International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025
p-ISSN: 2395-0072
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Multi-Model Databases: Unifying Relational, Document, and Graph Data Models KM. Anjali Kushwaha1, Deepshikha2 1Master of Technology, Computer Science and Engineering, Lucknow Institute of Technology, Lucknow, India 2Assistant Professor, Department of Computer Science and Engineering, Lucknow Institute of Technology,
Lucknow, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - In this context, proliferation of heterogeneous
have to work with semi-structured documents, interrelated objects and rapidly changing streams of data as well. This exertion in the data characteristics has questioned the conventional dominance of the singlemodel databases and has created attention on the multimodel databases, which are versatile in handling several types of data paradigm in a single system. This paper explores architectural and performance issues of combining a relational, document and graph model into one coherent multi-model database system and paves a way forward suggesting a solution which fills the gap in what the existing ones can offer.
information in the contemporary realizations has indicated the vulnerability of the single model database systems. The conventional relational databases, though strong with structured and transactional integrity, have a problem with semi-structured formats as well as with complex relationships. On the other hand, the document and graph databases are flexible and support relationship traversal but provide no consistency and unified query. The proposed research idea is that of developing a single unifying framework natively supporting the three commonly used data models (relational, document and graph) on a common storage and query processing engine. A combination of schema mapping techniques, dynamic indexing, and machine learning based query optimizer are incorporated into the framework in order to increase the effectiveness of hybrid workloads and their consistency.
1.1 Motivation It becomes more and more evident that a combination of data models must be used in modern applications that run in the sphere of social media, fraud detection, healthcare, and the Internet of Things (IoT). As an example, a social media platform will have relational data that enable it to track the activity of the user, document that can be used to house the user-generated content, and more graph models that can model the relationship between users. In the same way, a system of detecting a fraud needs structured transaction data (relational), behavioral logs (documents) and connection networks (graphs) to detect frauds of sophisticated patterns. Keeping these types of data in different systems with silos creates overheads and complications. With increasing size and functionality of applications, data modeling approach is not only desired but necessary to be unified. The incentive of this study is to sponsor such dynamic application requirements with the help of one single competent adaptable framework, multi model database.
An ability to travel, filter, and join across the various data models is provided by a new query interface that does all these tasks without the need to switch context or manually convert information. The experimental data based on the industry standards (TPC-H, LDBC SNB) and real non-trivial case-study of fraud detection reveals the latency decrease by 35-40% and up to 45% throughput improvement relative to the set of most popular multi-model databases: ArangoDB and OrientDB. Its framework has flexible schema enforcement, hybrid sharding, and fault-tolerant distributed execution and can be applied to any application in the finance, IoT, health care, and e-commerce domains. This paper shows that a holistic multi-model database can work and be able to reduce the complexity of data infrastructure and at the same time increased their performance and scaleability of data infrastructure used in the real world.
1.2 Problem Statement
Key Words: Multi-model databases, unified query processing, relational data, document stores, graph traversal, hybrid indexing, schema mapping, distributed databases.
Even though there is growing need to process data on various and diverse data types, majority of the organizations continue to use a mix of specialised databases, formulated to process a certain type of data. Such a non consolidated practice leads to a number of operation-wise inefficiencies. Redundancy of the data is the order of the day because the same data needs to be replicated in the various systems to satisfy the various analytical and transactional demands. The need to
1. INTRODUCTION By now, the data has changed to a more diverse, complex, and voluminous in the world that is using data-driven decision-making. The applications are not anymore restricted to process structured tabular data. Rather, they
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