Accelerating graph analytics

Abstract

Handling graph databases properly leads to obtaining a precise result within a fixed time. It is essential to take a fast-track in graph analytics to address the challenges faced in exploring the data, as it is an incipient form of data analysis. The analytic methodologies involved in discovering the underlying implications of data to deliver an optimized result should get drive-by emerging techniques. A graph database possesses many complex patterns which are to be quickly identified for specific outcomes. Graph analytics assist in building patterns based on the connectivity between the nodes to provide proper context. Graph analysis is transforming into a significant factor for today's data analysis, so there is a need to accelerate its medium to the next level by implanting its features with recent technologies. To boost the performance of graph analytics, Artificial Intelligence (AI) is embraced with its enhanced features and techniques. Graph-based machine learning and analytics act as a boon for data scientists to increase their productivity and representation of data which is widely expected from enterprise users. Implementing AI produces machine-generated quick and precise values and results in a complete dataset-adopting machine learning techniques helps in feeding training data to the algorithm, building relationships between disparate data points, and enabling the connection between nodes and structure graph databases. This chapter leads the path of advanced graph analytics, which will lead data analyzers to take their next move in a data-powered business.