Innovation pattern analysis

The evolution of innovation management in last decades was strongly influenced and led by the theory of the “Open Innovation” introduced by Chesbrough [1], and has become one of the hottest topic in business Literature. In the current economical scenario an increasingly number of organizations decide to adopt a more open approach in their innovation policy, trying to establish more or less strong relations with external partners, directly involving them in innovative projects. Consequently the collaborative work is gaining a growing importance in innovation practices of organizations, since the success or failure of innovative projects is often strictly related to results of collaborative tasks. Therefore, to support innovation processes of an organization one can investigate and improve its collaboration practices, with the aim to discover the best ones, i.e. those that maximize the success probability of organizations innovative projects. However, this kind of analysis is often prevented by the lack of real world data, mainly due to the limited diffusion of innovation management systems capable to collect innovation activities traces. Nevertheless, the daily activities of an enterprise, both internal and external, are almost completely performed by software systems. Both explicitly and implicitly, these systems keep track of users activities, e.g. ERP logs, versioning systems, list of emails, file timestamps, and so forth. In the present work we propose a methodology aimed to discover relevant collaboration patterns based on real data daily collected by enterprises, with the aim of providing business users with a better understanding of the dynamics of the interactions among members of collaborating groups. Our idea is firstly to collect any kind of data produced during the collaborative development of an innovation project, then to integrate them into a unique knowledge base storing traces of enterprise activities. Through preprocessing analysis, such traces are translated into process schemas, that can be considered as a representation of collaborative innovation processes in the organization, on which we can perform pattern discovery. To this aim we consider hierarchical clustering, which is capable to extracts frequent subprocesses representing common collaboration patterns and to arrange them in a hierarchy with different level of abstractions. The rest of this work is organized in two sections, the former aimed to describe the main ideas of the methodology, the latter to sketch out future extensions we plan to conduct.