SEN719: Data Characteristics of Australian Water Utilities Report

This report provides an in-depth analysis of data characteristics and applications within the Australian water utility sector. The introduction establishes the importance of the water utility industry and its role in providing safe and timely water distribution, highlighting the increasing challenges due to expanding markets. The report examines the types and sources of data used by Australian water utilities, focusing on their characteristics (volume, velocity, variety, variability, and veracity). It includes a literature review on the use of sensors, SCADA systems, and big data technologies in water management, as well as a methodology section outlining data collection methods. The report discusses the challenges associated with applying computational analytical approaches to these data sources and identifies research gaps. The conclusion summarizes the key findings and implications of the study, emphasizing the potential of big data to improve decision-making and water resource management in Australia. The report covers various aspects like Big Data, characteristics of data, and how it will be helpful in the decision-making process.

Rubrics for Assessment
task 3

Contents
INTRODUCTION.....................................................................................................................................3
Background research................................................................................................................................3
Research Aim.............................................................................................................................................3
Objectives...................................................................................................................................................3
Research questions....................................................................................................................................3
Literature Review......................................................................................................................................4
Methodology..............................................................................................................................................7
Research Gaps...........................................................................................................................................9
CONCLUSION........................................................................................................................................10
REFERENCES........................................................................................................................................11

INTRODUCTION
Water utility is an industry in the United Kingdom that comprises of all the companies
that are responsible for the distribution of water in a timely and safe manner. The companies are
also responsible for other water related services like treatment of the wastewater, managing the
demand from the industrial, residential as well as agricultural markets(Wen, and et.al., 2018).
This is because the markets are expanding due to which the supply of portable water is becoming
difficult. The report includes a literature review, methodology, scope as well as research gaps of
this topic. The complete report is based on the sensor data of Australian Water Utilities.
Background research
Big Data is becoming universal today, offering a wide range of opportunities and
development, despite innovations in scientific knowledge. Big Data methods capture and extend
the conventional approach to standard information, enabling a level of information preparation
that would normally be inaccessible. The increasingly multifaceted nature of new information
sources stems in part from a correlated cost reduction in both age and information capacity over
the past decade, which has therefore encouraged creation, and improved direct access to
meaningful databases.
Research Aim
To identify the various data types and sources available to or used by Australian water utilities
and the typical characteristics (volume, velocity, variety, variability, and veracity) of that data.
Objectives
To examine the challenges associated with the applicability of existing computational analytical
approaches, applied to such data sources, which may currently be limiting the value of the
information that can be derived.
Research questions
1. What are the typical characteristics (volume, velocity, variety, variability, and veracity)
of that data?

2. What are the sources available with Australian government to reveal big data for water
utility?
3. How this big data technology will be helpful in supporting Australian government in
decision making related with water utilization?
Literature Review
Sensors are the basis for plant automation and robotics. Their product interface to
firmware is one of the important areas in mechanical applications. It is important that they
understand their limitations when designing a control panel. Sensors, for example, temperature,
gas, sticky, IR, ultrasonic laser, PIR sensor and so on are commonly used in industries. Creating
campaigns that incorporate such sensors provides unprecedented thinking in understanding their
use and barriers. Data protection, SCADA, mobile logic control is hardly a level of activity that
typically adopts integrated frameworks and requires information about the programming area,
especially the "C" language. "This article examines a summary of projects based on sensors for
subtitle design (El Alaoui, Gahi and Messoussi, 2019).
Wireless sensor networks are called remote sensing networks and actuator (WSAN), a
group that incorporates distributed sensors to shield physical or ecological conditions, such as
pressure, noise, temperature and so on. forward. This framework includes a door to second-hand
access and appropriate hubs, which can move information through the organization to the main
area. Modern networks are two-way in nature and enhance sensory function. At present, a large
number of facilities for observation and control of water services are not interactive. There are a
number of restricted or semi-restricted provisions in place, some of which are likely to raise
issues. In reality, the control structures used in water transport services are similar to collection
structures, but they have amazing ideas. In this way, we can imagine an information-building
model for water services obtained from advanced stages in enterprises (Wu and et.al., 2018).
As a result of observation of the water service, levels 1 and 2 are provided in terms of geology
and for the control of the water services, management control and data acquisition applications
(SCADA) are used. . A number of applications at the enterprise fund (ERP) settlement stage will
complete the debit actions and follow the service providers ’allowances. It can be seen that there
is a direct flow at normal effort levels and a carrier flow for data protection in the field.

