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Data Transformation Knowledge Reuse in Spreadsheet-Based Mashup Development Platform

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Web Services Foundations

Abstract

Data transformation is a key task in mashup development (e.g., access to heterogeneous services, data flow). It is considered as a labour-intensive and error-prone process. The possibility of reusing previously specified mappings promises a significant reduction in manual and time-consuming transformation tasks, nevertheless its potential has not been fully realized in current approaches and systems. In this chapter, we study the problem of data transformation logic reuse in mashup development platforms. We formulate the problem and propose a solution that features novel reuse abstractions and techniques including spreadsheet templates, mapping generalization, and similarity join. Given a spreadsheet instance that is being mapped to the target schema, we recommend a list of mapping formulas that can be potentially reused for the instance. We implemented a prototype of the proposed solution and evaluated its performance via synthetic datasets.

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Authors and Affiliations

  1. University of New South Wales, Sydney, Australia

    Vu Hung, Boualem Benatallah & Angel Lagares Lemos

Authors
  1. Vu Hung
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  2. Boualem Benatallah
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  3. Angel Lagares Lemos
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Correspondence to Vu Hung .

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Editors and Affiliations

  1. School of Computer Science and Information Technology, RMIT University, Melbourne, Victoria, Australia

    Athman Bouguettaya

  2. School of Computer Science, University of Adelaide School of Computer Science, Adelaide, South Australia, Australia

    Quan Z. Sheng

  3. dell'Informazione, Università di Trento Dipto. Ingegneria e Scienza, Povo, Trento, Italy

    Florian Daniel

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Hung, V., Benatallah, B., Lemos, A.L. (2014). Data Transformation Knowledge Reuse in Spreadsheet-Based Mashup Development Platform. In: Bouguettaya, A., Sheng, Q., Daniel, F. (eds) Web Services Foundations. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7518-7_25

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  • DOI: https://doi.org/10.1007/978-1-4614-7518-7_25

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  • Print ISBN: 978-1-4614-7517-0

  • Online ISBN: 978-1-4614-7518-7

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