Explaining the Incorrect Temporal Events during Business Process Monitoring by Means of Compliance Rules and Model-Based Diagnosis

Sometimes the business process model is not known completely, but a set of compliance rules can be used to describe the ordering and temporal relations between activities, incompatibilities, and existence dependencies in the process. The analysis of these compliance rules and the temporal events thrown during the execution of an instance, can be used to detect and diagnose a process behaviour that does not satisfy the expected behaviour. We propose to combine model-based diagnosis and constraint programming for the compliance violation analysis. This combination facilitates the diagnosis of discrepancies between the compliance rules and the events that the process generates as well as enables us to propose correct event time intervals to satisfy the compliance rules.Austrian Science Fund (FWF):I743Ministerio de Ciencia y Tecnología TIN2009-1371

Diagnosing Business Processes

Ministerio de Ciencia y Tecnología TIN2015-63502-C3-2-

Compliance validation and diagnosis of business data constraints in business processes at runtime

Business processes involve data that can be modified and updated by various activities at any time. The data involved in a business process can be associated with flow elements or data stored. These data must satisfy the business compliance rules associated with the process, where business compliance rules are policies or statements that govern the behaviour of a company. To improve and automate the validation and diagnosis of compliance rules based on the description of data semantics (called Business Data Constraints), we propose a framework where dataflow variables and stored data are analyzed. The validation and diagnosis process is automated using Constraint Program-ming, to permit the detection and identification of possibly unsatisfiable Business Data Constraints, even if the data involved in these constraints are not all instantiated. This implies that the potential errors can be determined in advance. Furthermore, a language to describe Business Data Constraints is proposed, for the improvement of user-oriented aspects of the business process description. This language allows a business expert to write Business Data Constraints that will be automatically validated in run-time, without the support of an information technology expert.Junta de Andalucía P08-TIC-04095Ministerio de Ciencia y Tecnología TIN2009-1371

Partial lazy forward checking

Partial forward checking (PFC) may perform more consistency checks than really needed to detect dead-ends in MAX-CSP. After analyzing PFC, we have identified four causes of redundant check computation: (a) unnecessary lookahead when detecting an empty domain, (b) not always using the better bounds for future value pruning, (c) computing in advance inconsistency counts, and (d) lookahead is performed on the whole set of future variables. We present the partial lazy forward checking (PLFC) algorithm, which follows a lazy approach delaying as much as possible inconsistency count computation, keeping updated the contribution of future variables to the lower bound. This algorithm avoids the causes of redundant checks identified for PFC. It can be easily combined with DACs, producing the PLFC-DAC algorithm. Empirical results on random problems show that PLFC-DAC outperforms previous algorithms in both consistency checks and CPU time.Postprint (published version

Hybrid business process modeling for the optimization of outcome data

Context: Declarative business processes are commonly used to describe permitted and prohibited actions in a business process. However, most current proposals of declarative languages fail in three aspects: (1) they tend to be oriented only towards the execution order of the activities; (2) the optimization is oriented only towards the minimization of the execution time or the resources used in the business process; and (3) there is an absence of capacity of execution of declarative models in commercial Business Process Management Systems. Objective: This contribution aims at taking into account these three aspects, by means of: (1) the formalization of a hybrid model oriented towards obtaining the outcome data optimization by combining a data-oriented declarative specification and a control-flow-oriented imperative specification; and (2) the automatic creation from this hybrid model to an imperative model that is executable in a standard Business Process Management System. Method: An approach, based on the definition of a hybrid business process, which uses a constraint programming paradigm, is presented. This approach enables the optimized outcome data to be obtained at runtime for the various instances. Results: A language capable of defining a hybrid model is provided, and applied to a case study. Likewise, the automatic creation of an executable constraint satisfaction problem is addressed, whose resolution allows us to attain the optimized outcome data. A brief computational study is also shown. Conclusion: A hybrid business process is defined for the specification of the relationships between declarative data and control-flow imperative components of a business process. In addition, the way in which this hybrid model automatically creates an entirely imperative model at design time is also defined. The resulting imperative model, executable in any commercial Business Process Management System, can obtain, at execution time, the optimized outcome data of the process.Ministerio de Ciencia y Tecnología TIN2009-1371

A New Approach for Weighted Constraint Satisfaction

10.1023/A:1015157615164Constraints72151-165CNST