Convergence of Data Mining and Process Management for Operational Inteligence

ABSTRACT Convergence of data mining and process management is ideal -but still limited. Data mining techniques helps in actionable knowledge discovery but lack for context awareness whereas process management systems support context awareness but lack for operational intelligence. To make process management systems operational intelligent, data mining techniques can be integrated within them in removing different inefficiencies. This paper presents an example of such a convergence in resolving one of the inefficiency relating to its resource management specifically to its static agent assignment strategies. To highlight the potentials of this convergence, an exemplary use case from textile industry is presented and discussed in depth along with experiments and experiences from textile industry

A policy-based authorization model for workflow-enabled dynamic process management

Although workflow has been widely used to support the modeling and execution of business process, the majority of current workflow management systems are not designed and suited for supporting dynamic business processes. One of the deficiencies is the inability to model realistically the organization of an enterprise to manage the dynamic human-centric business processes. A framework for workflow-enabled dynamic business process management is described in the paper. It includes an organizational model and an authorization model for supporting dynamic business processes. More specifically, authorization policies are expressed in an SQL-like language which can be easily rewritten into query sentences for execution. In addition, the framework supports dynamic integration and execution of multiple access control polices from disparate enterprise resources. Finally, a prototype implementation of the dynamic business process management framework is described