The integration of large systems remains problematic in spite of advances in composite services approaches. Current composite services approaches can effectively address only the integration of mechanistic processes. Processes with human interaction, abundant in large systems, are integrated in an ad hoc manner. As a result, a process gap occurs between user needs and implemented systems. The lack of support for human interaction with processes in large systems is referred to as the composite process-personalization (CPP) challenge.

The CPP problems can occur at two stages. First, they occur at the modeling stage because of the difficulties in capturing the semantics of user needs. The lack of understanding of the problem domain and scope of integration of user needs results in a loss of semantics at this stage. Second, the CPP problems occur at the composition stage because of the lack of technology support for configuring and managing the semantics of user needs captured during the modeling stage.

The composite P²_FRAMEWORK, a composition, integration, and personalization framework, addresses the CPP problems in two ways. First, systematic guidance is provided to alleviate the problems of loss of semantics at the modeling stage. The systematic guidance is based on the dimensions of CPP, which identify the semantic and syntactic needs of large systems integration. The semantic aspects of the dimension provide guidance for the capturing of user needs. The syntactic aspects specify the need for flexible integration and automation of large systems processes. Second, the framework uses the service-agent model, a modeling approach for configuring and managing the semantics of user needs and developing composite services with CPP. The service-agent model supports both the semantic and syntactic dimensions of CPP.

The guidance of the composite P²_FRAMEWORK for large systems integration with CPP is demonstrated and validated by using two case studies. The first case study is a weather composite service process with a human interaction task. The second case study is the gene linkage identification part of the composite epidemiology research process for identifying candidate genes for obesity research.