Skip to main content
  • Get access
    Check if you have access via personal or institutional login
  • Cited by 1
  • Cited by
    This (lowercase (translateProductType product.productType)) has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Granell, Carlos 2014. Geographical Information Systems.

  • Print publication year: 2011
  • Online publication date: October 2011

15 - Scientific workflows for the geosciences: An emerging approach to building integrated data analysis systems

from Part V - Web services and scientific workflows

Scientific method and the influence of technology

Due to the increasing number and sophistication of data acquisition technologies, the amount of raw data acquired has vastly increased over the last couple of decades (Berman, 2008). This explosion of scientific data, growth in scientific knowledge, and the increase in the number of studies that require access to knowledge from multiple scientific disciplines amplify the complexity of scientific problems. In order to answer these “grand challenge” scientific questions, scientists use computational methods that are evolving almost daily. The basic scientific method, however, remains the same for the individual scientist. Scientists still start with a set of questions, then observe phenomena, gather data, develop hypotheses, perform tests, negate or modify hypotheses, reiterate the process with various data, and finally come up with a new set of questions, theories, or laws ( A recent change in this scientific method is that it is continuously being transformed with the advances in computer science and technology. The simplest examples of this transformation are use of personal computers to record scientific activity and the way scientists publish and search for publications online. More advanced technologies within the scientific process include sensor-based observatories to collect data in real time, supercomputers to run simulations, domain-specific data archives that give access to heterogeneous data, and online interfaces to distribute computational experiments and monitor resources.

Recommend this book

Email your librarian or administrator to recommend adding this book to your organisation's collection.

