of Objectives to achieve the aim 1

III Project management plan 2

1 Scope 2

2 Time 3

3 Communication 3

4 Risks 4

5 Resources 4

6 Cost estimation 4

3 Planning 5

1 WBS 5

2 Gantt chart 6

4 Primary research 7

1 List of interview question 7

2 Summary about interview 8

3 List of survey question 8

4 Summary about survey 10

5 Evaluation 10

5 Secondary research 10

1 List of articles/ books 11

2 Summarize 11

3 Evaluation 13

Figure 1: WBS 5

Figure 2: Gantt chart 6

Figure 3: Suvery question (1) 8

Figure 4: Survey question (2) 9

Figure 5: Survey question (3) 9

Figure 6: Survey question (4) 9

Figure 7: Survey question (5) 10

I Introduction

Problem: By 2025, the amount of data will reach 175ZB, which is a huge number Imagine the computer

hard drive is only 1TB then the difference will be obvious As data increases, the number of terminals such

as hard drives and phones also increases, which consumes a lot of power as well as resources, which is a reason for environmental pollution The main reason for the increase in data is that the old data is still stored and the data that is not needed or is no longer useful is kept

Solution: Stemming from the above reasons, the proposed solution will focus on the input data, process it

in real time, filter and store the necessary data, and delete the data that is no longer effective To implement this solution, AI and IoT are necessary tools IoT will collect real-time information, process it first if it's not too complicated, then send it back to the central processor, AI will decide what data is data back, what actions are taken, etc

Project: As a member of the research and development department of the National Hydrometeorological

Administration, I will be working on a project called "Climate Management AI"

With the characteristics of the meteorological industry, the data sent every day is very large such as rainfall, air humidity, satellite images, tropical depressions, etc The amount of data to be processed is very large, a lot of data is not important or often encountered causing time consuming Due to the large amount of data, manual deletion takes a long time, possibly deleting important data by mistake Large capacity leads to greater power consumption to run This project will integrate AI to solve the above problem

1 Main aim of project

 Integrate data processing technology for the system

 Input data processing eliminates redundant data

 Reduce newly generated data

 Reduce the workload for employees

 More accurate prediction of phenomena

 Easy data statistics

2 List of Objectives to achieve the aim

1 Learn about data types (work with data researchers)

1.1 Collect commonly sent weather data types

1.2 Learn about their influence on the weather

2 Learn similar systems (hospital applications, etc.)

2.1 Working with agencies using the system

2.2 Find out advantages, disadvantages, points to note

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3 Understanding AI technology for systems (Machine learning)

4 Building AI for the system (data processing and weather forecasting system)

4.1 Requirements

4.2 Analysis and design

4.3 Deployment (Encoding)

4.4 Testing

5 Evaluation

5.1 Get a technical staff assessment

5.2 Provide general assessment and reporting

5.3 Provide direction for development

1 Scope

The "Climate Management AI" project will be completed within 3 months This project is an AI with functions such as collecting and processing weather data, cleaning up redundant data, it is developed and applied to the current weather management system

During the first week, we will analyze the feasibility, identify the risk and plan for it The technology applied will also be explored along with the study of the climate center's actual data Finally, make

a specific plan for the whole project

In the next 3 weeks, work related to building the system will be carried out, including user requirements analysis (station staff), system design We spent 2 weeks programming the system and applying it to the existing system

We spend the next 7 weeks testing in the climate center to be able to give the most accurate assessments In the last week, assessments will be taken from staff, experts who have used the system, that information will be collected and written into a report along with the direction of development

Finally, we will hand over the project back to the climate center for use

The aim of this project is to reduce the amount of data stored on a daily basis, use machine learning

to predict the weather, increase accuracy, and help with real-time processing

- A self-learning system

- The system makes predictions on behalf of experts

- Minimize the amount of input data

2 Time

Analyze goals, systems, technology to be used, feasibility 1 day

Get the request of the staff, experts at the center 1 day

Run in actual

system

Evaluation

and closing

07/11/2022 – 14/11/2022 Awating

Development direction and hand over 1 day

3 Communication

Our team will have face-to-face meetings and some online meetings with Google Meet Daily reports will be sent via chat group on Zalo

times

Daily

meeting

3-5 times/week Reporting and implementing new tasks Google Meet

Daily Report Every day Daily progress report Zalo

Analysis

(Plan)

3 times Analyze problems to develop plans and

risk plans

Offline

Research

Data

3 times Working with staffs, experts to

understand data

Offline

Analysis

Requirement

2 times Segregate the task of taking user

requests and analyzing them

Offline

Design

Implement

Every day (in 2 weeks)

Building system Google Meet + Offline

Testing Every day (14

days)

4

Daily test

system

20-25 times/ 7 weeks

Test system and check work progress Offline

Get

feedback

Evaluate 3 times Evaluate and write report of project Offline

Handover 1 times Handing over the system to the climate

center leader

Offline

4 Risks

level

1 Requirement Collect or evaluate user

requirements, wrong project purpose

High Depending on the

stage that affects the project differently, it can affect the whole project

Work with many experts to understand, collect clear requirements, seriously analyze

2 Not enough

people

Temporary leave due to illness or other causes (more than 2 people)

Low Slow down the

project progress

Find 2 replacement personnel from the beginning of the project

3 Project

schedule

Important in-person meetings (possibly due to weather)

Low Slow down the

progress

Prepare another temporary replacement plan to avoid delays

4 Technology The selected technology

is not suitable for the system

Medium Back to the planning

stage

Work with development experts to understand, choose the right technology

5 Cost Types of costs incurred

such as hiring experts, buying equipment

Low Lack of budget Prepare a contingency budget

at the beginning of the project (about 10-20% of the main budget)

6 Many errors After actual testing, there

were more errors than expected

Medium Fix bugs that slow

progress, waste valuable time and data

Detailed test planning

5 Resources

Tools:

 Upgrading system hardware (chips, heatsinks, etc.)

