Predictive Performance Comparison Analysis of Relational & NoSQL Graph Databases

Abstract

Abstract-From last three decades, the relational databases are being used in many organizations of various natures such as Education, Health, Business and in many other applications. Traditional databases show tremendous performance and are designed to handle structured data with ACID (Atomicity, Consistency, Isolation, Durability) property to manage data integrity. In the current era, organizations are storing more data i.e. videos, images, blogs, etc. besides structured data for decision making. Similarly, social media and scientific applications are generating large amount of semi-structured data of varied nature. Relational databases cannot process properly and manage such large amount of data efficiently. To overcome this problem, another paradigm NoSQL databases is introduced to manage and process massive amount of unstructured data efficiently. NoSQL databases are divided into four categories and each category is used according to the nature and need of the specific problem. In this paper we will compare Oracle relational database and NoSQL graph database using optimized queries and physical database tuning techniques. The comparison is two folded: in the first iteration we compare various kinds of queries such as simpler query, database tuning of Oracle relational database such as sub databases and perform these queries in our desired environments. Secondly, for this comparison we will perform predictive analysis for the results obtained from our experiments.

Software Systems Approach to Multi-Scale GIS-BIM Utility Infrastructure Network Integration and Resource Flow Simulation

“…There is no need to predefine the database schema since new nodes, relationships, and properties can be added on-the-fly; this flexibility is powerful for a utility resource network model that must integrate data from diverse and dynamic data sources. More generally, the requirement for integration methods to enable deeper mathematical modelling [10] would be facilitated by the speed and efficiency of using graph databases to execute queries on connected data [24,25], and the relative simplicity of constructing query statements.…”

“…In the system, extracted elements and their topological connectivity are pushed to a Neo4j graph database. A graph database was selected as they have been found to be efficient for storing and querying topologically connected data [24,25]. Furthermore, it has been proposed that graph theory and graph models are suitable for understanding urban topologies [26] and integrating models of urban data [27], and that graph databases can be used for the detection of spatial-semantic changes in CityGML documents [28].…”

A Comparison between a Relational and a Graph Database in the Context of a Recommendation System

“…Another article [10] presents the results of the comparison between Oracle relational database and NoSQL graph database. The comparison was made in two directions: the first direction aimed at executing queries in the types of databases and the second direction involved performing a predictive analysis on the experimental results.…”

Pemodelan Basis data Graph dengan Neo4j (Studi Kasus : Basis Data Sistem Informasi Penjualan pada UMKM)

“…[9]. Penelitian mengenai analisis penggunaan graph dilakukan [10], [11], [12], [13] Penelitian yang berhubungan dengan basis data graph terutama Neo4j diantaranya dilakukan Widyayanti [14], basis data Graph digunakan untuk penyimpan basis data hotel terutama pengelolaan kamar. Zhang [15], membuat studi kasus ini dengan menggunakan basis data grafik Neo4j; salah satu model data NoSQL, untuk membangun mini prototipe blog untuk melakukan rekomendasi sosial yang efisien.…”