Declarative Process Models Research Articles

Complexity in declarative process models: Metrics and multi-modal assessment of cognitive load

Complex process models can hinder the comprehension of the underlying business processes. While several metrics have been suggested in the literature to evaluate the complexity of imperative process models, little is known about their declarative counterparts. In this paper, we address this gap through a suite of metrics that we propose to capture the complexity of declarative process models. Following this, we empirically investigate the impact of complexity, as measured by the suggested metrics, on users’ cognitive load when comprehending declarative process models. Therein, we use a multi-modal approach including eye-tracking and electrodermal activity. The findings of the empirical study provide evidence about the cognitive load emerging as a result of increased model complexity. Overall, the outcome of this paper presents empirically validated metrics to evaluate the complexity of declarative process models. Implementing these metrics and incorporating them in intelligent modeling tools would help assessing the complexity of declarative process models before being deployed. Furthermore, our empirical approach can be adopted by researchers in upcoming empirical studies to provide a multi-perspective assessment of users’ cognitive load when engaging with process models.

Automaton-based comparison of Declare process models

The Declare process modeling language has been established within the research community for modeling so-called flexible processes. Declare follows the declarative modeling paradigm and therefore guarantees flexible process execution. For several reasons, declarative process models turned out to be hard to read and comprehend. Thus, it is also hard to decide whether two process models are equal with respect to their semantic meaning, whether one model is completely contained in another one or how far two models overlap. In this paper, we follow an automaton-based approach by transforming Declare process models into finite state automatons and applying automata theory for solving this issue.

Modeling uncertainty in declarative artifact-centric process models using fuzzy logic

In many business processes, knowledge workers collect information and make decisions about business entities. Artifact-centric process (ACP) models have been proposed to represent such knowledge-intensive processes. Declarative ACP models use precise business rules to define flexible process executions. However, in many business situations knowledge experts have to deal with uncertainty and vagueness. Currently, how to deal with such situations cannot be expressed in declarative ACP models. We propose to use the fuzzy logic framework to model uncertainty in such models. Using Guard-Stage-Milestone (GSM) schemas as declarative ACP notation, we show how GSM schemas can be adapted for modeling gradual progress of real-life knowledge-intensive processes via a fuzzy logic interface. We evaluate the proposed fuzzy GSM schemas in real-life scenarios of airport ground operations and healthcare services.

Execution of Multi-Perspective Declarative Process Models Using Complex Event Processing

The Internet of Things (IoT) enables continuous monitoring of phenomena based on sensing devices as well as analytics opportunities in smart environments. Complex Event Processing (CEP) comprises a set of techniques for making sense of the behavior of a monitored system by deriving higher level knowledge from lower level system events. Business Process Management (BPM) attempts to model processes and ensures that executed processes con-form with a predefined sequence. In IoT scenarios frequently a large number of events has to be analyzed in real-time to allow an instant response. While BPM reaches its limits in such situ-ations, CEP is able to analyze and process high volume streams of data in real-time. The evaluation and execution of rules and models of both paradigms are currently based on separate formalisms and are frequently implemented in heterogeneous systems. The presented paper integrates both domains by proposing an execution approach for multi-perspective declarative process process models completely based on CEP. The efficiency of the combined paradigms is validated in an implemented demonstration with simulated and real-life sensor data.

DisCoveR: accurate and efficient discovery of declarative process models

DisCoveR: accurate and efficient discovery of declarative process models

Flexible runtime support of business processes under rolling planning horizons

This work has been motivated by the needs we discovered when analyzing real-world processes from the healthcare domain that have revealed high flexibility demands and complex temporal constraints. When trying to model these processes with existing languages, we learned that none of the latter was able to fully address these needs. This motivated us to design TConDec-R, a declarative process modeling language enabling the specification of complex temporal constraints. Enacting business processes based on declarative process models, however, introduces a high complexity due to the required optimization of objective functions, the handling of various temporal constraints, the concurrent execution of multiple process instances, the management of cross-instance constraints, and complex resource allocations. Consequently, advanced user support through optimized schedules is required when executing the instances of such models. In previous work, we suggested a method for generating an optimized enactment plan for a given set of process instances created from a TConDec-R model. However, this approach was not applicable to scenarios with uncertain demands in which the enactment of newly created process instances starts continuously over time, as in the considered healthcare scenarios. Here, the process instances to be planned within a specific timeframe cannot be considered in isolation from the ones planned for future timeframes. To be able to support such scenarios, this article significantly extends our previous work by generating optimized enactment plans under a rolling planning horizon. We evaluate the approach by applying it to a particularly challenging healthcare process scenario, i.e., the diagnostic procedures required for treating patients with ovarian carcinoma in a Woman Hospital. The application of the approach to this sophisticated scenario allows avoiding constraint violations and effectively managing shared resources, which contributes to reduce the length of patient stays in the hospital.

