scholarly journals Predicting Academic Course Preference using Hadoop

2019 ◽  
Vol 8 (4) ◽  
pp. 2527-2530
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
New Technologies ◽  
Parallel Applications ◽  
Distributed File System ◽  
Industrial Training ◽  
Effective Learning ◽  
Meaningful Information ◽  
Learning Techniques ◽  
Mining Tools

These days new technologies have been introduced by this new academic trends also have been came into existence into the education system. And this leads to huge amounts of data which makes a big challenge for the students to store the preferred course. For this many data mining tools have been invented to convert the unregulated data into structured format to understand the meaningful information. As we know that Hadoop is a distributed file system which is used to hold huge amounts of data this stores the files in a redundant fashion across multiple machines. Due to this it leads to failure and parallel applications do not work. To avoid this problem we are using Mapreduce for decision making of students in order to choose their preferred course for industrial training purpose for their effective learning techniques to increase their knowledge and capability.

Organizational Data Mining (ODM)

2011 ◽  
pp. 1-8
Author(s):  
Hamid R. Nemati ◽  
Christopher D. Barko
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Competitive Advantage ◽  
Current Status ◽  
Decision Making Process ◽  
Actionable Knowledge ◽  
The Core ◽  
Mining Tools

An increasing number of organizations are struggling to overcome “information paralysis” — there is so much data available that it is difficult to understand what is and is not relevant. In addition, managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. Organizational Data Mining (ODM) is defined as leveraging data mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage (Nemati & Barko, 2001). The fundamentals of ODM can be categorized into three fields: Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the core differentiator between ODM and data mining. We take a brief look at the current status of ODM research and how a sample of organizations is benefiting. Next we examine the evolution of ODM and conclude our chapter by contemplating its challenging yet opportunistic future.

Improved Privacy

2011 ◽  
pp. 295-316
Author(s):  
K. Abumani ◽  
R. Nedunchezhian
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Empirical Analysis ◽  
Time Complexity ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Sensitive Information ◽  
Data Mining Techniques ◽  
Novel Method ◽  
Mining Tools

Data mining techniques have been widely used for extracting non-trivial information from massive amounts of data. They help in strategic decision-making as well as many more applications. However, data mining also has a few demerits apart from its usefulness. Sensitive information contained in the database may be brought out by the data mining tools. Different approaches are being utilized to hide the sensitive information. The proposed work in this article applies a novel method to access the generating transactions with minimum effort from the transactional database. It helps in reducing the time complexity of any hiding algorithm. The theoretical and empirical analysis of the algorithm shows that hiding of data using this proposed work performs association rule hiding quicker than other algorithms.

Data Mining Fundamental Concepts and Critical Issues

2011 ◽  
pp. 418-423
Author(s):  
John Wang ◽  
Qiyang Chen ◽  
James Yao
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Classification Model ◽  
Data Warehouses ◽  
Business Decisions ◽  
Large Databases ◽  
Critical Issues ◽  
Mining Tools

Data mining is the process of extracting previously unknown information from large databases or data warehouses and using it to make crucial business decisions. Data mining tools find patterns in the data and infer rules from them. The extracted information can be used to form a prediction or classification model, identify relations between database records, or provide a summary of the databases being mined. Those patterns and rules can be used to guide decision making and forecast the effect of those decisions, and data mining can speed analysis by focusing attention on the most important variables.

scholarly journals Understanding the Investors’ Investment Decisions through Some Data Mining Tools- A Study in Dhaka Stock Exchange (DSE)

2018 ◽  
pp. 9-19
Author(s):  
Md. Khashrul Alam ◽  
S. M. Towhidur Rahman ◽  
Afifa Khanom
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Stock Market ◽  
Stock Exchange ◽  
Investment Decision ◽  
Scientific Data ◽  
Wrong Decision ◽  
Secondary Sources ◽  
Money Flow ◽  
Mining Tools

Purpose: Decision making is the process of choosing a particular alternative from a number of alternatives. Decision making is very much important in investment in the stock market. As it is enormously sensitive, a wrong decision may put the investor back to the street. Modern scientific data mining tools can play important role in making investment decision in the stock market. The purpose of the study is to find out the effectiveness of investors’ decision in buying and selling stock and the efficiency of some data mining tools in aiding investor’s decision. Methodology: This paper used several data mining techniques such as beta, Chaikin money flow indicator (CMI) and Bollinger band to analyze investors’ decision in buying and selling stocks. Data for the study were taken both from primary and secondary sources specially, from website of Dhaka Stock Exchange. Findings: The result shows that in most cases majority of investors failed to take right decision in right time in terms of the estimation derived from data mining tools used in the study. It was also found that Bollinger band was found to be more efficient than CMI in making prediction.

