Future Expectations About Big Data Analytics

1. Introduction

In many applications big data can be integrated from a variety of sources and termed heterogeneous data (Heterogeneous big data). However, in the future, users ‘u’, and devices ‘d’ will increase which generates heterogeneous data (text, audio, images, video etc.) that requires handling & retrieving important information. Future expectation from big-data processing applications requires developing applications based on speedy algorithms representing this heterogeneous data in aggregate form. However, a centralized coordinator will be necessary if the system involves multiple users, multiple systems etc. In the future, proper coordination is the key challenge in handling and processing the big-data. In the future focus should be on developing applications to handle ‘contextual data’. These applications can be termed as Next-Generation-big-data (NGBD) processing contextual data. Applications of context data are traffic handling, weather forecasting etc. Therefore processing historical as well as current data requires developing NGBD applications. We strongly believe these types of systems will serve the next generation of businesses.

If observing the trend of digitization, one can conclude that ‘n’ users are increasing and the simultaneous increase in the ‘n’ users added large volume of data in the system. However, this large volume of data is heterogeneous because it is integrated from different sources. Transformation of the data in unique representation is necessary for analysis purposes. Big data analytics helps users fulfilling their daily business needs, identify the customer segment and forecast business strategies. Traditional learning does not support in-depth learning whereas recent machine learning (ML), deep learning (DL) uses supervised & unsupervised learning techniques. Though the datasets are large & consist of ‘n’ records/values using classification methods one can classify the features of the datasets. Among those records/values, one can select a few records as a representative value instead of considering all the records from the given dataset which is known as sampling.

To store this large volume of data on a bigger platform ‘cloud’ service is necessary and one can go for open source cloud service providers. Though, this big data resides on the cloud there could be some risks associated with the storage. These risks can be mitigated using the various applications such as Cassandra service if there is any issue with the data and safe location can be determined by querying the measurable property such as write/read errors with the disks. A future expectation about big data gives more suggestions like:

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  User must use the data more efficiently & effectively

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  To process the large volume of data more sophisticated algorithms are required

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  Along with these parameters there is a more requirement of more intelligent and fast machines.