Why graph analytics?

Abstract

The study of relationships between entities such as clients, goods, operations, and devices is graph analytics, also called network analysis. To obtain knowledge that can be used in marketing or, for example, for analysing social networks, companies use graph models. The term 'graphical analysis' explicitly involves the study and analysis of data, which can be translated in a broad schematic. Graphical analytics are a fast-growing domain in the area of large-scale data mining and visualisation that is used in various multidisciplinary applications like network protection, finance and health care. While several methods have already addressed the study of unstructured collections of multidimensional points in the past, graph analytic technologies are a relatively new trend that presents a number of challenges. Graph analytics are a combination of mathematical, theory of graphs and techniques used to model, store, extract and performance analysis graph-structured information. The techniques recognise modules or interacting subgroups within graphs, search for sub-graphs that are similar to a particular pattern. Due to their polytrophic nature, graphs have acute importance and have widespread big data applications in the real world, e.g., information discovery, social media, search engines, network structures, etc. The main issue is the development of large-scale applications of efficient systems for storage, processing and analysis. Graph analytics are used in numerous applications to model all kinds of relationships and processes. Data scientists and business users can define and analyse complex relationships in healthcare datasets through graph analytics. Gartner Research said in a recent study, "Graph analysis is probably the single most efficient competitive differentiator for organisations that follow data-driven operations and decisions after data capture design." Since the data sources in health organisations, heterogeneously complex and highly dynamic data sources are well-known, the healthcare domain has acquired its importance through the effect of big data. While the position of large graph analytical methods, platforms and tools is realised across different domains, promising research directions are shown by their effect on healthcare organisations to introduce and produce new use cases for possible healthcare applications. The effectiveness of healthcare applications is solely dependent on the underlying nature and implementation of appropriate methods in the sense of broad graph analysis, as demonstrated in ground breaking research attempts. In this chapter, from the perspective of different stakeholders, we discuss the various methodological options available in the patient-centred healthcare system. In order to promote individual patients from diverse viewpoints, we address different architectures, benefits and repositories of each discipline that provide an integrated representation of how separate healthcare operations are carried out in the pipeline.