Data integration plays a crucial role in business intelligence, aiding decision-makers by consolidating data from heterogeneous sources to provide deep insights into business operations and performance. In the big data era, automated data integration solutions need to process high volumes of disparate data robustly and seamlessly for various analytical needs or operational actions. Existing data integration solutions exhibit limited capabilities for capturing and modeling users' needs to execute on-demand data integration. This study, underpinned by affordance theory and the goal definition principles from the Goal-Question-Metric approach, designs and instantiates a goal-driven data integration framework for data analytics. The proposed innovative design automates data integration for non-technical data users. Specifically, it demonstrates how to elicit and ontologize users' data-analytic goals and addresses semantic heterogeneity, thereby recognizing goal-relevant datasets. In a structured evaluation using the context of counter-terrorism analytics, our design artifact shows promising performance in capturing diverse and dynamic user goals for data analytics and in generating integrated data tailored to these goals. Our research establishes a theoretical framework to guide future scholars and practitioners in building smart, goal-driven data integration.

中文翻译：

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开发目标驱动的数据集成框架以进行有效的数据分析

数据集成在商业智能中发挥着至关重要的作用，通过整合来自异构源的数据来帮助决策者提供对业务运营和绩效的深入洞察。在大数据时代，自动化数据集成解决方案需要稳健、无缝地处理大量不同的数据，以满足各种分析需求或操作操作。现有的数据集成解决方案在捕获和建模用户需求以执行按需数据集成方面表现出有限的能力。这项研究以可供性理论和目标-问题-度量方法的目标定义原则为基础，设计并实例化了一个用于数据分析的目标驱动的数据集成框架。所提出的创新设计可以为非技术数据用户实现数据集成的自动化。具体来说，它演示了如何引出用户的数据分析目标并将其本体化，并解决语义异质性，从而识别与目标相关的数据集。在使用反恐分析背景的结构化评估中，我们的设计工件在捕获数据分析的多样化和动态用户目标以及生成针对这些目标定制的集成数据方面表现出了良好的性能。我们的研究建立了一个理论框架来指导未来的学者和实践者构建智能的、目标驱动的数据集成。