The Power of the Graph Database

When connections between entities in a database are at least as important as entities themselves, the graph database has no analogue in efficiency.

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Main advantages of the Graph Database

Top Performance

Data volumes are continuously increasing, and the number of connections between entities within data is growing exponentially. Relational databases become more and more obsolete since managing these massive volumes of connected data significantly impairs their performance. In contrast, the performance of graph databases remains excellent regardless of data volume and growth since they never need to load unrelated data for a given query. Real-time big data analytical queries are easily and quickly executed.

Updating data does not forbid query processing

Processing queries while performing real-time updates is a huge advantage when you have an ever-growing data pool and a massive active user base.

The BioSeek Graph Database

The BioSeek graph database processes unstructured textual data aggregated from the internet, such as scientific papers, clinical trials, and news, from a constantly growing list of sources like PubMed, NCBI, UniProt, etc. Patents are soon to be added. A semantic analysis identifies the bio terms within the texts so you don't need to navigate back and forth to search information of diverse nature.

Indexed so far:

It may sound as a detail, since navigating between different sites has been the status quo for years and years, but eliminating cross-site navigation greatly empowers smart research by saving time.

How the magic happens

Once we collect the data the relations between all these entities become functional which means entities are delivered to you as search results based on these relations. Exquisite recommendation systems are built based on the many-to-many relationships.

The graph database allows us to conduct complex data analyses which would be impossible in the environment of relational databases.
* More on the algorithm behind clusterization here.

A cluster analysis around the gene nfkb1

Graph databases for science

Using graph databases is not new in the field of scientific research and healthcare-related digital tools. A good example of this technology put into use in the domain of scientific research is the solution of eTRIKS (more info here). However, to use the eTRIKS platform, the user needs some knowledge in programming as well. Our database contains more than 2 million relations between 60 million entities, and the user does not need any software engineering skills to use it.

Bringing it all together:

Big Data gets bigger and we need graph databases to manage it efficiently and generate knowledge from data. Dependencies and relations between entities are important in every sphere of science, business and industry. Being aware of those relations and having the ability to analyse them significantly increases your efficiency.