Benchmarking Business Analytics Techniques in Big Data

Oliveira, Catia; Guimarães, Tiago; Portela, Filipe; Santos, Manuel Filipe

doi:10.1016/j.procs.2019.11.026

Cited by 10 publications

(9 citation statements)

References 8 publications

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“…With the development of technology and the increasing dependence of organizations and society on the use of the internet, resulting in the growth and variety of data that is increasingly large. The growth and variation of this large data becomes a challenge in conducting business data analysis, to help carry out the Big Data process, we need a framework that can assess the performance of several data mining tools, such as RapidMiner [16], [17], [18]. Several studies have conducted benchmarking on data mining tools for large data such as RapidMiner and KNIME.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Number of Participants in The Online Examination System at Universitas Terbuka

2020

IJATCSE

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The Online Examination System (SUO) conducted by the Universitas Terbuka (UT) is now one of the exams that is increasingly in demand by Universitas Terbuka students. The Online Examination System is implemented in all branches of the Open University in major cities in Indonesia. The Amount of Interest Students make UT need to predict how much interest is the registration of SUO, so that UT can prepare facilities and infrastructure so that the exam can run smoothly. Regression is a method that can be used in analyzing predictions, several regression models such as Multiple Linear Regression, Decision Tree, Support Vector Regression and Artificial Neural Network are used in this study. In addition to obtaining accurate predictions, in machine learning a feature selection is added to eliminate unrelated data and also duplicate data. Cities were grouped using the K-Means method. Decision tree model without feature selection has good performance with RMSE = 169.047 +/-0.000 and R2 = 0.749. And for the ANN model combined with optimize selection (evolutionary) it has a good performance value of RMSE = 115.023 +/-38,183 (micro average: 120,592 +/-0.000) and R2 = 0.841 + / -0.082 (micro average: 0.824) with several attributes related to the prediction process.

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Section: Methodsmentioning

confidence: 99%

Prediction of Number of Participants in The Online Examination System at Universitas Terbuka

2020

IJATCSE

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“…It can build anything using Python. Besides that, it is a great for backend web development, data analysis, artificial intelligence and scientific computing [46]. Pythons syntax come along with modules and package to support the task.…”

Section: E Machine Learning Toolmentioning

confidence: 99%

“…The module and package encourage program modularity and code reuse, so very suitable for Rapid Application Development [43]. It simple, easy to learn the syntax and very flexible [45,46].…”

Section: E Machine Learning Toolmentioning

confidence: 99%

Prediction of Mental Health Problems among Higher Education Student Using Machine Learning

Shafiee¹,

Mutalib²

2020

IJEME

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“…Multiple data mining techniques can be discussed, but the main techniques cited for finding patterns are regression, clustering, and classification. As suggested by the name, classification classifies data into different classes, and the goal is to create a set of classification rules that can be used to predict behavior or answer a question [7]. Regression is used to predict a range of numeric values, focused mainly on the relationship between two variables such as sales and day of the week [8].…”

Section: Data Warehousing To Data Miningmentioning

confidence: 99%

“…Given that the focus in this study is on SME's the focus would be on open-source tools which are freely available without a commercial license, encouraging widespread adoption and innovation. When [29] [7]. Each system essentially relies on aspects of statistics, mathematics and computing combined, and should be used in conjunction with other systems as part of an overall rounded approach to knowledge discovery within the business.…”

Section: Data Mining For Sme: a Modelmentioning

confidence: 99%

Data Mining for Small and Medium Enterprises: A Conceptual Model for Adaptation

Saeed¹

2020

IIM

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The following paper explored data mining issues in Small and Medium Enterprises' (SMEs), firstly exploring the relationship between data mining and economic development. With SME's contributing most employment prospects and output within any emerging economy such as the Kingdom of Saudi Arabia. Adopting technology will improve SME's potential for effective decision making and efficient operations. Hence, it is important that SMEs have access to data mining techniques and implement the most suited into their business to improve their business intelligence (BI). The paper is aimed to critically review the existing literature on data mining in the field of SME to provide a theoretical underpinning for any future work. It has been found data mining to be complicated and fragmented with a multitude of options available for businesses from quite basic systems implemented within Excel or Access to more sophisticated cloud-based systems. For any business, data mining is trade-off between the need for data analysis, and intelligence against the cost and resource-use of the system put in place. Multiple challenges have been identified to data mining, most notably the resource-intensive nature of systems (both in terms of labor and capital) and the security issues of data collection, analysis and storage; with General Data Protection Regulation (GDPR) a key focus for Kingdom of Saudi Arabia businesses. With these challenges the paper suggests that any SME starts small with an internal data mining exercise to digitalize and analyze their customer data, scaling up over time as the business grows and acquires the resources needed to properly manage any system.

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Benchmarking Business Analytics Techniques in Big Data

Cited by 10 publications

References 8 publications

Prediction of Number of Participants in The Online Examination System at Universitas Terbuka

Prediction of Number of Participants in The Online Examination System at Universitas Terbuka

Prediction of Mental Health Problems among Higher Education Student Using Machine Learning

Data Mining for Small and Medium Enterprises: A Conceptual Model for Adaptation

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