A holistic analysis to identify the efficiency of data growth using a standardized method of nonfunctional requirements in graph applications

In the modern era, several opportunities are provided to transfer data through graph models in which digital transformation plays a vital role. Maintaining the data using several devices will cause a processing time delay. Data collection is an important task in all data processing units, as is storing this type of information, as is providing security on this data through a database. To improve this process, the data retrieval is done using a graph data model. The proposed method is used to find the best way to store each record in a graph database rather than in another cloud or distributed database. In this, various techniques used in providing a better solution for data processing are done on graph databases without schema. To provide a good solution without any time delay, the graph analytics algorithm will help in making decisions on better results. In this method, many applications will be taken as case studies for finding the best relationship on the given graph database. In this, the collected data will be converted into graph format, an easy way of finding the duplication. The data model generated on each vertex is converted into low-and high-dimensional data forms. This chapter will go over a number of realtime Neo4j applications that are used to find optimal relationships on various datasets in an efficient manner.