The Data Product-service Composition Frontier: A Hybrid Learning Approach

Abstract

The service dominant logic is a base concept behind modern economies and software products, with service composition being a well-known practice for companies to gain a competitive edge over others by joining differentiated services together, typically assembled according to a number of features. At the other end of the spectrum, product compositions are a marketing device to sell products together in bundles that often augment the value for the customer, e.g., with suggested product interactions, sharing, and so on. Unfortunately, currently each of these two streams—namely, product and service composition—are carried out and delivered individually in splendid isolation: anything is being offered as a product and as a service, disjointly. We argue that the next wave of services computing features more and more service fusion with physical counterparts as well as data around them. Therefore a need emerges to investigate the interactive engagement of both (data) products and services. This manuscript offers a real-life implementation in support of this argument, using (1) genetic algorithms (GA) to shape product-service clusters, (2) end-user feedback to make the GAs interactive with a data-driven fashion, and (3) a hybridized approach which factors into our solution an ensemble machine-learning method considering additional features. All this research was conducted in an industrial environment. With such a cross-fertilized, data-driven, and multi-disciplinary approach, practitioners from both fields may benefit from their mutual state of the art as well as learn new strategies for product, service, and data product-service placement for increased value to the customer as well as the service provider. Results show promise but also highlight plenty of avenues for further research.