Supervisory Control and Data Acquisition (SCADA) systems are used in a wide range of
processes such as:
• Infrastructure processes (for example, oil and gas pipelines, table water, waste of actuators,
distribution and conduction of electrical power, and so on),
• Industrial processes (e.g. manufacturing, refining, power generation, etc.),
• Facilities process (e.g. buildings, bus terminals, airports, etc.)
The key point of SCADA frameworks is to observe and control a variety of supplies and
equipment. In all that is considered, there are some important considerations:
• Scope in SCADA frameworks; these frameworks can be identified based on: the year in which
they entered into operation, their capabilities and openness and so on
• The complexity of the frameworks provided by the design of the equipment and programming
tools used in the rotation of events; this view depends entirely on the body of the organization
articulated by temporary employees, buyers and even experts.
The water service sector is responsible for strict enactment, which seeks to address both
the promotion of practices within the drug / drug industry, and ecological safety advocacy, and is
educated by partner offices. The development of public ecology awareness is governed by a
reasonable distribution of responsibilities within the sector. This incredibly invisible and
dynamic environment poses challenges to water services as they try to deal with the diverse
synthetic mixtures that emerge from unparalleled sources reaching wastewater treatment plants,
including private, industrial, and modern start-up purposes and sources spread over running rural
waters and hard surfaces. There is a wide range of natural and inorganic mixtures, herbicides,
poisons, phosphorus, drugs and synthetic fertilizers that are becoming increasingly popular.
These toxins can be in an inseparable structure or manifest in the face of natural problems, the
associated hazards that cause critical ecological problems (Hasanin and et.al., 2019). Regardless
of the overall improvement in Big Data innovation and distribution in a number of broader
components, adoption within the water services sector remains limited to this day. They differ in
their breadth of use of Big Data in different areas, a character attracted by the need for

improvements to maximize the potential of this innovation in the services of water sector (Kapil,
Agrawal and Khan, 2016).
The water resources department has prepared all the basic components for the widespread
use of Big Data innovations, from controlling and improving the quality of edible water to
managing wastewater. The section draws on extensive sources of information on water quality,
such as dispersions, atmospheres, consumer bias data and use examples and area-based land use
exercises to provide examples. Having the ability to monitor, modify, dissolve and break down
completely can gain the upper hand and reduce the sensitivity of the dynamics. However, the
acceptance rate per account is lower than in various regions, where big data and AI are currently
widely used to support evidence-based dynamics (Wen, and et.al., 2018).
Characteristics of Big data:
1) Variety
Variety of sensor data includes location, displacement, movement, sound frequency,
temperature, pressure, humidity, electrical voltage level, camera images, color, chemical
composition, etc.
2) Velocity
Velocity essentially refers to the speed at which data is being created in real-time. In a broader
prospect, it comprises the rate of change, linking of incoming data sets at varying speeds, and
activity bursts.
3) Volume
Volume is one of the characteristics of big data. We already know that Big Data indicates huge
‘volumes’ of data that is being generated on a daily basis from various sources like social media
platforms, business processes, machines, networks, human interactions, etc. Such a large amount
of data are stored in data warehouses. Thus comes to the end of characteristics of big data (Kaur
and Sood, 2017).
4) Veracity
Veracity basically means the degree of reliability that the data has to offer. Since a major part of
the data is unstructured and irrelevant, Big Data needs to find an alternate way to filter them or to
translate them out as the data is crucial in business developments (Sun, 2018).

5) Value
Value is the major issue that we need to concentrate on. It is not just the amount of data that we
store or process. It is actually the amount of valuable, reliable and trustworthy data that needs to
be stored, processed, analyzed to find insights.
The Internet of Things (IoT) is the so-called introduction of sensors, devices and so on on the
internet. In the opinion of the Internet of Things, the term "object" can refer to individuals,
objects (eg devices, sensors, devices, etc.) or data. At present, “Web of Things” has a different
meaning that varies according to the specific situation, the effects and the perspectives of who is
interpreting it. As a result, from the point of view of things and the organized view of the
internet, the Internet of Things is seen as “a reality where things can come naturally to PCs and
offer forms of support for the benefit of humanity (Younas, 2019).
Writing research on logical information bases reveals a limited documented use of Big Data
methods in the water sector. It is an area that runs smart gases and familiar sensors that generate
a lot of stable data nearby, allowing water bodies to better manage and provide improved
frameworks, drawing on Big Data methods. However, the use of these advances in a number of
commercial outlets, for example, better representation of water flow, or a better understanding of
water quality, looks relatively undeveloped to date. This was evidenced by the low number of
results obtained when studying a specific combination of slogans in peer-tested distributions.
Significance was established for each setting through the metadata meeting of the article and the
material where it was made accessible. In the specific case of the use of Big Data in assessing the
risks associated with the presence of certain synthetic compounds in water, releases do not
appear to be relevant to date. The reason for the low performance of the results may be due to the
lack of this type of use within the company or the company’s trust in publishing that data (Wan,
and et.al.,, 2017).
Sensor data for water unity
Traditional systems have big gaps in trying to use board KPIs. Fortunately, the proliferation of
sensors, self-monitoring tools, and video / photo curation is thriving. Advanced resource-based
innovations, characterized by having experiences on water, removing secrecy from identifying
spills, stressing operators, circulating labor demand, and so on. Innovations such as the Internet

