Use Cases

 

System Migration Integrity

Establish controls to guarantee data and model quality throughout migration. System migration projects are routinely delayed, over-budget, and fragile upon completion. Conexus will help them to run to plan.

Objectives

Migrate pension data to a third party. Discover any conflicting business rules between source and target,

Results

Data migration expressed declaratively as a CQL program. Business rule conflicts discovered and repaired.


Regulatory verification

Make sure reporting data is accurate and complete to reduce audit risk and avoid fines. 


 

Trustworthy data integration

Objectives

Integrate experimental databases of two organizations. Identify and mediate between equivalent entities (eg: CO2, Carbon Dioxide).

Results

Data integration expressed declaratively as a CQL program. Identification of matches expressed as a CQL query. Mediation implemented as a CQL quotient.

 

Schema Evolution

Objectives

  • Rigorously specify changes to database schemas.

  • Automatically evolve queries to updated schemas.

Results

  • Schema updates formalized as CQL mappings.

  • Query evolution performed using CQL composition.


Supply chain efficiency

Objectives

Enrich manufacturing data with facts about chemistry given by an RDF ontology. Ensure queries return correct results.

Results

Data integration expressed declaratively as a CQL program. Schema integration expressed declaratively.


Knowledge graphs

Presenting issue

CQL’s catagorical approach provides more powerful and simpler model for representing data in large enterprises.

Results

CQL meta-model satisfying client’s knowledge graph properties.
Foundational technology for next generation data models. 

 

ETL Rescue

Half of data migration projects fail, for reasons both technology-related (inability to connect to data sources, to write queries, to identify links, etc) and people-related (territoriality, disagreement on goals, inability to agree on warehouse schemas, etc). Conexus's data integration experts, can rescue ETL projects that are failing by replacing inadequate ETL technology with CQL, a technology which requires people to make fewer design designs and uses AI to prevent common errors.