UTHM_Niche_Area_Final_Document_v1

n i c ha ree aUniversiti Tun Hussein Onn Malaysia

n i cahr eeaUniversiti Tun Hussein Onn Malaysia

Committee Members Contact
Pejabat Penolong Naib Canselor
Advisor Perancangan Strategik & Perhubungan Korporat
Professor Dr Ismail Abdul Rahman Universiti Tun Hussein Onn Malaysia
[email protected] 07-4537445 07-4536580
Members
Professor Dr Mustafa Bin Mat Deris ALL RIGHTS RESERVED 2018
Professor Dr Nazri Bin Mohd Nawi All rights reserv ed. No part of this publication may be
Professor Dr Rozaida Binti Ghazali reproduced, distributed, or transmitted in any form or by any
Associate Prof Dr Afandi Bin Ahmad means, including photocopying, recording, or other electronic
Ts Dr Shahiron Bin Shahidan or mechanical methods, without the prior written permission of
Dr Junaidah Binti Jailani the publisher, except in the case of brief quotations embodied
Dr Tong Yean Ghing in critical rev iews and certain other noncommercial uses
Dr Sharifah Salwa Binti Mohd Zuki permitted by copyright law.

Graphic
En Muhammad Muzakkir Bin Mohd Nadzri

Publication
En Anuar Bin Othman
En Yazeed Bin Norman
En Sani Bin Saidun
En Shaizan bin Ahmad Mahmud

Content
1 - Sustainable Technology
2 - Data Engineering

n i cahr eeaUniversiti Tun Hussein Onn Malaysia

Sustainable Technology

5

1 - Sust ainable Technology

Introduction

The definition of sustainable technology in generals are technologies focusing on sustainability principles,
resource conservation, reuse and recycling, energy efficiency, minimizing environmental impact and
pollution reduction (United State of Agriculture Department).
Definitions of sustainable technology in UTHM context is capabilities to produce a technology that relies
on following resources. It includes energy and water management, waste minimization and recycling,
built environment and quality education as depict ed in Figure 1. All these area have also been mapped
to all the faculties in UTHM as tabulated in Table 1.
The term sustainability is also stated in the Plan Strategic UTHM 2017-2020:
Nilai Teras 1 – Kelestarian:
Persekitaran tempatan dan global, kesihatan organisasi dan keupayaan UTHM untuk menghasilkan
masa depan yang positif dan cerah

6 | U THM Niche Area

1 - Sust ainable Technology

Energy Management
Ens ure energy reduct ion and

efficiency according t o
s us t ainability principles in

energy generat ion and
cons umpt ion

Quality Education Water Management
I ncorporat e s us t ainability Enhances opt imal us e of
curriculum in res earch and w at er, improve w ater qualit y
educat ional programmes and w as t ewater mangement

Built Environment Waste Minimization
I nt egrat e s ustainability & Recycling
concept s int o building life
Reduce w as t e generat ion
cycle
and opt imize t he was te to
w ealt h concept

Figure 1: Sustainable Technology Goals

U THM Niche Area | 7

1 - Sust ainable Technology

Table 1: Mapping of Sustainable Technology Goals to Faculties

Faculties Energy Water Waste Minimization Built Quality
Management Management & Recycling Environment Education
FKAAS 
FKEE     
FKMP    
FPTP     
FPTV    
FSKTM     
FAST     
FTK    
PPD   
 

8 | U THM Niche Area

1 - Sust ainable Technology

Process of Sustainable Technology in UTHM Context

The process of sustainable technology in UTHM context are as presented in Figure 2. The process begins
with all the faculties need to recognise the theories, problems and expectations behind sustainable
technology both in general and in UTHM context. The second stage is innovation. The sustainable
product that will be produced is more focused on community. In addition, these products will be
environmental and economical friendly. Finally, interdisciplinary where all the faculties need to work
together as a team and produce a final product that integrate sustainable technologies from different
discipline. It also requires strategic action as being tabulated in Table 2.

