Modeling of Collaboration Archetypes in Digital Market Places

Abstract

With everyone collecting and generating value out of data, this paper focus on distributed data trading platforms, digital market places (DMPs). The DMPs can handle the intricacies of data sharing: how, where, and what can be done with the traded data. Here, we represent collaborations among involving parities in DMPs in the form of archetypes and model them with numeric representations for easier manipulation with standard mathematical tools. We also develop an algorithm that aims to map any customer-defined trust-dependent application request into a best-fit infrastructure archetype in a DMP. Also, we propose multiple metrics that allow evaluate and compare competing the DMPs systemically from more dimensions: coverage, extensibility, precision, and flexibility. We demonstrate the effectiveness of these metrics in a concrete use case.