Abstract
Scientific workflows may be used to enable the collaborative implementation of scientific applications across various domains. Since each domain has its own requirements and solutions for data handling, such workflows often have to deal with a highly heterogeneous data environment. This results in an increased complexity of workflow design. As scientists typically design their scientific workflows on their own, this complexity hinders them to concentrate on their core issue, namely the experiments, analyses, or simulations they conduct. In this paper, we present a novel approach to a pattern-based abstraction support for the complex data management in simulation workflows that goes beyond related work in similar research areas. A pattern hierarchy with different abstraction levels enables a separation of concerns according to the skills of different persons involved in workflow design. The goal is that scientists are no longer obliged to specify low-level details of data management in their workflows. We discuss the advantages of this approach and show to what extent it reduces the complexity of simulation workflow design. Furthermore, we illustrate how to map patterns onto executable workflows. Based on a prototypical implementation of three real-world simulations, we evaluate our approach according to relevant requirements.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Dean, J., Ghemawat, S.: MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM 51(1) (2008)
Deelman, E., et al.: Pegasus: A Framework for Mapping Complex Scientific Workflows Onto Distributed Systems. Scientific Programming 13(3) (2005)
Fehr, J., et al.: Simulation Process of Flexible Multibody Systems with Non-modal Model Order Reduction Techniques. Multibody System Dynamics 25(3) (2011)
Freire, J., et al.: Provenance for Computational Tasks: A Survey. Computing in Science and Engineering 10(3) (2008)
Görlach, K., et al.: Conventional Workflow Technology for Scientific Simulation. In: Guide to e-Science. Springer, London (2011)
Hull, R.: Artifact-Centric Business Process Models: Brief Survey of Research Results and Challenges. In: Proc. of the 7th International Conference on Ontologies, DataBases, and Applications of Semantics (ODBASE), Monterrey, Mexico (2008)
Jordan, D., Evdemon, J.: Web Services Business Process Execution Language Version 2.0, OASIS Standard (2007)
Krause, R., et al.: Scientific Workflows for Bone Remodelling Simulations. Applied Mathematics and Mechanics 13(1) (2013)
Künzle, V., Reichert, M.: PHILharmonicFlows: Towards a Framework for Object-aware Process Management. Journal of Software Maintenance and Evolution: Research and Practice 23(4) (2011)
Liu, X., Thomsen, C., Pedersen, T.B.: ETLMR: A Highly Scalable Dimensional ETL Framework Based on MapReduce. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2011. LNCS, vol. 6862, pp. 96–111. Springer, Heidelberg (2011)
Ludäscher, B., Altintas, I., Gupta, A.: Compiling Abstract Scientific Workflows into Web Service Workflows. In: Proc. of the 15th International Conference on Scientific and Statistical Database Management, Cambridge, MA, USA (2003)
Ogasawara, E.S., et al.: An Algebraic Approach for Data-Centric Scientific Workflows. In: Proc. of the 37th International Conference on Very Large Data Bases (VLDB 2011), Seattle, WA (2011)
Radetzki, U., et al.: Adapters, Shims, and Glue – Service Interoperability for in Silico Experiments. Bioinformatics 22(9) (2006)
Rahm, E., Bernstein, P.A.: A Survey of Approaches to Automatic Schema Matching. International Journal on Very Large Data Bases (VLDB Journal) 10(4) (2001)
Reimann, P., Schwarz, H., Mitschang, B.: Data Patterns to Alleviate the Design of Scientific Workflows Exemplified by a Bone Simulation. In: Proc. of the 26th International Conference on Scientific and Statistical Database Management (2014)
Rommel, J.B., Kästner, J.: The Fragmentation-Recombination Mechanism of the Enzyme Glutamate Mutase Studied by QM/MM Simulations. Journal of the American Chemical Society 26(133) (2011)
Russell, N., ter Hofstede, A.H.M., Edmond, D., van der Aalst, W.M.P.: Workflow Data Patterns: Identification, Representation and Tool Support. In: Delcambre, L.M.L., Kop, C., Mayr, H.C., Mylopoulos, J., Pastor, Ó. (eds.) ER 2005. LNCS, vol. 3716, pp. 353–368. Springer, Heidelberg (2005)
Schumm, D., et al.: Process Fragment Libraries for Easier and Faster Development of Process-based Applications. Systems Integration 2(1) (2011)
Sheth, A.P.: Federated Database Systems for Managing Distributed, Heterogeneous, and Autonomous Databases. In: Proc. of the 17th International Conference on Very Large Data Bases (VLDB 1991), Barcelona, Spain (1991)
Shoshani, A., Rotem, D.: Scientific Data Management: Challenges, Technology, and Deployment. Computational Science Series. Chapman & Hall (2009)
Simitsis, A., et al.: Optimizing Analytic Data Flows for Multiple Execution Engines. In: Proc. of the 2012 ACM SIGMOD International Conference on Management of Data (SIGMOD 2012), Scottsdale, AZ, USA (2012)
Sonntag, M., Karastoyanova, D.: Next Generation Interactive Scientific Experimenting Based on the Workflow Technology. In: Proc. of the 21st IASTED International Conference on Modelling and Simulation, Prague, Czech Republic (2010)
Sonntag, M., et al.: Views on Scientific Workflows. In: Proc. of the 10th International Conference on Perspectives in Business Informatics Research (2011)
Taylor, I., Deelman, E., Gannon, D.: Workflows for e-Science - Scientific Workflows for Grids. Springer, London (2007)
Vrhovnik, M., et al.: An Approach to Optimize Data Processing in Business Processes. In: Proc. of the 33rd International Conference on Very Large Data Bases (VLDB 2007), Vienna, Austria (2007)
Wolstencroft, K., et al.: The myGrid Ontology: Bioinformatics Service Discovery. Int. Journal on Bioinformatics Research and Applications 3(3) (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Reimann, P., Schwarz, H., Mitschang, B. (2014). A Pattern Approach to Conquer the Data Complexity in Simulation Workflow Design. In: Meersman, R., et al. On the Move to Meaningful Internet Systems: OTM 2014 Conferences. OTM 2014. Lecture Notes in Computer Science, vol 8841. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45563-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-662-45563-0_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45562-3
Online ISBN: 978-3-662-45563-0
eBook Packages: Computer ScienceComputer Science (R0)