Graph-based managing and mining of processes and data in the domain of intellectual property

Digitalization of knowledge work in communication-intensive domains such as intellectual property protection poses great challenges but also opportunities to improve today’s working environments. The legal domain is strongly characterized by work, whereby, despite a common framework, creativity individual experts decisive. This knowledge-intensive deals with amount data objects, not only basis, result. While heavily follow styles, they still rely on vast administrative tasks, which are carried out the supporting staff. These tasks expected be performed regularly, reliably and without errors, necessary adjustments current case changing framework. Today, typically supported different tools that hardly integrated. Therefore, tracing continuous processes based exchanged objects challenge. traceability crucial, for security reasons, enable mining learning applicable about processes. In this paper, we propose bottom-up approach, applies continuously evolving graph integrated model store static dynamic aspects well test approach real-world setting property. We further present initial results novel dependency-based learn data-dependent task sequences graph-based discuss several methods enabling privacy-preserving sharing mining.