Database Systems, Data Mining, and Data Analysis Report for MITS4003

This report provides a critique analysis of an article concerning database systems, data mining, and data analysis, specifically focusing on the application of these concepts in environmental research. The report examines the article's intention, which is to offer an overview of current data mining techniques and tools, encouraging researchers to utilize these tools to extract valuable insights from complex datasets. The report details the qualitative research method used, primarily observation, and presents the article's findings, highlighting the increasing volume and complexity of data and the potential of data mining to uncover knowledge. The report identifies key issues, including challenges in spatial data mining and the potential for database growth, as well as challenges in environmental research, such as dealing with the interdisciplinary nature of data and the impact of data on decision-making. The report concludes by summarizing the key points of the original article, including the role of data mining in the context of environmental research and the importance of LOD (Linked Open Data) approaches. The report also mentions the use of open-source data mining tools like RapidMiner and KNIME. The report includes a bibliography of the sources used.

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Contents
Introduction......................................................................................................................................2
Intention and content of the article..................................................................................................3
Research method..............................................................................................................................4
Findings...........................................................................................................................................4
Problems or issues...........................................................................................................................5
Issues in environmental research.................................................................................................6
Conclusion.......................................................................................................................................7
Bibliography....................................................................................................................................8

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Introduction
The aim of the report is to conduct the critique analysis of the article that is related to the
concepts of the database, data mining, and data analyses. The article is linked to the data mining
and linked open data that reflects the new perspective for analysing the data in the research of
environmental. The article reflects that rapid development in the computer technology as well as
in the information system has allows the immense growth in collection as well as storage of the
digital data volumes that digital data that does not automatically correlate with the advances and
new insights in understanding the related data. The new technique of data mining provides the
promising manner to extract the knowledge as well as patters from the large, multidimensional,
as well as complex sets of data. Further, the report includes the review of the article, which
shows the intention as well as the content. The paper also includes the research method that is
considered by the author while conducting the research. The article includes the different
problems or issues that are presented in their research with the conclusion of the article.

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Intention and content of the article
The intention of the author in the article aims to offer comprehensive overview related to the
present techniques of the data mining as well as the aligned tools that demonstrate the potential
linked with the research of the data mining areas by the means of the applications example. The
article intention is to encourage those of the research scientist that do not have the programming
of extensive as well as data mining knowledge in order to take the benefit of present data mining
tools that benefits of data mining tools. This embraces the classical data mining as well as
approaches of the LOD in support to gain more insight as well as patterns of recognizing in the
highly complex data sets.1 It has been found in the intention of the article that despite the
number of the conventional data techniques as well as methods, the classical approaches are
generally restricted to isolated data sets and due to which it remains likely majorly stand alone
with the specialised in nature. The article content shows that highly complex and majorly
interdisciplinary questions that are included in the environmental research remained unanswered
importantly using the area-based data mining or isolated approach. Further, it includes the linked
open data (LOD) approach that will reflect as a new possibility that leads to the support of inter-
disciplinary as well as complex data mining analysis. 2 The intention of the author is to reflect
the views related to the merit of LOD that are explained with the use of examples majorly from
the environmental as well as the medicine research. Further, it includes the benefits related to the
LOD data mining that will be weighed against the classical techniques of the data mining.
The author of the article is also willing to present the views about the easy-to-learn source data
mining tools that include RapidMiner and KNIME as these are considered as good and simple. 3
These tools enable the complex processes of the data mining to be generated without any prior

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compute knowledge related to the programming that can easily assist in helping for the graphical
user interfaces. Further, it includes that many data mining related functions can be added to those
by means of plugins as well as connectors to WEKA and R/Rattle are available.
Research method
The author has conducted the research for the article is Qualitative research that is a method of
collecting the observation to gather non-numerical data.4 This method helps the author to get the
insights into the problem and helps to form the ideas as well as the hypotheses for the potential
quantitative research. Qualitative data collection methods are used by authors might vary using
semi-structure or unstructured techniques. Some of the methods include the focus groups,
individual interviews, and observations. In the research, the author has used the observation
method for gathering the information and details related to the data mining as well as linked open
data that shows new perspective for analyses in the research of the environmental.5 This method
is described as being a systematic observation method that is applied on those observation
techniques that are sensible as well as replicable procedures that supports the research that could
be reproduced.
Findings
The findings of the articles say that rapid development of the information technology in the 21st
century is linked with the increase in the volume of the data as well as its complexity. The gains
from the insight for such kind of the data are not even holding up to the expectation because the
modern data analyses techniques are not used for wide purpose. It has been found that data
mining provides the promising solution with the different approaches for the knowledge

