Abstract
The Ad hoc DAta Grid Environment (ADAGE) has been proposed as a framework to support analysis processes for large repositories of ad hoc data. Its use of a service-oriented architecture (SOA) brings the promise of flexibility, as well as enabling domain experts to define their own analysis processes at a high level of abstraction. However, these claims have not been verified empirically and the performance penalty of using additional abstract software layers has not been assessed on complex problems. This chapter describes a case study involving a realistic analysis process conducted by an expert user. It assesses the benefits and drawbacks of using the ADAGE approach versus conventional manual analysis processes. This chapter also outlines some avenues for future research to address existing limitations.
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References
Hey, T., Tansley, S., Tolle, K. (eds.): The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research, Redmond (2009)
Ozdemir, V., Smith, C., Bongiovanni, K., Cullen, D., Knoppers, B.M., Lowe, A., Peters, M., Robbins, R., Stewart, E., Yee, G., Yu, Y.K., Kolker, E.: Policy and Data-Intensive Scientific Discovery in the Beginning of the 21st Century. OMICS A Journal of Integrative Biology 15(4), 221–225 (2011)
Szalay, A.: Extreme Data-Intensive Scientific Computing. IEEE Computing in Science and Engineering 13(6), 34–41 (2011)
Tukey, J.W.: Exploratory Data Analysis. Addison-Wesley Publishing Company, Reading (1977)
Hartwig, F., Dearing, B.E.: Exploratory Data Analysis. Sage Publications, Beverly Hills (1979)
Dacorogna, M.M., Gençay, R., Müller, U., Olsen, R.B., Pictet, O.V.: An Introduction to High-Frequency Finance. Academic Press, San Diego (2001)
Yao, L., Rabhi, F.A.: Modelling Exploratory Analysis Processes for eResearch. In: Proceedings of 21st Australasian Conference on Information Systems, ACIS 2010 (2010), http://aisel.aisnet.org/acis2010/14
Guabtni, A., Kundisch, D., Rabhi, F.A.: A User-Driven SOA for Financial Market Data Analysis. Enterprise Modelling and Information Systems Architectures 5(2), 4–19 (2010)
Rabhi, F., Baradarannia, M., Yao, L.: A Case Study Using the Ad-hoc DAta Grid Environment (ADAGE) for Financial Time Series Building. In: Proceedings of FinanceCom 2010 (2010)
Rabhi, F.A., Yao, L., Guabtni, A.: ADAGE: A Framework for Supporting User-Driven Ad-Hoc Data Analysis Processes. Computing 94(6), 489–519 (2012)
Nutt, G.J.: Open Systems. Prentice Hall, Englewood Cliffs (1992)
Peng, R.D.: Reproducible Research in Computational Science. Science 334(6060), 1226–1227 (2011)
Houstis, E.N., Catlin, A.C., Tsompanopoulou, P., Gottfried, D., Balakrishnan, G., Su, K., Rice, J.R.: GasTurbnLab: a multidisciplinary problem solving environment for gas turbine engine design on a network of nonhomogeneous machines. Journal of Computational and Applied Mathematics 149(1), 83–100 (2002)
Shu, J., Watson, L.T., Zombori, B.G., Kamke, F.A.: WBCSim: an environment for modeling wood-based composites manufacture. Engineering with Computers 21(4), 259–271 (2006)
Wilkins-Diehr, N.: Science Gateways—Common Community Interfaces to Grid Resources. Concurrency and Computation: Practice and Experience 19(6), 743–749 (2007)
Barbera, R., Falzone, A., Ardizzone, V., Scardaci, D.: The GENIUS Grid Portal: Its Architecture, Improvements of Features, and New Implementations about Authentication and Authorization. In: Proceedings of the 16th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 2007), pp. 279–283 (2007)
Watson, P., Hiden, H., Woodman, S.: e-Science Central for CARMEN: science as a service. Concurrency and Computation: Practice and Experience 22(17), 2369–2380 (2010)
Thomson Reuters Tick History, http://thomsonreuters.com/products_services/financial/financial_products/quantitave_research_trading/tick_history
Rabhi, F.A., Guabtni, A., Yao, L.: A Data Model for Processing Financial Market and News Data. International Journal of Electronic Finance 3(4), 387–403 (2009)
Klösgen, W., Żytkow, J.M.: Knowledge Discovery in Databases: The Purpose, Necessity, and Challenges. In: Klösgen, W., Żytkow, J.M. (eds.) Handbook of Data Mining and Knowledge Discovery, pp. 1–9. Oxford University Press, New York (2002)
Houstis, E., Gallopoulos, E., Bramley, R., Rice, J.: Problem-Solving Environments for Computational Science. IEEE Computational Science and Engineering 4(3), 18–21 (1997)
Oinn, T., Addis, M., Ferris, J., Marvin, D., Senger, M., Greenwood, M., Carver, T., Glover, K., Pocock, M.R., Wipat, A., Li, P.: Taverna: A Tool for the Composition and Enactment of Bioinformatics Workflows. Bioinformatics 20(17), 3045–3054 (2004)
Carmichael, R., Braga-Henebry, P., Thain, D., Emrich, S.: Biocompute 2.0: An Improved Collaborative Workspace for Data Intensive Bio-science. Concurrency and Computation: Practice and Experience 23(17), 2305–2314 (2011)
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Yao, L., Rabhi, F.A., Peat, M. (2013). A Case Study in Using ADAGE for Compute-Intensive Financial Analysis Processes. In: Rabhi, F.A., Gomber, P. (eds) Enterprise Applications and Services in the Finance Industry. FinanceCom 2012. Lecture Notes in Business Information Processing, vol 135. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36219-4_6
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DOI: https://doi.org/10.1007/978-3-642-36219-4_6
Publisher Name: Springer, Berlin, Heidelberg
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