SIT717 2018T2 - Enterprise Business Intelligence Oral Presentation

This presentation provides an overview of Enterprise Business Intelligence, focusing on data mining and business modeling. It begins with an introduction to the growing popularity of business intelligence and the use of social networks for data analysis, highlighting the challenges of unstructured data. The presentation then reviews literature on business intelligence and supervised data mining, emphasizing its importance in organizational decision-making. It delves into supervised and unsupervised data mining methods, including information crawling, tokenization, and visualization techniques. Furthermore, it discusses association rule and neural network techniques. The evaluation and demonstration section showcases the application of these tools, focusing on supervised information extraction. The report also covers data reduction, data mining tasks, and algorithm selection. The presentation concludes by emphasizing the role of data design in business intelligence, with references to relevant research.

enterprise business
intelligence

Introduction.
 The rate of increase in business popularity such as facilitated has
facilitated users to exchange messages and information needed for
data analysis for different purposes ranging from business
intelligence to the issue of security is one face that is very important
to the public. The social network is being used by a large number of
people for events, update and sentiments exploration. The fact that
tweets are given certain structure during tweeting, the messages
presented does not follow grammatical structure and passing
techniques due to an increment and speech to an individual words.

introduction
 This paper presents a proposal of a statistical based approach as well
as identifying significant factors related to modeling of the business.
The method presents a method of generation graph which considers
node and the degree of similarity that could exist between as a
weighed edge between the tweets and the way the tweets work

Literature Review
 The literature review is basically presented to discuss about business
intelligent and supervised way of data mining technology and its
understanding. From several articles on research, business
intelligence is very key in decision making in most of the
organizations use. In this literature part, the research tend to present
a detailed discussion on the intelligence and how the entire idea of
business intelligence work to make the work of the managers easier.

Supervised data mining
 The supervised data classification is started as the main method. The
classification can be of different types especial when applied to
different business. Other method such as regression can also be used
to a target value as per the numerical as opposed to performing data
categories. All the values must be assessed through the use of the
organization in order for the entire data to get all the desired
outcomes. The process is also known the predictive data mining
because it has capability to proceed the user data and numerical.

Proposed methods
 This section presents functional details for all the unsupervised data
mining and approach that is used to get the information about the
business. To make the work easy for the users, the methods is
presented in a workflow all the methods that are highlighted. One of
the method to be used is the information crawling, information
tokenization to analyze the business contents and remove unwanted
contents(O'Leary, 2018). Secondly the information is also viewed as
the first hand information because it comes directly from the people
who uses social media on a daily basis. Extracting useful information
from the data and information has become very much easier than
collecting information

Visualization Techniques
 Visualization techniques is used because it is a very useful method for
discovering partners in the data sets and may be actually used at the
begging of the data mining process. In this technique, there is a
whole field of research that is dedicated to the search of the
projection that the user is interested in. This projection is also known
as the projection pursuit. For instance the cluster are usually
numerically represented by different numerical reprobation.

Association Rule Techniques
 The association rule will tell us about the association that exist
between two or more data. For instance when there are two similar
methods that is coming from the managers, the association rule will
help the analysis determine the association. The main task
determined by association rule is to find out the presence of various
items that is within ascertain databases. For us to use this rule
successfully, two pieces of information must be put into
consideration. The first is to make sure that there is support were the
rule lies (Chattamvelli, 2011 pg345).

Neural Network Technique
 The artificial and neural network are called this name because of
historical development that stated with the knowledge that machine
can be due to this and do things lie human beings. This was possible
only if the scientist can find a way to mimic its structure and its
functioning the way human being are functioning. To use this
techniques to analyst the data, the researcher uses two main
techniques and this two techniques corresponds to human brain and
link. It also corresponds to the neurons and the human brain at all
points

Evaluation and demonstration
 This part demonstrate that use of the three tools to analysis data and
come up with meaningful information about the data in question. It is
important to note that information given in this case should be
relevant and in line with the results. Supervised information
extraction is a process that needs so much accuracy because there
are so many opinion posted by the users. According to most
companies, the above techniques helps managers to make decision
according to the information that is provided and classified. There is
no techniques that can be presented and can be made effective apart
from simple supervised solution

Evaluation and demonstration
 The following observation was deduced from the application it is
important that the researchers have their knowledge and the goals of
the people who are posting information on the social media. That
helped in creating data set and selecting data set as well as focusing
on the variable

Data reduction
 Data reduction was also done to reduce or to remove some of the
attributes a process the will help suit the set to the goal. Next is
choosing the data mining task. This is determined whether the goal of
KDD is well achieved. After data reduction and isolation, the best
algorithm is chooses for the best method to be used for searching
patterns in the data. This process also involves deciding the
appropriate model and pattern. Finally is data mining for the
information and for the representational messages is done

Report
 The report is presented to demonstrate the use of data mining in
business intelligence. It takes a keen look at the supervised data
mining method. This is basically called supervised data mining simply
because there are several data that is expected or outcome that is
expected. The main aim of this report was to report the important of
data mining and business modeling

Conclusion
 Method and working process are presented to ensure that the reader
understands the way data mining is done and how to make sure that
the entire part of the report is accomplished using supervised method
and data mining. It can be concluded that data design is entirely
required in business intelligence.

References
 O'Leary, D.E., 2018. Open Information Enterprise Transactions:
Business Intelligence and Wash and Spoof Transactions in Blockchain
and Social Commerce. Intelligent Systems in Accounting, Finance and
Management, 25(3), pp.148-158.
 Yeoh, W. and Popovič, A., 2016. Extending the understanding of
critical success factors for implementing business intelligence
systems. Journal of the Association for Information Science and
Technology, 67(1), pp.134-147.
 Sharda, R., Delen, D. and Turban, E., 2016. Business intelligence,
analytics, and data science: a managerial perspective. Pearson.