Application of AI in Big Data Processing

1. Introduction

Massive amounts of data and artificial intelligence have been around for a long time. Despite this, most firms believe that they are further ahead in adoption than they would want to be and that both areas constitute a danger to the viability of their company in the future due to the complexity associated with each of these areas. The analysis of large amounts of data and machine learning go together because they let businesses take use of their vast data resources while also delegating much of the laborious task of searching through various data sets for insights to computer algorithms.

Figure 1.

Schematic diagram of processing of data

Big data AI is a revolutionary approach to data analytics, giving corporate decision-makers access to a degree of knowledge that was previously unattainable (Iftikhar, 2023). A broad variety of tasks in this domain can also be covered by machine learning. It may be applied at every stage of data preparation, not only when looking for patterns in certain data sets to highlight a particular need. There are several instances of machine learning having a big influence on automating data preparation chores including identifying human mistake patterns to maintain data “clean” and discovering potential links between datasets. For most firms, using AI and machine learning for big data analytics is a great potential. But the question of where to begin is a whole different one. The domains of data science and machine learning require distinct sets of skills. It's improbable that there will be enough money in the budget to create a completely new department with highly compensated PhD holders to create the machine learning algorithms.

Software developers using artificial intelligence can help with this.

2. Literature Review

When employing routine NGS clinical sequencing for cancer detection and treatment, one of the biggest obstacles is data processing. Large servers along with experienced persons in bioinformatics are required for the handling and evaluating large-scale data. Information on variants categorized as pathogenic, potentially pathogenic, variation of unknown relevance, benign, and likely benign included in the created datasets for diagnostic purposes. It is crucial to identify the clinical importance of each variation and classify them all. Data collected may be beneficial for managing cancer in addition to diagnosing it. (Jiang, 2017) Despite its potential benefits, using artificial intelligence in the healthcare sector still faces several challenges. There's a tremendous data and expense explosion with automatic calculation. AI systems can be expensive since they depend on certain computing requirements for speedy data processing. Plus, these systems need extra quality control procedures. (Davenport, 2019) While even while AI technologies can analyse images and provide accurate data, the resulting data is only useful if it is analysed properly and has therapeutic relevance. Before applying AI-based solutions to routine clinical practice, the target users need to be taught and understand the technology.