Organisations typically have to cope with large numbers of business rules and existing regulations governing the business in which they operate. Due to the size and complexity of those rules, maintenance is difficult and it is increasingly complicated to ensure that each business process adheres to those rules. As such, automated extraction of business processes from rules has a number of clear advantages: (1) visualisation of all possible executions allowed by the rules, (2) automated execution and compliance by design, (3) identification of "inefficiencies" in the business rules. Existing approaches, however, only allow to generate partial traces based on input specifications and cannot handle many different input cases resulting in a full process. This paper presents a formal method to visualise and operationalise such sets of rules as a verifiable business process that is compliant by design and allows us to analyse all possible execution paths. In addition, it maintains information of all distinct input cases, to preserve dependencies between consecutive exclusive paths.

Organisations have to cope with large numbers of business rules and existing regulations governing the business in which they operate. Such rules are difficult to maintain due to their size and complexity, and it is increasingly challenging to ensure that each business process adheres to those rules. As such, automated extraction of business processes from rules has three clear advantages: (1) visualisation of all possible executions allowed by the rules, (2) automated execution and compliance by design, (3) identification of "inefficiencies" in the business rules. Existing approaches, however, only allow for the generation of partial traces based on input specifications and cannot handle many different input cases resulting in a full process. This paper presents a formal method to visualise and operationalise such sets of rules as a verifiable business process that is compliant by design, which allows us to analyse all possible execution paths. Additionally, we formally prove correctness of the business processes generated by our method. The approach is implemented in a tool and evaluated on both performance and correctness, showing that even for highly complex sets of rules the approach performs well and outperforms a well-known state-of-the-art approach. Evaluation on a real-life process shows the feasibility of the presented approach. * This paper extends the work presented in [1] in the following ways: (i) it provides an updated and more efficient algorithm, (ii) it provides formal proofs of correctness of the algorithms, and (iii) it includes an extensive evaluation of the approach on both synthetic and real-life processes.

The interest of scholars in devising automated methods to describe and analyse business processes is increasing in the last decades due to the extreme interest of organisations in achieving their business objective while remaining compliant with the relevant normative system. Adhering with the relative normative system does not only avoid to incur in severe sanctions but results in greater confidence by the consumers and prestige for the organisation. Defining processes through the paradigm declarative specifications is gaining momentum due to its intrinsic characteristic of being able to capture business as well as normative specifications within the same framework. We describe some of the state of the art techniques in the field of Business Process Compliance, focusing on pros and cons and advancing future lines of research.

We present a planner named Transition Constraints for Parallel Planning (TCPP). TCPP constructs a new constraint model from domain transition graphs (DTG) of a given planning problem. TCPP encodes the constraint model by using table constraints that allow don't cares or wild cards as cell values. TCPP uses Minion the constraint solver to solve the constraint model and returns the parallel plan. Empirical results exhibit the efficiency of our planning system over state-of-the-art constraint-based planners.