Diana Borrego

Conformance checking and diagnosis for declarative business process models in data-aware scenarios

A business process (BP) consists of a set of activities which are performed in coordination in an organizational and technical environment and which jointly realize a business goal. In such context, BP management (BPM) can be seen as supporting BPs using methods, techniques, and software in order to design, enact, control, and analyze operational processes involving humans, organizations, applications, and other sources of information. Since the accurate management of BPs is receiving increasing attention, conformance checking, i.e., verifying whether the observed behavior matches a modelled behavior, is becoming more and more critical. Moreover, declarative languages are more frequently used to provide an increased flexibility. However, whereas there exist solid conformance checking techniques for imperative models, little work has been conducted for declarative models. Furthermore, only control-flow perspective is usually considered although other perspectives (e.g., data) are crucial. In addition, most approaches exclusively check the conformance without providing any related diagnostics. To enhance the accurate management of flexible BPs, this work presents a constraint-based approach for conformance checking over declarative BP models (including both control-flow and data perspectives). In addition, two constraint-based proposals for providing related diagnosis are detailed. To demonstrate both the effectiveness and the efficiency of the proposed approaches, the analysis of different performance measures related to a wide diversified set of test models of varying complexity has been performed.

Diagnosing correctness of semantic workflow models

To model operational business processes in an accurate way, workflow models need to reference both the control flow and dataflow perspectives. Checking the correctness of such workflow models and giving precise feedback in case of errors is challenging due to the interplay between these different perspectives. In this paper, we propose a fully automated approach for diagnosing correctness of semantic workflow models in which the semantics of activities are specified with pre and postconditions. The control flow and dataflow perspectives of a semantic workflow are modeled in an integrated way using Artificial Intelligence techniques (Integer Programming and Constraint Programming). The approach has been implemented in the DiagFlow tool, which reads and diagnoses annotated XPDL models, using a state-of-the-art constraint solver as back end. Using this novel approach, complex semantic workflow models can be verified and diagnosed in an efficient way.

Diagnosing errors in dbc programs using constraint programming

Model-Based Diagnosis allows to determine why a correctly designed system does not work as it was expected. In this paper, we propose a methodology for software diagnosis which is based on the combination of Design by Contract, Model-Based Diagnosis and Constraint Programming. The contracts are specified by assertions embedded in the source code. These assertions and an abstraction of the source code are transformed into constraints, in order to obtain the model of the system. Afterwards, a goal function is created for detecting which assertions or source code statements are incorrect. The application of this methodology is automatic and is based on Constraint Programming techniques. The originality of this work stems from the transformation of contracts and source code into constraints, in order to determine which assertions and source code statements are not consistent with the specification.

Automating correctness verification of artifact-centric business process models

Artifact-centric business process models are fully automatically verified.Two correctness notions are verified: reachability and weak termination.The models integrate pre and postconditions defining the behavior of the services.Verification of numerical data, even for models formed by several artifacts.Novel verification algorithms check the correctness, offering precise diagnosis. ContextThe artifact-centric methodology has emerged as a new paradigm to support business process management over the last few years. This way, business processes are described from the point of view of the artifacts that are manipulated during the process. ObjectiveOne of the research challenges in this area is the verification of the correctness of this kind of business process models where the model is formed of various artifacts that interact among them. MethodIn this paper, we propose a fully automated approach for verifying correctness of artifact-centric business process models, taking into account that the state (lifecycle) and the values of each artifact (numerical data described by pre and postconditions) influence in the values and the state of the others. The lifecycles of the artifacts and the numerical data managed are modeled by using the Constraint Programming paradigm, an Artificial Intelligence technique. ResultsTwo correctness notions for artifact-centric business process models are distinguished (reachability and weak termination), and novel verification algorithms are developed to check them. The algorithms are complete: neither false positives nor false negatives are generated. Moreover, the algorithms offer precise diagnosis of the detected errors, indicating the execution causing the error where the lifecycle gets stuck. ConclusionTo the best of our knowledge, this paper presents the first verification approach for artifact-centric business process models that integrates pre and postconditions, which define the behavior of the services, and numerical data verification when the model is formed of more than one artifact. The approach can detect errors not detectable with other approaches.

Constraint satisfaction techniques for diagnosing errors in design by contract software

Design by Contract enables the development of more reliable and robust software applications. In this paper, a methodology that diagnoses errors in software is proposed. This is based on the combination of Design by Contract, Model-based Diagnosis and Constraint Programming. Contracts are specified by using assertions. These assertions together with an abstraction of the source code are transformed into constraints. The methodology detects if the contracts are consistent, and if there are incompatibilities between contracts and source code. The process is automatic and is based on constraint programming.

Data State Description for the Migration to Activity-Centric Business Process Model Maintaining Legacy Databases

One of the reasons why the companies keep out the business process adaptation, is focused on the complexity to adequate their databases to a Business Process Management. It implies to determine the relation between the activities of the process, and the data objects stored in the database. Our proposal allows the business expert to know the state of the data objects according to the business process, facilitating the migration.

Constraint-Driven Approach to Support Input Data Decision-Making in Business Process Management Systems

A business process consists of a set of activities that are performed in coordination in an organizational and technical environment (Weske 2007). The base of business process management systems (BPMS) is the explicit representation of business processes with their activities and the execution constraints between them. Compliance rules represent a natural step to include requirements between business functionality and data. For the design of a whole business process management (van der Aalst et al. 2003), it is necessary to design the model of activities and define the causal and temporal relationships between them (Walzer et al. 2008). Compliance rules can help to complete this information, since they can be used to validate business data (Chesani et al. 2008).

Minimizing test-point allocation to improve diagnosability in business process models

Diagnosability analysis aims to determine whether observations available during the execution of a system are sufficient to precisely locate the source of a problem. Previous work deals with the diagnosability problem in contexts such as circuits and systems, but no with the adaptation of the diagnosability problem to business processes. In order to improve the diagnosability, a set of test points needs to be allocated. Therefore, the aim of this contribution is to determine a test-point allocation to obtain sufficient observable data in the dataflow to allow the discrimination of faults for a later diagnosis process. The allocation of test points depends on the strategies of the companies, for this reason we defined two possibilities: to improve the diagnosability of a business process for a fixed number of test points and the minimization of the number of test points for a given level of diagnosability. Both strategies have been implemented in the Test-Point Allocator tool in order to facilitate the integration of the test points in the business process model life cycle. Experimental results indicate that diagnosability of business processes can be improved by allocating test points in an acceptable time.

Determination of an optimal test points allocation for business process analysis

The management and automation of business processes have become an essential task within IT organizations. Diagnosis enables fault isolation in a business process. The diagnosis process uses a set of test points (observations) and a model in order to explain a wrong behavior. In this work, a series of algorithms to allocate test points are presented. The key idea is to improve the diagnosability, improving the computational complexity for isolating faults in a system. The methodology is based on constraint programming.

Improving the Diagnosability of Business Process Management Systems Using Test Points

The management and automation of business processes have become an essential task within IT organizations, where the diagnosis is a very important issue, since it enables fault isolation in a business process. The diagnosis process uses a set of test points (observations) and a model in order to explain a wrong behavior. In this work, an algorithm to allocate test points is presented, where the key idea is to improve the diagnosability, getting a better computational complexity for isolating faults in the activities of business processes.