of Things (IoT), ML, AI, Deep Learning, information and analytics impact by focusing on
executive KPI assets (Song and Deng, 2019).
A sensitive frame / experience tool, designed specifically for water resources, will allow
facilities to stay once ahead of problems at all levels of clean water and drain with grace, fitness
and board (Malekzadeh and et.al., 2019). The tool will utilize the information used using
abstraction and survey cycles, calculations, models and obvious best practices for water
resources. Knowledge, options and recommendations will be presented to enterprise customers
and applications through a variety of tools (Ang, and et.al., 2017).
Methodology
Methodology in research can be referred to as specific techniques or procedures that are
used in order to identify, select as well as analyze the information related to a particular topic.
Data collection is an important component when it comes to conducting a research and coming
to useful outcomes.
Primary Methods – The data for a research can either be collected through primary or
secondary methods. There are different primary methods of collecting data that include surveys,
questionnaires, interviews, observations etc. Primary methods of research are commonly used in
order to collect data as they are convenient as well as time and cost effective. There are many
advantages of using primary methods, like the information that is collected is first hand and also,
there is not any kind of dilution of data. The data which is collected can be customized by the
researcher in order to suit the requirements of the research. The information that is collected
using primary methods helps in conducting the research in an effective manner and achieve the
objectives of the research.
Secondary Methods –The other way in which information can be collected is through
secondary methods. Secondary information is available in the form of books, research articles,
journals, online websites etc. The method of collecting data with the help of secondary methods
is cost-effective(Sun, 2018). Also, the amount of time required to collect the data is also less as
compared to primary methods. Benefits of using secondary methods to collect information is that
the researcher can tailor the same based on their needs.

Quantitative Research – This is a type of research that involves collection as well as
analyzing numerical data. The results of a quantitative research are presented in the form of
tables, graphs, charts and numbers so that it is easy to interpret the same. It can also be used to
make predictions and generalize results for a wider population. Quantitative research is more
reliable as well as objective as compared to qualitative research. Not only this, it can be used to
establish a relationship of cause and effect in different circumstances. Some of the methods that
are included in the data collection of this type of research include survey research, cross-
sectional surveys, questionnaires etc.
Qualitative Research – Qualitative research is another kind of research that is
undertaken by researchers in order to analyze the non-numerical data. This type of research can
be used in order to understand the underlying reasons of a particular problem. And the results of
this type of research cannot be represented in the form of numbers, graphs or tables. The
different methods of collecting data for a qualitative research include one-on-one interviews,
focus groups, record keeping and observation(Younas, 2019). There are many advantages of
qualitative research like it provides in depth understanding of a particular research problem.
Besides this, it also creates openness and encourages the respondents to expand their existing
responses.
Sampling – Sampling is a technique which is used while conducting a research and
involves selecting a pre-determined number of observations from a large population. There are
two types of sampling, probabilistic and non-probabilistic sampling. For this research,
probability sampling has been used. Some advantages of sampling are that it helps in collecting
almost accurate results, without putting into a lot of time and also, the level of accuracy of the
results that are obtained by sampling is high. The cost as well as time involved in the collection
of data from a large population are more, thus sampling can help in conducting the research in an
effective manner. Also, sampling has helped in completing this research well within time and in
an effective manner.
Research Gaps
A research gap can be defined as a topic that can create limitation in order to reach a
conclusion for the research. For this particular research, there are some gaps that have been
identified. The size of the sample large due to which, the research could not be conducted in an