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discovery in the complex as well as in the multi-dimensional. Presently, the data-mining
techniques are majorly used in the research in the analytical areas like computer science,
mathematics and many others.6 It has been found that data mining processes, methods, types that
were depicted in the understandable method as it is important references that are offer to present
open source data mining tools that are majorly characterized by intuitive use in the order to assist
the complex data analysis.
The findings of paper provide the comprehensive details related to LOD. The paper include the
comparison related to the traditional techniques data mining, references is also made to the
benefits of LOD and existing open source and commercial LOD tools. The approach of LOD is
very new concept majorly for the integration with the huge potential with the motive to analyse
the interdisciplinary as well as the complex distributed sets of data. It has been found that the
LOD approach will facilitate to search for the novel insights from and novel association among
the complex as well as the interdisciplinary sets that lies on the knowledge that will be able to
deal with the difficult problems and make the effective predictions. 3
Problems or issues
The article reflects different types of issues that are presented by the author in the context of data
mining, database and data analyses.
Spatial Data Mining: - It has been found that the spatial data mining majorly pursues that aim
of discovering the large patters, multi-dimensional spatial data sets that are majorly formed by
remote sensing techniques present in the observations of the earth. It has been found that spatial-
temporal autocorrelations is more difficult when comparing it with the traditional numeric as
well as the categorical data. 7

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Potential rise in databases: - Author reflected in the article that the potential growth in the
databases in all the related areas makes the analysis of the huge amounts of the more complex
details in more difficult as well as in less clear manner. This leads to the issues for the companies
who deal with the complex database. 8
Issues in environmental research
In the article, the authors have presented the different issues or challenges that are witnessed in
the environmental research in the present as well as in the future. These issues include –
 The major issue is dealing with the enormous improvement in the volume of data as well
as the complexity from the different kind of disciplines.
 The issues related to the interdisciplinary nature and interaction related to the different
disciplines such as law, ecology, economics, toxicology, and many others.
 The problem might occur related to the impact on social, economic, environmental, as
well as planning decisions, complex data that is presently present to be brought into the
perspective related with its historical change as well as the development. 9

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Conclusion
In the end, this can be concluded that there is rise in the management of the data mining because
of advance computer technology. This task has been prepared majorly on the database system
that includes the title of Data mining and linked open data – New perspectives for data analysis
in environmental research. The paper majorly reflects the intention as well as the content that is
linked to the article and willing to express by the authors. Further, it reflects the research method
used by the author in conducting the research. The findings of the article are also discussed
which is supported with the article. It also includes the problem or issues that are presented by
author in the article.

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Bibliography
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[1] Pang-Ning Tan, Introduction to data mining. India: Pearson Education, 2018.
[2] Shengyi Pan, Thomas Morris, and Uttam Adhikari, "Developing a hybrid intrusion detection
system using data mining for power systems," IEEE Transactions on Smart Grid, vol. 6, no.
6, pp. 3104-3113, 2015.
[3] Angela Lausch, Andreas Schmidt, and Lutz Tischendorf, "Data mining and linked open
data–New perspectives for data analysis in environmental research," Ecological Modelling,
pp. 5-17, 2015.
[4] Ian Witten et al. , Data Mining: Practical machine learning tools and techniques.: Morgan
Kaufmann, 2016.
[5] Junlan Feng, Luciano De Andrade Barbosa, and Valerie Torres, "Systems and methods for
social media data mining," U.S. Patent No. 9,262,517, 2016.
[6] Charu C. Aggarwal, Data mining: the textbook.: Springer, 2015.
[7] Furqan, et al. Alam, "Analysis of eight data mining algorithms for smarter Internet of Things
(IoT)," Procedia Computer Science 98, pp. 437-442, 2016.
[8] Stanislav S., R. Borysov, Matthias Geilhufe, and Alexander V. Balatsky, "Organic materials
database: An open-access online database for data mining," PloS one, vol. 12, no. 2, 2017.
[9] Feng et al. Chen, "Data mining for the internet of things: literature review and challenges,"

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nternational Journal of Distributed Sensor Networks , vol. 11, no. 8, p. 431047, 2015.
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