GDBApex: A graph‐based system to enable efficient transformation of enterprise infrastructures. (1st July 2020)

• Record Type: Journal Article
• Authors: Jarrah, Moath; Al‐khatieb, Bahaa; Mahasneh, Naseem; Al‐khateeb, Baghdad; Jararweh, Yaser
• Other Names: Baker Thar guestEditor.; Al‐Jumeily Dhiya guestEditor.; Maamar Zakaria guestEditor.; Tari Zahir guestEditor.
• Is Part Of: Software, practice & experience. Volume 51:Number 3(2021)
• Page Start: 517
• Page End: 531
• Publication Date: 2020-07-01
• Subjects: graph‐based modeling -- Hazelcast -- Hibernate -- load balancing -- OrientDB -- scalability -- semantic relations -- software design and development; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2871
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: Graph‐based database engines have been developed by different researchers and companies. Many optimization methods have been integrated within these engines to enable fast and efficient data processing. However, many small‐ and medium‐size organizations have not changed their database infrastructures and still rely on a relational management modeling approach. This limits their service performance, especially in today's large‐scale data processing requirements. Transformation to the use of graph‐based modeling and design is not a straightforward process. In order to make a successful transformation, correct process semantics as well as the design of vertices, edges, labels, and process relations are required. The goal of this article is to help small‐ and medium‐size organizations make this transformation successful in order to satisfy customers' expectations and meet the requirements of data‐intensive applications. The proposed graph‐based modeling approach uses a graph structure for semantic queries and applies software engineering design principles. Moreover, it provides a case study with many data transactions. The system outperformed relational database management systems by an order of magnitude. Scalability of the system is examined and compared with the regular relational‐based modeling. In addition, a load balancing solution is used to achieve high scalability.