effective manner as there were issues with the collection of data. Apart from this, time also posed
to be one of the research gaps because the time available for conducting the research was very
less and it could not be completed as efficiently as it was though of. Both primary as well as
secondary methods were used to collect the data(Wan, and et.al.,, 2017). But there was a
research gap that the some of the respondents did not answer to the questions given. There are
different ways in which research gaps can be identified. For example, the literature review can be
revised and if there is anything which is found to be missing from the research is considered to
be a gap.
Also, there are different types of research gaps like in context of this research, there are
certain issues relating to the Australian Water Utilities that were not identified. This led to an
influence on the overall research. The different types of research gaps can include knowledge
gap, customer gap etc. Once the research gaps have been identified, it is important to cover them
and thus, should be treated as a priority. There are many tools that can be used in order to fill in
these research gaps and ensure the overall effectiveness as well as efficiency of the research. So
that when the audience reads the research, they are able to understand the topic effectively and
the results as well. If any research gaps are left unidentified, they can pose challenges not only to
the researcher, but the people who read the results a well. Some of these challenges can include
difficulty in dealing with the large amount of data, difficulty in searching for a particular
information etc.
Therefore, in order to avoid research gaps in the first place, it is important to look for
information that is authentic and accurate(Song and Deng, 2019). This is because it will allow
the researcher to conduct their research in an effective manner. Also, if the researcher comes
across any difficulty pertaining to the data collection, they can reach out to their advisor.
Articulating the ideas and understanding what others think and are working on can also help it
become aware of having a look at place or even identify mistakes for the method. If it watched a
question could be thrilling to paintings on, it can talk it with the consultant and get their
suggestions. Therefore, one should always focus on their area of research to be more specific.
Once there is a listing of questions that could be explored, it should conduct thorough
research on them. Read greater about every doubt or question which ithas. Find out if different
researchers have had comparable questions and whether or not they have observed answers to

them. This will help it avoid duplication of labor. While itfinalizes an unprecedented studies
concept, make sure itrecalls the time-frame available to complete the assignment in addition to
different crucial components including the availability of budget, system, and infrastructure. An
over-bold venture can be hard to perform due to time and resources restraints, while research that
makes an insufficient contribution might also fail to get the approval of the investment
committee or the magazine’s editorial board.
CONCLUSION
From the above report, it can be concluded that a sensor is always used by different
devices, simple like light or invisible like a PC. Advanced chip innovation allows you to easily
coordinate the capabilities you need, in a small volume and with low power consumption. The
devices, both of which structure the Internet of Things (IoT), are full of sensors. With these
sensors, the devices collect information about how they are used and the surrounding terrain. The
information gathered can be as simple as a temperature estimate or as invisible as a full video
feed. But it also considers sensory information such as area, sound or heat and the various
perceptions of devices or our body. These devices have a transparent (remote) network, so they
can be connected to the Internet and can exchange information. Billions of connected devices are
important to the IoT. The hallmark of the IoT is that all connected devices produce an enormous
amount of information (Big Data).

REFERENCES
Books & Journals
Ang, L.M., Seng, K.P., Zungeru, A.M. and Ijemaru, G.K., 2017. Big sensor data systems for
smart cities. IEEE Internet of Things Journal, 4(5), pp.1259-1271.
El Alaoui, I., Gahi, Y. and Messoussi, R., 2019, April. Full consideration of Big Data
characteristics in sentiment analysis context. In 2019 IEEE 4th International Conference
on Cloud Computing and Big Data Analysis (ICCCBDA) (pp. 126-130). IEEE.
Hasanin, T., Khoshgoftaar, T.M., Leevy, J.L. and Seliya, N., 2019. Examining characteristics of
predictive models with imbalanced big data. Journal of Big Data, 6(1), p.69.
Kapil, G., Agrawal, A. and Khan, R.A., 2016, October. A study of big data characteristics.
In 2016 International Conference on Communication and Electronics Systems
(ICCES) (pp. 1-4). IEEE.
Kaur, N. and Sood, S.K., 2017. Efficient resource management system based on 4vs of big data
streams. Big data research, 9, pp.98-106.
Malekzadeh, M., Clegg, R.G., Cavallaro, A. and Haddadi, H., 2019, April. Mobile sensor data
anonymization. In Proceedings of the International Conference on Internet of Things
Design and Implementation (pp. 49-58).
Song, Y. and Deng, Y., 2019. A new method to measure the divergence in evidential sensor data
fusion. International Journal of Distributed Sensor Networks, 15(4), p.1550147719841295.
Sun, Z., 2018. 10 Bigs: Big data and its ten big characteristics. PNG UoT BAIS, 3(1), pp.1-10.
Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H. and Vasilakos, A.V., 2017. A
manufacturing big data solution for active preventive maintenance. IEEE Transactions on
Industrial Informatics, 13(4), pp.2039-2047.
Wen, L., Zhou, K., Yang, S. and Li, L., 2018. Compression of smart meter big data: A
survey. Renewable and Sustainable Energy Reviews, 91, pp.59-69.
Wu, Y., Zhang, W., Shen, J., Mo, Z. and Peng, Y., 2018. Smart city with Chinese characteristics
against the background of big data: Idea, action and risk. Journal of Cleaner
Production, 173, pp.60-66.
Younas, M., 2019. Research challenges of big data.