Machine Learning is a branch of computer science, a field of artificial intelligence. It is a data analysis method that further helps to automate the analytical model structure. Alternatively, as the word suggests, it allows the machines (computer systems) to learn from the data without external help in making decisions with the least human interference possible. With the development of new technologies, machine learning has changed a lot over the last few years.
Let’s discuss what Big Data is?
Big data means too much information and analysis means analyzing a large amount of data to filter the information. A human cannot perform this task effectively within a time limit. So here is the point where machine learning for big data analytics comes into play. Let’s take an example, suppose you are an owner of the company and need to collect a large amount of information, which is very difficult on your own. Then you begin to find a clue that will help you in your business or make decisions faster. Here you realize that you are dealing with tremendous information. Your analysis needs a little help to make the search successful. In the machine learning process, the more data you provide to the system, the more the system can learn from it and return all the information you searched, thus making your search successful. That’s why big data analysis works so well. Without big data, it cannot work to its optimum level due to the fact that with less data, the system has few examples to learn from. So we can say that big data plays a big role in machine learning.
Instead of different advantages of machine learning in analyzing there are also different challenges. Let’s discuss them one by one:
Learning massive data: With the development of technology, the amount of data we process day by day increases. In November 2017, it was found that Google processes approx. 25PB pr. Nowadays, over time, companies will traverse these petabytes of data. The most important attribute of data is Volume. So dealing with such a huge amount of information is a huge challenge. To overcome this challenge, distributed computing frameworks should be preferred.
Learning different types of data: There is a lot of different data today. Variation is also an important feature of big data. Structured, unstructured and semi-structured are three different types of data that further result in the generation of heterogeneous, non-linear and high-dimensional data. Learning from such a large data set is a challenge and further results in an increase in data complexity. To overcome this challenge, data integration should be used.
Learning high speed streamed data: There are various tasks that include completing the work for a specific period. Speed is also one of the key features of big data. If the task is not completed for a specified period of time, the results of the treatment may become less valuable or even worthless. For this you can take the example of stock market prediction, earthquake prediction etc. So it is very necessary and challenging task to process big data in time. To overcome this challenge, online learning must be used.
Learning ambiguous and incomplete data: In the past, the machine learning algorithms provided more accurate data relatively. So the results were accurate at that time as well. However, today there is an ambiguity in the data because the data is generated from various sources which are also uncertain and incomplete. So that’s a big challenge for machine learning in big data analytics. Examples of uncertain data are the data generated in wireless networks due to noise, shadow, fading, etc. To overcome this challenge, distribution-based approach should be used.
Learning low value density data: The main purpose of machine learning for big data analysis is to extract useful information from a large amount of data for commercial benefits. Value is one of the most important attributes of data. It is very challenging to find the significant value from large amounts of data with a low value. So that’s a big challenge for machine learning in big data analytics. To overcome this challenge, Data Mining technologies and knowledge discovery in databases should be used.