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<p>Data-driven systems integrate and analyze large volumes of distributed and highly heterogeneous data (a.k.a. big data) to discover trends and build predictive models, which in turn are the basis of decision-making processes in various sectors of our life. However, the complexity of data and their quality, which is far from being perfect, makes building such systems challenging. One of the main challenges is in building predictive models that are explainable (interpretable by a human). This paper serves as an introduction to the special issue of the Information Systems Frontiers (ISF) journal, entitled <i>Towards Designing Valuable and Explainable Data-Driven Systems</i>. Its five papers were selected among the best ones from the European Conference on Advances in Databases and Information Systems (ADBIS), held in 2023 in Barcelona. For each of these papers, we provide a brief contextualization, followed by a short description of its main contributions.</p>

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Towards Designing Valuable and Explainable Data-Driven Systems

  • 期刊论文

  • 发表日期：

    2026年3月28日

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  • Alberto Abelló,
  • Ladjel Bellatreche,
  • Oscar Romero,
  • Panos Vassiliadis,
  • Robert Wrembel

摘要

Data-driven systems integrate and analyze large volumes of distributed and highly heterogeneous data (a.k.a. big data) to discover trends and build predictive models, which in turn are the basis of decision-making processes in various sectors of our life. However, the complexity of data and their quality, which is far from being perfect, makes building such systems challenging. One of the main challenges is in building predictive models that are explainable (interpretable by a human). This paper serves as an introduction to the special issue of the Information Systems Frontiers (ISF) journal, entitled Towards Designing Valuable and Explainable Data-Driven Systems. Its five papers were selected among the best ones from the European Conference on Advances in Databases and Information Systems (ADBIS), held in 2023 in Barcelona. For each of these papers, we provide a brief contextualization, followed by a short description of its main contributions.