AIBPS 2014 - International Workshop on Artificial Intelligence meets Business Processes and Services (AIBPS2014)

Business Process Management (BPM) is a set of activities aimed at defining, executing, monitoring and optimizing business processes, with the objective of making the business of an enterprise as effective and efficient as possible, and of increasing its economic success.
Such activities are highly automated, traditionally by means of workflow technology, in BPM Systems, or, more generally, Process-Aware Information Systems (PAISs) which also encompass other types of processes related to human activity (health care, emergency management, home automation, etc.). Service-Oriented Computing (SOC) is a computing paradigm that uses services as the basic constructs to support rapid, low-cost development of distributed applications in heterogeneous environments. The main purpose of SOC is to create a world of cooperating services loosely connected, creating dynamic business processes and agile applications that span organizations and platforms.
Every aspect of a process/service involves a certain amount of knowledge, and common BPM/SOC modeling approaches are not necessarily able to cope with the divergence from structured, pre-defined models, due to autonomous user decisions and to unpredictable, emergent events and contextual changes, which make the structure of processes and services significantly less rigid.
To tackle these issues, several Artificial Intelligence methodologies can be exploited to design, model and manage business processes and services. Knowledge representation and reasoning techniques can be used for modeling processes/services and exceptions, for modeling background knowledge (e.g. in the form of ontologies) and to reason about them (e.g. for logic-based verification). Moreover, since many systems share the idea of recalling and reusing concrete examples of change adopted in the past, Case-based Reasoning can be exploited, to retrieve adaptation cases, and to support the user in the overall adaptation task. When adaptations take place, quality evaluation is needed and compliance of the new version of the process/service with respect to specific semantic constraints can again be verified (on line or post mortem). As a final example, data mining techniques can be resorted to when the default process schema is not known, but has to be learned from a set of available execution traces. Such methodologies have proved to be helpful in a wide range of application domains, from industrial to medical ones.
The workshop aims at collecting methodological and application papers on the topic, addressing research on modeling and theory of business processes, support to process adaptation and flexibility, process mining, process verification. The final goal is to stimulate the exchange of novel as well as more consolidated ideas and examples in the field, and to identify promising research lines and challenges for the future.
Contributions are welcome on AI approaches for the following non-exhaustive list of topics:
Process/service modeling, notations and methods, esp. declarative ones, for processes/services and their background knowledge
Process adaptation and optimization
Process mining
Dynamic configuration
Artifact-centric business processes
Process/service verification, analysis and validation
Run-time verification and monitoring
Recommendations in business process and service modeling
Case studies, empirical evaluations and experimentations