Initial Stage Innovation Interdisciplinary

•Theories •Significantly increase •Integrate with different
•Problems outcomes disciplines and
•Expectations p ersp ectiv es
•Less env ironmental
consequences •Emerge technologies

•Less cost

Figure 2 : Process of Sustainable Technology

U THM Niche Area | 9

1 - Sust ainable Technology

Table 2: Strategic plans to achieve Sustainable Technology in UTHM

Energy Management
•Cultiv ate awareness of resource and energy conserv ation
•Innovate technologies and ideas to reduce carbon emissions and energy intensity
•Promote renewable and alternative energy
•Dev elop and implement energy efficiency management

Water Management
•Cultiv ate awareness of resource and water conservation
•Innovate technologies and ideas to reduce portable water intensity
•Use and optimise rainwater harvesting system for sanitary and landscape irrigation
•Dev elop and implement water efficiency management

Waste Minimization and Recycling
•Cultiv ate awareness of reduce, reuse and recycle for better env ironment
•Innovate technologies and ideas to reduce waste generation
•Use and optimise the waste to generate significant outcomes with less env ironmental impacts.

Built Env ironment
•Cultiv ate awareness of the importance of a green built env ironment,
•Adopt sustainability features and principles in the design, planning, construction method and material selections
•Take into consideration Malaysian green building rating tools requirements in building life cycle.

Quality Education
•Incorporate multi-disciplinary educational and research programmes
•Dev eloped world class educational and research programmes by integrating sustainability approaches and solutions
•Linking academic and local knowledge for sustainable community

10 | U THM Niche Area

1 - Sust ainable Technology

Sustainable Technology in the Future

UTHM will ultimately become a sustainable t echnology hub in future. The aim of a sustainable
technology hub is to provide sufficient space, technology and expertise for the development and
application of sustainable infrastructure. Sustainable technology will optimise all levels of innovation,
creation and production of future products, equipment and systems. At the same time, the natural
environment and resources will be carefull y conserved during the process. This helps to minimise and
reduce the negative impact of human activities.
Strategic plans for implementing sustainable technology in UTHM incorporates multidisciplinary expertise
to address global challenges such as energy demand, resource allocation and pollution. For future
sustainable technology, innovative ways to advance in the areas of nanotechnology, nuclear power,
biofuels, bioplastics, smart-monitoring systems, predi ctive analytics, and wind and tidal energy need to
be investigated further.

U THM Niche Area | 11

Data Engineering

2 – Dat a Engineering

Introduction

The current trend show that one of the most common mistakes make by organizations is failing to
capture the right data needed to make the right decisions. As the volume of information continues to
skyrock et, the variety and velocity of data will grow as well. And as more data is being collected,
extracting value from that data is only going to become more complex. Anal ysing data will need to rely
on statistical and machine-l earning approaches to extract information from data automatically.
Machine learning will become critical in order to deliver insights to the right decision makers at the right
time. Data engineering is the best solution for those analysis and those difficult problems is possible via
data engineering. Data engineering is a fundamental part of the new world of big data, not only
increasing the amount of data collected, but also ensuring that is clean, consistent, and high quality.

Furthermore, the increasing complexity of the world of Big Data means that gaining insights requires
more than a set of rudimentary algorithms and a basic understanding of analytical principles. Every
aspect needs to be ensuring that the process is managed accurat ely and appropriatel y which will play
an important role on the strengths and abilities of various disciplines. Data engineering will continue to
be an important process in developing and implementing the new technologies that will form the data-
driven future.

Typicall y, data engineers come from a background in engineering, computer science, or software
development, with knowledge in both database development and management and engineering
practices.

U THM Niche Area | 13

2 – Dat a Engineering

Over the next 5 years, data engineering will play an important role in helping data scientists in develop
the ability to utilize all sorts of data in real-time as can be seen in Figure 3. In addition, this will fuel the
need for making more intricate predictions and computations at scale which will spark the emergence
of new data science paradigms due to the needs of future applications. Data engineering definitel y will
contribute to the current trend in Big Data where more and more data will be used to drive key business
decisions, and will enable researches that allow for accurate predictions and decision making.
Data engineering in UTHM context shows that there is a need for data engineering for all disciplines
(engineering or non-engineering) and the term is well suited with UTHM Hala Tuju in producing more
professionals and experts in data engineering with global view in preparing for Big Data challenge as
stated in the Plan Strategic UTHM 2017-2020
[Hala Tuju 2 – Kolaborasi yang dilakukan merentasi sempadan Negara dengan mengambilkira
penetapan tahap oleh organisasi di peringkat global]

14 | U THM Niche Area

2 – Dat a Engineering

The Data Science Hierarchy of Needs

Learn/Optimize AI, Data
Aggregate/Label Deep Scientist
Explore/Transform Learning
Data
A/B Testing, Engineer
Experimentation,
Simple ML Algorithms U THM Niche Area | 15

