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Exploiting the Life Science Data Explosion to Speed New Drug Discovery

rest that will ultimately lead to understanding the “forest”.


Comparative Examples
Example: discoveryHub Approach to Relational Database Population
There are applications where a local repository is required and a relational technology is appropriate for localized data. The implementation of the discoveryHub as a middleware platform alongside the relational technology (for localized data) can be a powerful combination. Many companies simply do not have the short-term resources to create a full entity relational schema to model the complete public and privately available data from all the various sources, yet they need to reduce some subset of both public and private sources into ‘privatized’ datasets accessible via a relational infrastructure. To address this, we have created an alternative to the design-focused long-term data model – the discoveryHub approach to life science data population.

The Relational vs. Non-Relational Challenge
In cases where local repositories are required and ultimately a simplified relational view is adequate, good value can be offered by the established relational database vendors. But this creates a challenge: how do we load and store a highly complex data structure into a relational model? There are many answers to this question – and many factors that influence the answer. To illustrate, let us consider two simple alternatives that will help to understand some of the issues.

Relational Approach
The typical relational approach to this problem is shown in diagram #1. This approach applies the same set of rules to this environment as to any other transactional system. We define a set of entities such as Feature, Lineage, etc. create a relationship model and develop a number of ‘synthetic’ keys between each relevant level.
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