Machine Learning Algorithms for Big Data
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Machine Learning Algorithms for Big Data


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Big data provides useful information and knowledge from multiple systems and from data generated on the internet. However, handling such huge chunks of data need robust tools, knowledge and automated systems which can handle large datasets in real time. Towards this trend, machine learning methods help organizations across various industries to integrate big data sources with proprietary data for analysis by which organizations can realize the new potential and enhance their performance from new uncovered or extracted insights. In this report, discussions related to machine learning methods for their use in big data is provided. The categories of machine learning methods and their associated algorithms are also provided in brief. It must be noted that there is no one size fits all, and hence the algorithm chosen must be done appropriately to handle the big data problem. The report also highlights the application uses of machine learning in industry sectors to emphasize its need for managing big data.

Keywords: Big data, Analytics, Machine, Learning, Learning Algorithms, supervised, semi-supervised, reinforcement learning


Introduction and Background 

Big data refers to huge chunks of heterogeneous data in the order of petabytes and there is no standard definition available for big data. Big data is not a terminology instead at a conceptual level it is easy to identify data from multiple data sources on the internet. Big data can be described as volume, velocity, variety, veracity, and value. Volume refers to the measure of information, velocity is the speed of information extraction, variety is the nature of data or arrangement of information, veracity explains the reliability of information and value refers to the insights and beneficial effects of extracted information. Organizations, in order to realize the benefits from big data, are integrating big data sources with their proprietary data for analytics and business intelligence activities (Gandomi & Haider, 2015).

Big data consists of both structured and unstructured data, and big data is used by organizations in obtaining useful information and insights related to their performance and operational strategies. Further, big data is accessible and available at all times as applications and services on the internet are available without disruptions. This is possible through technologies such as cloud computing, mobile computing, and so on powered by data centers that are up and running even during unexpected events such as hardware failure, or cyber-attack or natural disaster. Due to large volumes of datasets available in big data, processing such data using conventional database system or software model is not effective or is not possible (De Mauro, Greco, & Grimaldi, 2015). In this situation, machine learning (ML) comes into play. ML is a branch of artificial intelligence (AI) that provides the technique for data analysis and automates the creation of analytical models with minimum human intervention. Irrespective of data sizes or type, ML supports organizations to manage, analyze and use big data effectively by providing intelligent automation. ML is a set of algorithms and methods with abilities to learn from data patterns and make decisions based on the identified pattern in data. It also helps businesses and organizations to analyze complex data, mine useful information from big datasets, uncover hidden patterns, provide insights on market trends and so on with accuracy. ML automates analytical models at speed and scale which is not possible by humans (Witten, Frank, Hall, & Pal, 2016) . The report discusses the use of ML algorithms for big data processing and provides details on real-world applications of machine learning for managing big data. The focus here is the discussions related to ML procedures and, different algorithms in ML with regard to big data applications in general.

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