Big Data Pipelines for Cross-Domain Decision Support: A Cloud-Centric Approach

Abstract

Advancements in cloud computing technologies and the resultant involvement of the big data paradigm have catalyzed the emergence of novel cloud-centric data pipelines that streamline the pipelines' distinct phases. An overview of these advances is given, covering their role in cross-domain decision support—one of the greatly useful yet difficult-to-achieve standards in big data applications—upon which their motivation is grounded. The discussion transcends a mere listing of the recent patterns and principles by merging them in a coherent, syntactic whole, culminating in an evaluation framework for the assessment of supporting cloud-centric decision-support pipelines.
Cross-domain decision support addresses the supply of information necessary for decision making across several thematic data domains. Supported by data offerings from different application domains, such type of support enables and simplifies correlated or congruous decisions involving diverse subjects or instances, which would otherwise require engaging expert resources from the different areas. As naturally occurring or deployed data in several themes are stored using cloud solutions, the public or private clouds that combine the different data sources into one logical entity become the pivotal data pipelines for such cross-domain decision-support actions.