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Engineering the Black-Box Meta Model of Data Exploration

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Advances in Enterprise Engineering XIII (EEWC 2019)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 374))

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Abstract

With an increasing amount and diversity of available data, data exploration is becoming critical for many businesses to create insights for business innovation. From an analysis and design perspective, however, data exploration is still dominated by IT-oriented modeling concepts that make it difficult to engage business users because they are not used to think in terms of (even conceptual) data structures, but rather in terms of business questions, intended insights, decision context, information quality, etc. This study elaborates requirements for conceptualizing data exploration from a business perspective, discusses to what extent existing business-oriented conceptualizations fulfil such requirements, and consolidates promising modeling concepts into a meta model proposal that links purpose, context, domain knowledge, exploration history, business question, available data, user, decision, business insight, presentation and non-user stakeholder as key concepts. The meta model is demonstrated by instantiating it to conceptualize five exemplary data exploration use cases, and first evaluative evidence is presented. The paper closes with a discussion how to transform the proposed meta model into an innovative data exploration analysis and design tool.

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Author information

Authors and Affiliations

  1. University of St. Gallen, 9000, St. Gallen, Switzerland

    Robert Winter

  2. Southwest Petroleum University, Chengdu, 610500, China

    Li Yang

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  1. Robert Winter
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  2. Li Yang
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Correspondence to Robert Winter .

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Editors and Affiliations

  1. University of Madeira, Funchal, Portugal

    David Aveiro

  2. Free University of Bozen-Bolzano, Bolzano, Italy

    Giancarlo Guizzardi

  3. University of Lisbon, Lisbon, Portugal

    José Borbinha

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Winter, R., Yang, L. (2020). Engineering the Black-Box Meta Model of Data Exploration. In: Aveiro, D., Guizzardi, G., Borbinha, J. (eds) Advances in Enterprise Engineering XIII. EEWC 2019. Lecture Notes in Business Information Processing, vol 374. Springer, Cham. https://doi.org/10.1007/978-3-030-37933-9_6

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  • DOI: https://doi.org/10.1007/978-3-030-37933-9_6

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-37933-9

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