Research Theme 2: Data Management for Analytics

The five subthemes managed in the Hosting phase of CeADAR by the team at UCC with the leadership of Professor Barry O'Sullivan under Research Theme 2 - Data Management for Analytics are:

Research Theme: Reduce data management effort for analytics

This theme seeks to address the resource intensive activity around preparing data for analytics (as opposed to operational) purposes.  The main goal of this project is to develop approaches, methods and tools to improve, simplify and reduce the effort involved in the management of data for analytics purposes.

One tool developed under this subtheme is:

AI for Business Process Modelling

A state-of-the-art report about this topic is available here:  SOTA on Query Correctness

 

Research Theme: Data validation

This topic seeks to minimise the possible downstream effects on error prone results from bad data. This theme seeks to develop advanced analytics techniques and demonstrators to manage the validity of the data subsequently being used for data analytics purposes.

One tool developed under this subtheme is:

Querying with Confidence Tool

A state-of-the-art report about this topic is available here:  SOTA on Workflow Management

 

Research Theme: Relevance of Events to Relationships

Seeking to improve the understanding of the relevance of events to relationships (between people, things or other data), this theme aims to develop analytical approaches, methods, models and tools to understand and improve to understand the relevance of an event on relationships.

 

Research Theme: Data Curation (determing useful data)

With a view to reducing the costly storage of data which is not useful, the goal of this theme is to use advanced analytics techniques to determine which data may be considered 'useful' to improve data archiving and data storage approaches.

 

Research Theme: Adaptive ETL (Extract, Transform, Load)

This theme focuses on integrating external data sources where there is a clear sender and receiver with applicability between internal systems.  The goal is to create tools, demonstrators and advanced analytics techniques to prevent STP (Straight Through Processing) breaks by automatically compensating for changes in data received-Adaptive ETL.