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The contribution of linked open data to augment a traditional data warehouse

机译：链接开放数据增强传统数据仓库的贡献

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摘要

The arrival of Big Data has contributed positively to the evolution of the data warehouse (DW ) technology. This gives birth of augmented DW s that aim at maximizing the effectiveness of existing ones. Various augmentation scenarios have been proposed and adopted by firms and industry covering several aspects such as new data sources (e.g., Linked Open Data (LOD), social, stream and IoT data), data ingestion, advanced deployment infrastructures, programming paradigms, data visualization. These scenarios allow companies reaching valuable decisions. By examining traditional DW s, we realized that they do not fulfill all decision-maker requirements since data sources alimenting a target DW are not rich enough to capture Big Data. The arrival of LOD era is an excellent opportunity to enrich traditional DW s with a new V dimension: Value. In this paper, we first conceptualize the variety of internal and external sources and study its effect on the ETL phase to ease the value capturing. Secondly, a Value-driven approach for the DW design is discussed. Thirdly, three realistic scenarios for integrating LOD in the DW landscape are given. Finally, experiments are conducted showing the added value by augmenting the existing DW environment with LOD.
机译：大数据的到来对数据仓库（DW）技术的演变提供了积极的贡献。这给了增强DW S的诞生，旨在最大化现有的效力。公司和行业已经提出和采用了各种增强方案，包括新数据源（例如，链接开放数据（LOD），社交，流和物联网数据），数据摄取，高级部署基础架构，编程范例，数据可视化。这些方案允许公司达到有价值的决策。通过审查传统的DW S，我们意识到他们不符合所有决策者要求，因为反向目标DW的数据源不足以捕获大数据。 LOD ERA的到来是丰富传统DW S的绝佳机会，以新的V维度：价值。在本文中，我们首先将各种内部和外部来源概念化，并研究其对ETL相位的影响，以缓解捕获值。其次，讨论了DW设计的值驱动方法。第三，给出了一个用于在DW横向中集成LOD的三种现实情景。最后，通过使用LOD加强现有的DW环境来进行实验。

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