Towards Semantically Enriched Data Spaces

Motivation

Usually, large corporations struggle to provide an enterprise-wide overview of available data and data demands.
The main challenges are complex processes which are intensified by the exponential growth of data. As a result, data is often split and locked in separate data silos maintained by the diverse departments. 

Leveraging these data silos for analytical and data science tasks is a hard problem.
Studies show that data scientists spend up to 80% of their time for discovering, accessing and transforming data instead of analyzing it. 
Current, heavily engineering-based solutions do not allow queryable ad-hoc access and do not scale beyond a certain number of data sources and models. 

Approach

We are currently researching new systems to enable next-generation data management using the following approaches:

Linked data management using graph databases and ontologies that enable:

• enrichment of decentral data with meta / semantic / provenance information
• efficient incremental management of this information as annotated labeled graphs
• adoption of emergent meta data standards and ontologies 

Query federation using industry standards (like Apache Drill, GraphQL) that enable:

• Virtual data sets
• Avoidance of data duplication
• Decoupling of source and target schema

Publications