Abstract

Comparing graph databases with traditional,e.g., relational databases, some important database features are often missing there. Particularly, a graph database schema including integrity constraints is mostly not explicitly defined, also a conceptual modelling is not used. It is hard to check a consistency of the graph database, because almost no integrity constraints are defined or only their very simple representatives can be specified. In the paper, we discuss these issues and present current possibilities and challenges in graph database modelling. We focus also on integrity constraints modelling and propose functional dependencies between entity types, which reminds modelling functional dependencies known from relational databases. We show a number of examples of often cited GDBMSs and their approach to database schemas and ICs specification. Also a conceptual level of a graph database design is considered. We propose a sufficient conceptual model based on a binary variant of the ER model and show its relationship to a graph database model, i.e. a mapping conceptual schemas to database schemas. An alternative based on the conceptual functions called attributes is presented. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Highlights

  • There are several application domains in which the data has a natural representation as a graph

  • Concerning graph database schemas, three most popular graph database management systems (GDBMS) from the DB-Engines Ranking of Graph DBMS [23], Titan and OrientDB use the notion of schema, not Neo4j [7]

  • Especially in cases where no graph database schema is at disposal, i.e., in schemaless GDBMSs

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Summary

Introduction

There are several application domains in which the data has a natural representation as a graph. A graph database schema reecting the above features consists of three components: a set of data structure types, a set of operators or inference rules, and a set of ICs (often called only constraints). Concerning graph database schemas, three most popular GDBMS from the DB-Engines Ranking of Graph DBMS [23] , Titan and OrientDB use the notion of schema, not Neo4j [7] They enable only some simple ICs (see, Sec. 3.2.). A typed lambda calculus can be used as a data manipulation language This approach reects the graph structure of a GDB and, on the other hand, provides powerful possibilities for dealing with properties in querying the GDB content.

Graph Denitions
Graph database modelling
Formal approaches to integrity constraints
A binary E-R model as a variant for graph conceptual modelling
Mapping conceptual schemas to database schemas
Conclusion
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