Automating the Adaptation of Evolving Data-Intensive Ecosystems

Petros Manousis, Panos Vassiliadis, George Papastefanatos
32nd International ER International Conference on Conceptual Modeling (ER 2013), Hong Kong, 11-13, November, 2013.
Abstract. Data-intensive ecosystems are conglomerations of data repositories surrounded by applications that depend on them for their operation. To support the graceful evolution of the ecosystem's components we annotate them with policies for their response to evolutionary events. In this paper, we provide a method for the adaptation of ecosystems based on three algorithms that (i) assess the impact of a change, (ii) compute the need of different variants of an ecosystem's components, depending on policy conflicts, and (iii) rewrite the modules to adapt to the change.