Analytics, Metrics,
Segments, Aggregates,
Features, Training Data

Cleaning, Anomaly, Detection, Prep

Move/Store Reliable Data Flow, Infrastructure, Pipelines,
Collect ETL, Structured & Untstructured Data Storage

Instrumentation, Logging, Sensors, External Data,
User Generated Content

Figure 3: Data Science hierarchy of needs

2 – Dat a Engineering

Data Engineering Processes

Figure 4 presented UTHM Data Engineering processes which are; 1) Extract, 2) Transform, 3) Anal yse, 4)
Load. These processes are applicable across all the faculties in UTHM.

Big organizations such as UTHM need to implement Data Engineering for reporting purposes and data
analysis which will help in simplifying the decision making process for UTHM. Furthermore, the decision
making process usuall y a recurring process that occur daily, weekl y, etc. and should be maintained and
updated accordingly. There are four major scopes that cover in Data Engineering:

Ext ract Transform Analyse Load

ExEtxrEtarxcattcErtaxctrtaTcrtTarnaTsnfrosarfTnomrFsigraufmrnoe 4sr: DmfaotarAScminenAcaelnpyroaAsceelnsysesasAelnysaelysLoeaLdoaLdoaLdoad

16 | U THM Niche Area

2 – Dat a Engineering

Extract:
Able to extract and reads data from different kinds of sources database and extracts the desired subset
of data (structured, semi-structured and un-structured). The purpose of this step is to retrieve all the
required data from the source system with minimum resources. This step needs to be designed in a way
that it does not affect the source system negatively in terms of performance or response time.
Transform:
Able to filter, cleanse and prepare the extracted data using lookup tables or rules or by creating
combinations with other data and converts it to the desired state. The transform step includes validation
of records, rej ection of data (if they are not acceptable or outliers) and data integration. The commonly
used pro cesses for transformation are conversion, sorting, filtering, clearing the duplicat es, standardizing,
translating and looking up or verifying the consistency of data sources.
Analyze:
Able to discover patterns in large data that been transform which involve methods such machine
learning, statistics, and database systems. The discover patterns can then be used to obtain more
accurate prediction results by a decision support system.
Load:
Able to write the resulting data, i.e. the extracted and transformed data, (all of the subset or just the
changes) to a target data repository.

U THM Niche Area | 17

2 – Dat a Engineering

Since Data Engineering is a subset of Data Science, it is clear that Data Science deals with both
structured and unstructured data. It is a multidisciplinary field that includes everything that is associated
with the cleansing, preparation and final analysis of data. Data science combines the programming,
logical reasoning, mathematics and statistics. It captures data in the most ingenious ways and
encourages the ability of looking at things with a different perspective. Likewise, it also cleanses,
prepares and aligns the data. In addition Data Science is an umbrella of several techniques that are
used for extracting the information and the insights of data. Therefore Data scientists are responsible for
creating the data products and several other data based applications that deal with data in such a
way that conventional systems are unable to do.

The Role of a Data scientist and Data Engineer also different as follows:
Data scientists should be spending time and brainpower on applying data science and analytic results
to critical business issues. They are responsible to help an organization to turn data into information and
then turn information into knowledge and insights.

Whereas, a Data engineers are the designers, builders and managers of the information or "big data"
infrastructure. They develop the architecture that helps analyze and process data in the way the
organization needs it. And they make sure those systems are performing smoothly.

18 | U THM Niche Area

2 – Dat a Engineering

Conclusion

Previously, many organisations believe that whoever gets the most data will win. The statement is no
longer valid. Furthermore, it is not necessarily true to say that having easy access to a broad scope of
data can give businesses a competitive edge. Today, the technology is fast changing and with the
current trend in Big Data, organisations such as UTHM need access to all sorts of big data – from videos,
social media, the internet of things (IoT), server logs, spatial data, open or crowd sourced data, and
more. There is a need for centre that can handle Data Engineering and can add new transformations to
the existing techniques to support these emerging requirements and new data sources.

U THM Niche Area | 19

n i cahr eeaUniversiti Tun Hussein Onn Malaysia

n i cahr eeaUniversiti Tun Hussein Onn Malaysia

n i cahr eeaUniversiti Tun Hussein Onn Malaysia