A natural semantics for lazy evaluation
POPL '93 Proceedings of the 20th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Modeling Stream-Based Applications Using the SBF Model of Computation
Journal of VLSI Signal Processing Systems
ICWS '04 Proceedings of the IEEE International Conference on Web Services
A taxonomy of scientific workflow systems for grid computing
ACM SIGMOD Record
Workflows for e-Science: Scientific Workflows for Grids
Workflows for e-Science: Scientific Workflows for Grids
Management of real-time streaming data Grid services: Research Articles
Concurrency and Computation: Practice & Experience - Autonomous Grid Computing
Framework for bringing data streams to the grid
Scientific Programming - AxGrids 2004
Stream processing in data-driven computational science
GRID '06 Proceedings of the 7th IEEE/ACM International Conference on Grid Computing
Service oriented architectures for science gateways on grid systems
ICSOC'05 Proceedings of the Third international conference on Service-Oriented Computing
Hybrid programming abstraction for e-science workflows and event processing
Proceedings of the 5th ACM international conference on Distributed event-based system
Coherence and performance for interactive scientific visualization applications
SC'11 Proceedings of the 10th international conference on Software composition
In-situ I/O processing: a case for location flexibility
Proceedings of the sixth workshop on Parallel Data Storage
| Hi-index | 0.01 |
Geo-sciences involve large-scale parallel models, high resolution real time data from highly asynchronous and heterogeneous sensor networks and instruments, and complex analysis and visualization tools. Scientific workflows are an accepted approach to executing sequences of tasks on scientists’ behalf during scientific investigation. Many geo-science workflows have the need to interact with sensors that produce large continuous streams of data, but programming models provided by scientific workflows are not equipped to handle continuous data streams. This paper proposes a framework that utilizes scientific workflow infrastructure and the benefits of complex event processing to compensate for the impedance mismatch between scientific workflows and continuous data streams. Further we propose and formalize new workflow semantics that would allow the users to not only incorporate stream in scientific workflow, but also make use of the functionalities provided by the complex event processing systems effective within the scientific workflows.