UTHM Niche Area

Universiti Tun Hussein Onn Malaysia

naicrehea

Universiti Tun Hussein Onn Malaysia

niche
area

Universiti Tun Hussein Onn Malaysia

niche
area

Content
1 - Sustainable Technology
2 - Data Engineering

Sustainable Technology

5

1 - Sustainable 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; Energy Management, Water Management, Waste Minimization and Recycling,
Built Environment, and Quality Education.
The term sustainability is also stated in the Plan Strategic UTHM 2017-2020:
Nilai Teras 1 – Kelastarian:
Persekitaran tempatan dan global, kesihatan organisasi dan keupayaan UTHM untuk menghasilkan
masa depan yang positif dan cerah

6 | UTHM Niche Area

1 - Sustainable Technology

Sustainable Technology Goals

Figure 1 presented UTHM Sustainable Technology goals which are; 1) Energy Management, 2) Water
Management, 3) Waste Minimization and Recycling, 4) Built Environment and 5) Quality Education.
These goals are mapped to all the faculties in UTHM as stated tabulated in Table 1.

Energy Management
Ensure energy reduction and

efficiency according to
sustainability principles in
energy generation and

consumption

Quality Education Water Management
Incorporate sustainability Enhances optimal use of
curriculum in research and water, improve water quality
educational programmes and wastewater mangement

Built Environment Waste Minimization
Integrate sustainability & Recycling
concepts into building life
Reduce waste generation
cycle and optimize the waste to

wealth concept

Figure 1: Sustainable Technology Goals

UTHM Niche Area | 7

1 - Sustainable 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 | UTHM Niche Area

1 - Sustainable 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 recognize 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. And 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.

Initial Stage Innovation Interdisciplinary

•Theories •Significantly increase •Integrate with different
•Problems outcomes disciplines and
•Expectations perspectives
•Less environmental
consequences •Emerge technologies

•Less cost

Figure 2 : Process of Sustainable Technology

UTHM Niche Area | 9

1 - Sustainable Technology

Table 2: Strategic plans to achieve Sustainable Technology in UTHM

Energy Management
•Cultivate awareness of resource and energy conservation
•Innovate technologies and ideas to reduce carbon emissions and energy intensity
•Promote renewable and alternative energy
• Develop and implement energy efficiency management

Water Management
•Cultivate 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
•Develop and implement water efficiency management

Waste Minimization and Recycling
•Cultivate awareness of reduce, reuse and recycle for better environment
•Innovate technologies and ideas to reduce waste generation
•Use and optimise the waste to generate significant outcomes with less environmental impacts.

Built Environment
•Cultivate awareness of the importance of a green built environment,
•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
•Developed world class educational and research programmes by integrating sustainability approaches and solutions
•Linking academic and local knowledge for sustainable community

10 | UTHM Niche Area

1 - Sustainable Technology

Sustainable Technology in the Future

UTHM will ultimately become a sustainable technology 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 carefully 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, predictive analytics, and wind and tidal energy need to
be investigated further.

1. Dr Junaidah Jailani - FKAAS
2. Dr Tong Yean Ghing -FKAAS
3. Dr Sharifah Salwa Mohd Zuki – FKAAS
4. Dr Shahiron Shahidan

UTHM Niche Area | 11

Data Engineering

2 – Data 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
skyrocket, 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. Analysing data will need to rely
on statistical and machine-learning 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 accurately and appropriately 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.

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

UTHM Niche Area | 13

2 – Data 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 1. 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 definitely 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 | UTHM Niche Area

2 – Data 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 UTHM 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 1: Data Science hierarchy of needs

2 – Data Engineering

Data Engineering Processes

Figure 2 presented UTHM Data Engineering processes which are; 1) Extract, 2) Transform, 3) Analyse, 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 usually a recurring process that occur daily, weekly, etc. and should be maintained and
updated accordingly. There are four major scopes that cover in Data Engineering:

Extract Transform Analyse Load

ExEtxrEtarxcattcErtaxctrtaTctrTarnaTsnfrosarTfnmorFsirgafumrnoe 2sr: DfmaotarAScminenAacelnpyroasAceelnsysesaAs elnysaelyLsoeaLdoaLdoaLdoad

16 | UTHM Niche Area

2 – Data 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, rejection of data (if they are not acceptable or outliers) and data integration. The commonly
used processes for transformation are conversion, sorting, filtering, clearing the duplicates, 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.

UTHM Niche Area | 17

2 – Data 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 | UTHM Niche Area

2 – Data 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.

Prof. Dr. Nazri Mohd Nawi
Prof. Dr. Rozaida Ghazali
Prof. Dr. Mustafa Mat Deris

UTHM Niche Area | 19

Universiti Tun Hussein Onn Malaysia

niche
area

Universiti Tun Hussein Onn Malaysia

niche
area

Universiti Tun Hussein Onn Malaysia

niche
area