 Google Meet, Zalo

 Development tools for AI

Human:

 5 persons: 1 senior, 3 junior AI, 1 tester

6 Cost estimation

No Type Detail Cost

1 Salary 1 senior (3000$/month), 3 junior (1500$/month), tester

(1000$/month), time is 3 month

25500$

2 Upgrading

system hardware

Upgrade the system to be able to integrate AI technology 25000$

3 Hire an

expert

Hire experts to advise on technology, development direction 1000$

4 Learn

more technology

Working on projects using similar technology 500$

5 Risks The cost to deal with the risks if they arise, it is about 10% of

the total cost

5000$

3 Planning

1 WBS

Figure 1: WBS

This WBS is divided into 4 phases: Planning, Building, Run the actual system, Evaluation and closing

In the planning phase, the first thing is to plan and analyze the project The problems analyzed are the advantages and disadvantages of the old system, the solution for the disadvantages, the technology for the solution, the feasibility of the solution Next, find out the possible risks (natural and man-made),

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then create a risk plan including remedial methods Finally, the data of the current system is studied, as

a premise to apply to the new system

The building phase will learn the requirements of employees who use the current system, and then analyze those requirements to build functions for the new system Next is system design, building wireframes, diagrams, etc Based on those designs to build the system (programming) Then add to the existing system to test the new system

The stage of running the actual system is simply observing and recording the results and performance

of the AI when it comes to actual work

The Evaluation and closing phase will take user reviews (mainly data) to get information about the accuracy and usefulness of the project Then write an evaluation of the results, write a report and future development directions based on the evaluated information Finally, handover to end the project

2 Gantt chart

Figure 2: Gantt chart

The Gantt chart designed for the project is also divided into 4 phases like WBS, the time span is about

3 months (larger because there are days off) The first is the planning phase which will include project planning activities (1 day), risk plan (1 day), research data and real project – activities at the center and projects at other enterprises, so it will take 2 days, then spend 1 day to develop the final plan

Phase building includes the work of taking requirements from users and analyzing them (2 days), system design (3 days because of a lot of work), Coding (2 weeks), Adding to the system and testing (2 days) day)

Phase run the actual system will take about 7 weeks to achieve accuracy as well as gather enough parameters for the system

Phase Evaluation and closing includes taking user feedback, then evaluating and writing reports, building future development plans Finally, hand over to the climate center All wrapped up within 1 week

All phases will be performed by Team member 1 (our team)

4 Primary research

Methods of building primary research

Primary research includes interviews and surveys, interviews will be used qualitative research methods because it will take people's subjective information, information related to feelings, impressions and usually do not use data For the survey, the quantitative research method will be used because it will present the data to the survey takers, take those data and analyze

The qualitative research method used is In depth Interviewing, it is optimal for collecting data about personal views and experiences and especially understanding of the problem

The quantitative research method used is survey, it is used to ask closed questions about the issues to be collected, the collected data will be data such as rate, level, etc

Purpose of Primary research

The purpose of primary research is to collect data from respondents, interview about current data problems, find out the influence and level of interest of the surveyors on the problem of data pollution Rely on their experience and understanding to have more contributions to the project The post-project survey will give the most accurate data for the project as well as the system, confirm the feasibility in practice and the level of user satisfaction Most importantly, survey takers will make assessments and suggestions for future development based on user roles

Overview about Interview/Survey (Who, What, Why, When, Where)

The purpose of the interview was to determine the feasibility of the project as well as the complexity, data-based, and user confidence in the project The audience of the interview are the staff, experts at the National Hydrometeorological Administration, they are the users of the system later, their requirements are the core requirements for the project The interview will be conducted in the 12th week of the project (the last week) to get the best evaluations

1 List of interview question

 What's your name ? (Closed question)

 What is your position in the agency? (Closed question)

 Do you know anything about AI? (Opened question)

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 Weather data is increasing, polluting the environment, what do you think is the solution? (Opened question)

 Is the AI project to control the weather possible? (Closed question)

 After testing, do you find this project useful? Does it predict correctly? (Closed question)

 What do you want the system to be improved in the future? (Opened question)

2 Summary about interview

Answer: Most of the user answers do not understand the problem of pollution by data However, they

highly recommend this hybrid AI project

Through the interview, users provided the necessary information about their understanding of AI, the problem of environmental pollution caused by the data, the feasibility of the system, and the usefulness

of doing so actual work

In addition, users also provide requirements for future development, thereby building a development plan

3 List of survey question

The survey includes personal information and some questions about the project We received 10 replies from users (due to the relatively small number of users)

Form:https://docs.google.com/forms/d/e/1FAIpQLSd7WoEOoYn4GVRO9G7uUHms7dyW4fJ1mJZA dMPSJHVLay7YzA/viewform?usp=sf_link

Figure 3: Suvery question (1)

Most are system administrators (staff), in addition there are experts and interns (Closed question)

Figure 4: Survey question (2)

Basically the system has high accuracy in weather prediction (Closed question)

Figure 5: Survey question (3)

The project reduced the amount of input data stored by at least 70% compared to before, which is an impressive number (Closed question)

Figure 6: Survey question (4)