Organizational Data Mining (ODM)

2008 ◽  
pp. 2289-2295 ◽  
Author(s):  
Hamid R. Nemati ◽  
Christopher D. Barko
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Competitive Advantage ◽  
Current Status ◽  
Decision Making Process ◽  
Actionable Knowledge ◽  
The Core ◽  
Mining Tools

An increasing number of organizations are struggling to overcome “information paralysis” — there is so much data available that it is difficult to understand what is and is not relevant. In addition, managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. Organizational Data Mining (ODM) is defined as leveraging data mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage (Nemati & Barko, 2001). The fundamentals of ODM can be categorized into three fields: Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the core differentiator between ODM and data mining. We take a brief look at the current status of ODM research and how a sample of organizations is benefiting. Next we examine the evolution of ODM and conclude our chapter by contemplating its challenging yet opportunistic future.

Designing a Training Platform for Higher Education Engineering Instructors in the Digital Era

2020 ◽  
pp. 53-69
Author(s):  
Fırat Sarsar ◽  
Özge Andiç Çakır
Keyword(s):  
Higher Education ◽  
Online Learning ◽  
Learning Process ◽  
New Technologies ◽  
Effective Learning ◽  
Digital Era ◽  
Learning Platform ◽  
Learning Techniques ◽  
Creative Learning

Higher education (HE) should focus on solving the following critical educational problems: (1) using technology and (2) fostering education by new creative learning techniques. In this chapter, the authors indirectly talk about using new technologies in education. There are many reasons that make this choice challenging such as believing in the benefits, having enough knowledge, accessing alternative technological sources, etc. To facilitate this, they introduce an online learning platform for engineering instructors in HE. Moreover, according to their experiences in the field of education and engineering, instructors in HE should improve and revise their skills and knowledge. It is important to enhance knowledge on content, technology, and pedagogy; therefore, this training platform itself focuses on improving those skills necessary for instructors in HE for maintaining an effective learning process. This chapter mainly focuses on creating a course for higher education engineering instructors and a TERR model that is proposed by the authors.

scholarly journals Bayesian Kernel Methods

2021 ◽  
Vol 6 (1) ◽  
pp. 26-39
Author(s):  
Arti Saxena ◽  
Vijay Kumar
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Medical Decision Making ◽  
Kernel Method ◽  
Machine Learning Techniques ◽  
Medical Decision ◽  
Learning Tools ◽  
Meaningful Information ◽  
Learning Techniques ◽  
Available Information

In the healthcare industry, sources look after different customers with diverse diseases and complications. Thus, at the source, a great amount of data in all aspects like status of the patients, behaviour of the diseases, etc. are collected, and now it becomes the job of the practitioner at source to use the available data for diagnosing the diseases accurately and then prescribe the relevant treatment. Machine learning techniques are useful to deal with large datasets, with an aim to produce meaningful information from the raw information for the purpose of decision making. The inharmonious behavior of the data is the motivation behind the development of new tools and demonstrates the available information to some meaningful information for decision making. As per the literature, healthcare of patients can be analyzed through machine learning tools, and henceforth, in the article, a Bayesian kernel method for medical decision-making problems has been discussed, which suits the purpose of researchers in the enhancement of their research in the domain of medical decision making.

Rapid Privacy Preserving Algorithm for Large Databases

2009 ◽  
pp. 1050-1061
Author(s):  
K. Anbumani ◽  
R. Nedunchezhian
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Time Complexity ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Sensitive Information ◽  
Data Mining Techniques ◽  
Large Databases ◽  
Novel Method ◽  
Mining Tools

Data mining techniques have been widely used for extracting non-trivial information from massive amounts of data. They help in strategic decision- making as well as many more applications. However, data mining also has a few demerits apart from its usefulness. Sensitive information contained in the database may be brought out by the data mining tools. Different approaches are being utilized to hide the sensitive information. The proposed work in this article applies a novel method to access the generating transactions with minimum effort from the transactional database. It helps in reducing the time complexity of any hiding algorithm. The theoretical and empirical analysis of the algorithm shows that hiding of data using this proposed work performs association rule hiding quicker than other algorithms.

Data Mining

2011 ◽  
pp. 167-172
Author(s):  
Richard Peterson
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Classification Model ◽  
Data Warehouses ◽  
Business Decisions ◽  
Large Databases ◽  
Mining Tools

Data mining is the process of extracting previously unknown information from large databases or data warehouses and using it to make crucial business decisions. Data mining tools find patterns in the data and infer rules from them. The extracted information can be used to form a prediction or classification model, identify relations between database records, or provide a summary of the databases being mined. Those patterns and rules can be used to guide decision making and forecast the effect of those decisions, and data mining can speed analysis by focusing attention on the most important variables.

Sign in / Sign up

Export Citation Format

Share Document