Hyper Nested Graph: Data Model for Big Data

in the era of information, data which are worthwhile asset of human, organizations and enterprises have become such sophisticated that the conventional approaches and methods are not usable anymore, or not efficient at least. Such complexity which is known as the Big Data problem is the affordable extraction of value from big data sets that we are encountered in many recent applications e.g., e-business, scientific research, monitoring, search engines, social networking, etc.. Big Data complexities are instantiated by three major dimensions, high Volume, high Variety, and high Velocity (a.k.a. 3Vs). The first and most essential step in data management (also for Big Data management) is designing and employing a proper data model, as the footstone of the other data management activities such as R&D of DB languages, DBMSs, tools, methods, algorithms, etc.. In this paper, a proper data model for Big Data is designed and proposed in which the properties required for Big Data problem (i.e., to be integrated, complete, scalable, flexible, compatible, and efficient) are considered. As a data model, data representation is designed and implicit integrity constraints are presented for the proposed HNG (Hyper Nested Graph) data model. Experimental evaluation results show that the proposed data model outperforms other currently used data models such as the document-based, graph document-based, and graph-based data models in terms of response time.