  • Online ISBN: 9780511976308
  • Book DOI:
Please enter your name
Please enter a valid email address
Who would you like to send this to *
Abramson, D., Bethwaite, B., Enticott, C., Garic, S., and Peachey, T. (2009). Parameter space exploration using scientific workflows. ICCS 2009, Baton Rouge, LA, USA, May 2009.
Abramson, D., Enticott, C., and Altintas, I. (2008). Nimrod/K: Towards massively parallel dynamic Grid workflows. In Proceedings of Supercomputing 2008 (SC 2008): p. 24.
Altintas, I., Barney, O., and Jaeger-Frank, E. (2006). Provenance collection support in the Kepler scientific workflow system. In Proceedings of International Provenance and Annotation Workshop (IPAW2006), pp. 118–132.
Altintas, I., Jaeger, E., Lin, K., Ludaescher, B., and Memon, A. (2004). A web service composition and deployment framework for scientific workflows. In 2nd International Conference on Web Services (ICWS), San Diego, California, July 2004.
Altintas, I., Lin, A. W., Chen, al. (2010). CAMERA 2.0: A Data-Centric Metagenomics Community Infrastructure Driven by Scientific Workflows. IEEE 2010 Fourth International Workshop on Scientific Workflows, Miami, FL, USA.
Barker, A. and Hemert, J. (2008). Information Scientific Workflow: A Survey and Research Directions. LNCS 4967. Berlin: Springer, pp. 746–753.
Barseghian, D., Altintas, I., and Jones, M. B. (2008). Accessing and using sensor data within the Kepler scientific workflow system. In Proceedings of Environmental Information Management Conference, ed. Gries, C. and Jones, M. B.. pp. 26–32.
Barseghian, D., Altintas, I., Jones, M. al. (2010). Workflows and extensions to the Kepler scientific workflow system to support environmental sensor data access and analysis. Ecological Informatics, 5: 42–50.
Berman, F. (2008). Got data? A guide to data preservation in the information age. Communications of the ACM, 51: 12, 50–56.
Carter, W. E., Shrestha, R., and Slatton, K. C. (2007). Geodetic laser scanning. Physics Today, 60(12): 41–47.
Carter, W. E., Shrestha, R. L., Tuell, G., Bloomquist, D., and Sartori, M. (2001). Airborne laser swath mapping shines new light on Earth's topography. Eos Transactions AGU, 82: 549–550, 555.
Crawl, D. and Altintas, I. (2008). A provenance-based fault tolerance mechanism for scientific workflows. In Proceedings of International Provenance and Annotation Workshop (IPAW 2008), Salt Lake City, UT, USA, pp. 152–159.
Cuadrado, D. L. (2008). Automated distribution simulation in Ptolemy II. Ph.D. thesis, Aalborg University.
Deelman, E., Blythe, J., Gil, al. (2004). Pegasus: Mapping scientific workflows onto the Grid. In European Across Grids Conference, pp. 11–20.
Freire, C. T., Silva, S. P., Callahan, al. (2006). Managing Rapidly-Evolving Scientific Workflows. In International Provenance and Annotation Workshop (IPAW), LNCS 4145. Berlin: Springer, pp. 10–18.
Fricke, T. T., Ludaescher, B., Altintas, al. (2004). Integration of Kepler with ROADNet: Visual dataflow design with real-time geophysical data. AGU Fall Meeting, San Francisco, CA, USA, December, 13–17, 2004.
Goderis, A., Brooks, C., Altintas, I., Lee, E. A., and Goble, C. A. (2007). Composing different models of computation in Kepler and Ptolemy II. International Conference on Computational Science, 3: 182–190.
Jaeger-Frank, E., Crosby, C. J., Memon, al. (2006). A Three Tier Architecture for LiDAR Interpolation and Analysis, LNCS 3993. Berlin: Springer, pp. 920–927.
Kim, H., Arrowsmith, J. R., Crosby, C. al. (2006). An efficient implementation of a local binning algorithm for digital elevation model generation of LiDAR/ALSM dataset. Eos Transactions AGU, 87(52), Fall Meet. Suppl., Abstract G53C-0921.
Leinfelder, B., Altintas, I., Barseghian, al. (2009). An integrated approach to managing workflow runs and generating reports in Kepler. In Eighth Biennial Ptolemy Miniconference, April 2009.
Ludäscher, B., Altintas, I., Berkley, al. (2006). Scientific workflow management and the Kepler system. Concurrency and Computation: Practice & Experience, 18(10): 1039–1065.
Ludäscher, B., Podhorszki, N., Altintas, I., Bowers, S., and McPhillips, T. M. (2008). From computation models to models of provenance: The RWS approach. Concurrency and Computation: Practice & Experience, 20(5): 507–518.
Mouallem, P., Crawl, D., Altintas, I., Vouk, M., and Yildiz, U. (2010). A Fault-Tolerance Architecture for Kepler-Based Distributed Scientific Workflows. In SSDBM 2010, ed. Gertz, M. and Ludascher, B.. LNCS 6187. Berlin: Springer, pp. 452–460.
Nandigam, V., Baru, C., and Crosby, C. J. (2010). Database design for high-resolution LIDAR topography data. In SSDBM 2010, ed. Gertz, M. and Ludascher, B.. LNCS 6187. Berlin: Springer, pp. 151–159.
,OPeNDAP: Open-source Project for a Network Data Access Protocol,, 2010.
Pennington, D. D., Higgins, D., Peterson, A. al. (2007). Ecological niche modeling using the Kepler workflow system. In Workflows for e-Science Scientific Workflows for Grids, ed. Taylor, I. J, Deelman, E., Gannon, D. B. and Shields, M.. New York: Springer, pp. 91–108.
Podhorszki, N., Klasky, S., Liu, al. (2009). Plasma fusion code coupling using scalable I/O services and scientific workflows. In Proceedings of the 4th Workshop on Workflows in Support of Large-Scale Science (WORKS09) at Supercomputing 2009 (SC2009) Conference. Portland, OR: ACM.
Podhorszki, N., Ludaescher, B., and Klasky, S. (2007). Workflow automation for processing plasma fusion simulation data. In 2nd Workshop on Workflows in Support of Large-Scale Science (WORKS07) at the 16th International Symposium on High-Performance Distributed Computing (HPDC-16 2007), Monterey, CA, USA, 2007.
Prentice, C. S., Crosby, C. J., Whitehill, C. Set al. (2009). Illuminating northern California's active faults. Eos Transactions AGU, 90(7): 55–56.
Sallenger, A. H., Krabill, W., Swift, al. (2003). Evaluation of airborne scanning lidar for coastal change applications. Journal of Coastal Research, 19(1): 125–133.
Smanchat, S., Indrawan, M., Ling, S., Enticott, C., and Abramson, D. (2009). Scheduling multiple parameter sweep workflow instances on the Grid. IEEE e-Science 2009.
Sudholt, W., Altintas, I., and Baldridge, K. (2006). Scientific workflow infrastructure for computational chemistry on the Grid. International Conference on Computational Science, 3: 69–76.
Taylor, I. (2006). Triana generations. In 2nd International Conference on e-Science and Grid Technologies (e-Science). New York: IEEE Computer Society, p. 143.
Turi, D., Missier, P., Goble, C., Roure, D. D., and Oinn, T. (2007). Taverna workflows: Syntax and semantics. In eScience, Bangalore, India, pp. 441–448.
Vouk, M. A., Altintas, I., Barreto, al. (2007). Automation of network-based scientific workflows. Proceedings of the IFIP WoCo 9 on Grid-based Problem Solving Environments: Implications for Development and Deployment of Numerical Software, IFIP WG 2.5 on Numerical Software, Prescott, AZ, USA, In Grid-Based Problem Solving Environments, ed. Gaffney, P. W and Pool, J. C. T. IFIP, Vol. 239. Boston: Springer, pp. 35–61.
Wang, J., Altintas, I., Berkley, C., Gilbert, L., and Jones, M. B. (2008). A high-level distributed execution framework for scientific workflows. In Proceedings of Workshop SWBES08: Challenging Issues in Workflow Applications, 4th IEEE International Conference on e-Science (e-Science 2008), New York, pp. 634–639.
Wang, J., Altintas, I., Hosseini, P. al. (2009a). Accelerating parameter sweep workflows by utilizing ad-hoc network computing resources: An ecological example. In Proceedings of IEEE 2009 Third International Workshop on Scientific Workflows (SWF 2009), 2009 Congress on Services (Services 2009), pp. 267–274.
Wang, J., Crawl, D., and Altintas, I. (2009b). Kepler + Hadoop : A general architecture facilitating data-intensive applications in scientific workflow systems. In Proceedings of the 4th Workshop on Workflows in Support of Large-Scale Science (WORKS09) at Supercomputing 2009 (SC2009) Conference. Portland, OR: ACM.
Wang, J., Korambath, P., Kim, al. (2010). Theoretical enzyme design using the Kepler scientific workflows on the Grid. Accepted by 5th Workshop on Computational Chemistry and Its Applications (5th CCA) at International Conference on Computational Science (ICCS 2010), Amsterdam, The Netherlands, 2010.
Yu, J. and Buyya, R. (2005). A taxonomy of scientific workflow systems for Grid computing. In ACM SIGMOD Record, ed. Ludaescher, B. and Goble, C.. Special Issue on Scientific Workflows, 34(3).