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Business Intelligence, Research About Big Data Analytics

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Hello everyone, this is my first post about Business Intelligence, I hope you would find this useful!

1.      Abstract

The contents of this post include big data and analytics related topics and this post will also try to answer the following questions about big data and analytics. How can data mining techniques be used in big data analytics, which architectures can be used for this purpose, how many machine learning methods can be used for big data analytics, how can the big data be handled in different platforms.

Keywords: Big data, Analytics, Big data analytics, Data mining techniques, machine learning, Bayes algorithms, clustering, classification

2.      Introduction

The contents of this post include the definition of big data and analytics and try to answer common questions about big data and analytics. The main questions that will be handled in this post will be how can data mining techniques be used in big data analytics, which architectures can be used for this purpose, how many machine learning methods can be used for big data analytics, how can the big data be handled in different platforms. First concept to be discussed is what is big data. According to Oracle, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them  [1]. The analytics is another important concept to grasp in order to better understand the topics of the post. Conforming to Oracle, analytics is the process of discovering, interpreting and communicating significant patterns in data. Analytics helps us to observe data in a meaningful way [2]. Since the big data is larger and more complex than traditional data, the analytics helps the observers to gain meaningful information about the gathered data. Therefore the assumption for big data analytics as to be analytics used on big data can be made safely. As reported by IBM, big data analytics is the use of advanced analytic techniques against very large, diverse big data sets [3]. Since the post has explained the key concepts of big data and analytics, now it will try to answer the raised questions. In the methods section of the post, the questions asked about data mining techniques, and machine learning methods that can be used for big data analytics will be answered in the methods section of this post.

3.      Methods

First question that comes to mind is how can big data be handled in different platforms? With the help of tools such as hadoop, domo, cloudera, amazon redshift the big data can be handled in the business intelligence. Also cloud services such as Google Cloud, Microsoft Azure, Amazon Web Services (AWS) offer specialized tools for big data management for their own specialties. Many big data platforms combine both relational and non-relational technologies whereas others rely on Hadoop and NoSQL. In consonance with Wayne Eckerson’s article, the flagship for the success of a big data platform lies on the number and variety of applications it supports [4].

The second question is about machine learning methods that can be used for big data analytics. There are three types of algorithms in machine learning that can be used for big data classification and these are supervised, semi-supervised and unsupervised. As for the supervised learning algorithms, according to an article from jigsawacademy written by Jyotsna, the most commonly used ones include, maximum entropy method, support vector machines, naive bayes, boosting algorithm [5]. There is one key difference between classification and regression algorithms and that is in classification, the class attribute of a set is discrete whereas in the regression case it is continuous [5]. In the unsupervised learning, the algorithms take unlabelled data and classify it by drawing a comparison among data features stated by Jyotsna from jigsawacademy [5]. In the classification part, the post mentioned that there are 3 classification algorithms, if an algorithm uses unsupervised learning, it can utilize supervised clustering which works best when there are target variables and training sets, it can utilize unsupervised clustering which works best on a very specific object function and semi-supervised clustering which is the hybrid version of those mentioned previously [5].

For the final question, there are 5 data mining techniques and these are association, classification, clustering, decision trees  and sequential patterns. According to an article from IBM, data mining involves processing data and identifying patterns and trends in that information [6]. Association makes a correlation between two or more items to identify a pattern, classification means that multiple attributes can be used to identify a particular class of items as reported by datafloq article [7]. In this post, due to the time limitations and page limitations, other data mining techniques will not be touched upon.

4.    Conclusions

In big data, this post mentioned some keywords about big data, data mining and try to answer some common questions about big data.

5.    References

[1] What is Big Data? Oracle, Available: https://www.oracle.com/big-data/what-is-big-data/ Accessed: 08.04.2022

[2] What is Analytics? Oracle, Available: https://www.oracle.com/business-analytics/what-is-analytics/ Accessed: 08.04.2022

[3] Big Data Analytics. IBM, Available: https://www.ibm.com/analytics/big-data-analytics Accessed: 08.04.2022

[4] Which Big Data Platform Is Right For You? Wayne Eckerson, Available: https://tdwi.org/articles/2016/02/05/which-big-data-platform.aspx Accessed: 08.04.2022

[5] ML and AI Algorithms for Big Data Classification, jigsawacademy, Available: https://www.jigsawacademy.com/ml-and-ai-algorithms-for-big-data-classification/ Accessed: 08.04.2022

[6] What is data mining, IBM, Available: https://www.ibm.com/cloud/learn/data-mining Accessed: 08.04.2022

[7] 5 Major Data Mining Techniques Being Used by Big Data, datafloq, Available: https://datafloq.com/read/5-major-data-mining-techniques-being-used-big-data/ Accessed: 08.04.2022


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