1. Simon Woodman Hugo Hiden Paul Watson Jacek Cala

2. Outline 1. What is e-Science Central? 2. Architecture and Features 3. Workflows and Applications

3. Store Analyse Automate Share Background: North East Regional e- Science Centre  25 Research Projects across many domains:  Bioinformatics, Ageing & Health, Neuroscience, Chemical Engineering, Transport, Geomatics, Video Archives, Artistic Performance Analysis, Computer Performance Analysis,....  Same key needs:

4. Recent Trends Software as a Service Cloud Computing Social Networking e-Science Central e-Science Central Dynamic Resource Allocation Pay-as-you-Go Web based Works anywhere Web based Works anywhere Controlled Sharing Collaboration Communities Controlled Sharing Collaboration Communities

5. e-Science Central  ‘Active Repository’  Integrated Store-Analyse-Automate-Share infrastructure  Bowkers “Standard Scientific Model” predicts data loss  Minimise and ideally remove  Generic  Cheminformatics as pilot

6. Outline 1. What is e-Science Central? 2. Architecture and Features 3. Workflows and Applications

7. Security Storage Workflow Provenance Searching Core Services

8. Connections Discussion Groups Tagging Blogs Collaboration Services

9. Core Services QSAR Chemistry Modeling R Scripts “Scientific” Services

10. Core ServicesCollaboration Services “Scientific” ServicesExpansion…

11. Outline 1. What is e-Science Central? 2. Architecture and Features 3. Workflow and Applications

12. Data Supply Services Calculation and Third Party Services Publication and Storage Services amazon web services ™ Worklfows in e-Science Central

13. Applications  Utilise e-Science Central Service  Present data in different ways  Microsoft Surface  Mobile Apps  Excel  REST API for data access, workflow management  Java and C# client libraries  Run outside e-Science Central  Served in iFrame  Standalone

14. Discovery Bus Application  Discovery Bus  Automated QSAR (Quantitative Structure Actitivity Relationship)  Multi-agent System  Oracle PL/SQL, R, Java  Difficult and error prone to add agents  e-Science Central  agents == workflow  agent creation == application

15. API Discovery Bus Application Generate Models and x-valuidate Filter Features Calculate Descriptors Discovery Bus Agent Installation App Workflow Data Authentication Windows Azure Discovery Bus: Automated QSAR

16. Other Applications  CARMEN Metadata editor  Excel plugin to save/load data in e-Science Central  Mobile application to transcribe notes  OpenQSAR  authenticate and generate model predictions

17. Future  More Blocks and languages  Esmeralda (sequential data modelling)  Discovery Bus Agents  Access external data (provenance problems?)  Real time data processing?  Support for more Workflow Engines  Trident, WWF, Taverna?  Collective Intelligence  Find similar people and documents  Algorithms must include security  Provenance  Query and display with security

18. Demo times  North East Regional e-Science Centre Booth  Today: 12.40  Thursday: 12.40