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consumption, production and catalysis), and graph models provide a valuable framework in this situation. metabolites, proteins, complexes and metabolic reaction names) and the relations among them (e.g. For instance, the study of human metabolism requires identifying hundreds of concepts (e.g. The biomedical domain is a complex area that is inevitably studied in many different sub-domains that are inherently related and connected. Consequently, the trend in graph databases has permeated into many disparate domains, and we can find applications in Energy Management Systems (EMS) ( 8), Power Grid Modeling ( 9) and even less technologically driven fields like Digital Humanities ( 10). Graph models are present in multiple formal representations and become very powerful when the problem model exhibits varied relations among the entities or concepts. Furthermore, the schema-less or schema-optional approach that most GDBMSs follow grants a high degree of flexibility, allowing applications to adapt and evolve quickly and introduce abstraction, specialization of entities and relations among them more easily. GDBMSs, in contrast to regular DBMSs, allow working directly with a graph model, avoiding sophisticated engineering to represent relationships efficiently, and provide straightforward ways to store, access and operate graph data, especially for traversing paths and matching subgraphs. These modeling and performance limitations have increased the interest in Graph Database Management Systems (GDBMSs).

However, when most relationships are many-to-many, prevalent in densely connected data, querying the database requires multiple expensive JOIN operations, impacting the performance ( 6).Īlthough graphs can be modeled with tables representing vertices and edges, complex queries or graph algorithms (like path traversals) are challenging to optimize without implementing complementary structures, such as adjacency lists ( 7). Hence, given its maturity and technological development, RDBMSs are widely used for data storage, with countless examples experienced in everyday life, like user data, inventory tracking, blog posts and many more. The relational paradigm is very appropriate for well-defined data structures that are unlikely to change and translate naturally to tables, and the relations among its entities are not numerous and not as relevant as the entities’ attributes.

To exploit these references and include different tables in a database query, the Structured Query Language (SQL) ( 5) provides the JOIN clause. The standard mechanism to create relationships between entities is by defining unique IDs (primary keys) that can be copied into referencing tables (foreign keys). However, strong graph data is not straightforward in conventional Database Management Systems (DBMSs), and the physical implementation of a given data model and how the relations are treated ultimately depend on the database type.įor example, the basis of Relational Database Management Systems (RDBMSs) are tables (relations) ( 2–4), where each row represents a single data element of an entity and a single column usually defines a particular data attribute. The graph abstraction directly translates concepts and instances into nodes and their relationships into edges, making it intuitive for data modeling.

Formally, a graph G( V, E) is composed of an ordered pair of two disjoint sets: vertices V (also referred to as nodes) and edges (or links) E ( 1). In this situation, where the relationships among data grow both in quantity and in significance, graph models become an appealing solution, as graphs are mathematical entities in which objects are connected. Nowadays, the generation, consumption and, more importantly, analysis of highly interconnected data have become ubiquitous.