Graph Database: A Complete GDBMS Survey

Journal of Process Management. New Technologies

Since 1970, relational database models have been in use for storing, manipulating and retrieving data. The importance of relational databases is going to decrease due to the exponential growth of data as it is difficult to work with large number of joining tables. For such kinds of problems, one of the best solutions is to use graph database for storing data. The graph database can be used to store highly connected data. In this article, we are going to put forward a comparison between relational database and graph database with reference to an experiment performed.

Graph Data Management, 2018

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graphoriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them.

2012

Abstract: Relational model has been dominating the computer industry since the 1980s mainly for storing and retrieving data. Lately, however, relational database is losing its importance due to its dependence on a rigid schema which makes it difficult to add new relationships between the objects. Another important reason of its failure is that generally, the relational model works best when there are a relatively small and static number of relationships between objects.

Graph database models can be defined as those in which data structures for the schema and instances are modeled as graphs or generalizations of them, and data manipulation is expressed by graph-oriented operations and type constructors. These models took off in the eighties and early nineties alongside objectoriented models. Their influence gradually died out with the emergence of other database models, in particular geographical, spatial, semistructured, and XML. Recently, the need to manage information with graph-like nature has reestablished the relevance of this area. The main objective of this survey is to present the work that has been conducted in the area of graph database modeling, concentrating on data structures, query languages, and integrity constraints.

ACM Computing Surveys (CSUR), 2008

Abstract:-Relational model has been dominating the computer industry since the 1980s mainly for storing and retrieving data. Lately, however, relational database is losing its importance due to its dependence on a rigid schema which makes it difficult to add new relationships between the objects. Another important reason of its failure is that as the available data is growing manifolds, it is becoming difficult to work with relational model as joining large number of tables is not working efficiently.

2012 IEEE 28th International Conference on Data Engineering Workshops, 2012

The limitations of traditional databases, in particular the relational model, to cover the requirements of current applications has lead the development of new database technologies. Among them, the Graph Databases are calling the attention of the database community because in trendy projects where a database is needed, the extraction of worthy information relies on processing the graph-like structure of the data. In this paper we present a systematic comparison of current graph database models. Our review includes general features (for data storing and querying), data modeling features (i.e., data structures, query languages, and integrity constraints), and the support for essential graph queries.

2021

Planning, developing, and updating software cannot be separated from the role of the database. From various types of databases, graph databases are considered to have various advantages over their predecessor, relational databases. Graph databases then become the latest trend in the software and data science industry, apart from the development of graph theory itself. The proliferation of research on GDB in the last decade raises questions about what topics are associated with GDB, what industries use GDB in its data processing, what the GDB models are, and what types of GDB have been used most frequently by users in the last few years. This article aims to answer these questions through a Literature Review, which is carried out by determining objectives, determining the limits of review coverage, determining inclusion and exclusion criteria for data retrieval, data extraction, and quality assessment. Based on a review of 60 studies, several research topics related to GDB are Semant...