Modeling Big Data-centric Services using Knowledge Graphs

Abstract

Big services have recently emerged from the synergy between big data and cloud computing paradigms. This new big data-centric service model aims to provide customer-oriented massive services by combining both physical and virtualized resources from different domains. Although such complex ecosystem is able to process, encapsulate and offer huge volumes of data as services, its management operations are beyond the ability of human administrators, due to several challenges including the big services’ large-scale nature and complexity, the heterogeneity of their components (e.g., services, data sources, connected things), the dynamicity and uncertainty of their hosting cloud environments. To cope with the lack of understanding regarding big services capabilities, we propose to describe them using a novel meta-model for the quality of big services (QoBS). We also take advantage of a recent technology called knowledge graphs, to represent the big service information (service descriptions, services’ and data sources’ quality levels, management policies) as a heterogeneous information network. Finally, a multi-view representation learning approach is proposed to infer additional knowledge regarding big services capabilities.