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PALMS-CI: A Policy-driven Cyberinfrastructure
For the Exposure Biology Community
Barry Demchak ([email protected]), Jacqueline Kerr, Gregory Norman,
Ernesto Ramirez, Fred Raab, Dane Lotspeich, Ingolf Krüger, and Kevin Patrick
California Institute for Telecommunications and Information Technology, San Diego Division
This material is based upon work supported by the National Institutes of Health under Grant
No 1U01CA130771-01and the National Science Foundation under Grant No CCF-0702791
The Problem
The Solution
Sensor Data
Stakeholder Policies
Build Cyberinfrastructures
serve stakeholder communities
Model-based Policy Elicitation
• Support research workflows
• Workflows are templates to be
customized by stakeholders
• Traditional development cycles are
long & mis-target stakeholder
• Healthy CIs solve all requirements
simultaneously & continuously
• Stakeholders specify policies
directly1,2 on workflow models (UML
Activity Diagrams4) using visual
Domain Specific Language (DSL )
• Policies specify alternate workflows
& data flow transformations
• Cross-pollination of other
requirement domains
PALMS Cyberinfrastructure
Service Oriented Architecture
logical/deployment models
Solve Stakeholder Concerns
• Leverage standard patterns3:
strategy, messaging, routing, &
composite pattern (i.e., systems-ofsystems)
• Leverage role-based interaction,
choreography, & interceptor
Support emerging workflows
Multifactor access control
Confidentiality & privacy (HIPAA/IRB)
High availability & reliability
Scalability (bandwidth/storage/users)
Provenance & curation
PALMS Browser
Shared Data
Requirement elicitation
Policy Concerns
• Functional & quality requirements
• Crosscutting concerns
• Precise & accurate formulation
Low latency to implementation
Faithful to original requirement
Conflict & error detection
Scalable to large data flows
• Leverage relationships with other
visual notations (e.g., Business
Process Modeling Notation)
• Alignment with additional policy
patterns & domains
Physical Activity Location Measurement System to understand where activity-related energy expenditure occurs in
humans as a function of time and space. Harvests data fromf wearable devices on small and large scales, provides
framework for research and analysis, and has ultimate goal of discovering methods for engineering better health.
Expand repertoire of policy patterns
Define policy composition rules
Code generation and deployment
Conflict/completeness checking
Scalable and distributed execution
Domain Specific Language
End-to-end Traceability
Future Work
An Internet-based research computing environment that supports data acquisition, data storage, data management, data
integration, data mining, data visualization, and other computing
and information processing services. Different
stakeholders produce, consume, manage, and govern a CI, and their requirements must be simultaneously met or else
the integrity of the CI degrades.
1. J. Juerjens. Security Systems Development with UML. Springer-Verlag Berlin Heidelberg, 2003.
2. T. Lodderstedt, D. Basin, and J. Doser. SecureUML: A UML-Based Modeling Language for Model-Driven Security. Proceedings of the
5th International Conference on The Unified Modeling Language. pp426-441. Springer Verlag, 2002.
3. M. Arrott, B. Demchak, V. Ermagan, C. Farcas, E. Farcas, I. H. Krüger, and M. Menarini. Rich Services: The Integration Piece of the
SOA Puzzle. In Proceedings of the IEEE International Conference on Web Services (ICWS), Salt Lake City, Utah, USA. IEEE, Jul.
2007, pp. 176-183.
4. A. Bhattacharjee and R. Shyamasundar. Activity Diagrams: A Formal Framework to Model Business Processes and Code Generation.
Journal of Object Technology. Vol 8, No 1, Jan 2009.
5. J. Viega. Building security requirements with CLASP. Proceedings of the 2005 Workshop on Software Engineering for Secure Systems
– Building Trustworthy Applications. St Louis, MO, 2005.