Language-independent look-ahead for checking multi-perspective declarative process models

Declarative process modelling languages focus on describing a process by restrictions over the behaviour, which must be satisfied throughout the whole process execution. Hence, they are well suited for modelling knowledge-intensive processes with many decision points. However, such models can be hard to read and understand, which affect the modelling and maintenance of the process models tremendously as well as their execution. When executing such declarative (multi-perspective) process models, it may happen that the execution of activities or the change of data values may result in the non-executability of crucial activities. Hence, it would be beneficial to know all consequences of decisions to give recommendations to the process participants. A look-ahead attempts to predict the effects of executing an activity towards possible consequences within an a priori defined time window. The prediction is based on the current state of the process execution, the intended next event and the underlying process model. While execution engines for single-perspective imperative process models already implement such functionality, execution approaches, for multi-perspective declarative process models that involve constraints on data and resources, are less mature. In this paper, we introduce a simulation-based look-ahead approach for multi-perspective declarative process models. This approach transforms the problem of a context-aware process simulation into a SAT problem, by translating a declarative multi-perspective process model and the current state of a process execution into a specification of the logic language Alloy. Via a SAT solver, process trajectories are generated that either satisfy or violate this specification. The simulated process trajectories are used to derive consequences and effects of certain decisions at any time of process execution. We evaluate our approach by means of three examples and give some advice for further optimizations.

Feature-oriented engineering of declarative artifact-centric process models

Declarative artifact-centric process models are suitable for specifying knowledge-intensive processes. Currently, such models need to be designed from scratch, even though existing model fragments could be reused to gain efficiency in designing and maintaining declarative artifact-centric process models. To address this problem, this paper proposes an approach for composing model fragments, abstracted into features, into fully specified declarative artifact-centric process models. We use Guard-Stage-Milestone (GSM) schemas as modeling language and let each feature denote a GSM schema fragment. The approach supports feature composition at different levels of granularity. Permutability of features is analyzed. Syntactic conditions that ensure permutability are defined and refactoring of non-permutable into permutable features is discussed. The approach has been evaluated by implementing it in a tool based on the Case Management Model Notation (CMMN) and applying it to three real-world processes. Using the approach, declarative artifact-centric process models can be composed from existing model fragments in an efficient, robust and correct way.

Exploring how users engage with hybrid process artifacts based on declarative process models: a behavioral analysis based on eye-tracking and think-aloud

Process design artifacts have been increasingly used to guide the modeling of business processes. To support users in designing and understanding process models, different process artifacts have been combined in several ways leading to the emergence of the so-called “hybrid process artifacts”. While many hybrid artifacts have been proposed in the literature, little is known about how they can actually support users in practice. To address this gap, this work investigates the way users engage with hybrid process artifacts during comprehension tasks. In particular, we focus on a hybrid representation of DCR Graphs (DCR-HR) combining a process model, textual annotations and an interactive simulation. Following a qualitative approach, we conduct a multi-granular analysis exploiting process mining, eye-tracking techniques, and verbal data analysis to scrutinize the reading patterns and the strategies adopted by users when being confronted with DCR-HR. The findings of the coarse-grained analysis provide important insights about the behavior of domain experts and IT specialists and show how user’s background and task type change the use of hybrid process artifacts. As for the fine-grained analysis, user’s behavior was classified into goal-directed and exploratory and different strategies of using the interactive simulation were identified. In addition, a progressive switch from an exploratory behavior to a goal-directed behavior was observed. These insights pave the way for an improved development of hybrid process artifacts and delineate several directions for future work.

Prognosis of multiple instances in time-aware declarative business process models

Technological evolution, heading for industry 4.0, makes companies tend to automate their management and operation, ideally defining it through business process models. To describe policies or rules related to the execution order of the activities in an organization, Declarative Business Process Models permit a relaxed description of activity order, which needs monitoring to detect non-conforming behaviors. Commonly, the detection of a violation implies that the malfunction has already occurred, being better to avoid the violation in advance. To predict future violations, prognosis is required.To allow the modeling of real business behavior, an extension of declarative business process models including both time patterns and multiple instances is proposed. This new model can be used to prognosticate if current process instances may violate a defined model in the future, according to the analysis of the robustness of the process instances evolution. The proposed Model-Based Prognosis is based on analyzing the event traces that represent the current instances and propagate their possible progression through the Constraint Programming paradigm. To ascertain if the model could be violated, it is analyzed how its robustness can tackle unexpected behaviors.To complete the formalization and modeling, an implementation applied to a real medical example is included in the paper. The prognosis of concurrent instances is addressed, dealing with formalized time and activity patterns even considering the resource availability, and getting acceptable execution times.The automatic verification and prognosis of declarative business processes are addressed considering concurrency and synchronization of multiple instances, performing well in terms of execution time.

Declarative and Hybrid Process Discovery: Recent Advances and Open Challenges

Knowledge-intensive processes, such as those encountered in health care, finance and government, tend to allow a large degree of flexibility: there are many possible solutions towards a goal, and it is left to the expertise of knowledge workers to find the one most suitable for the particular case at hand. As a result, such processes usually exhibit more varied behaviour than traditional production processes. This poses a challenge for process discovery algorithms that return imperative, flow-based, models. The models tend to become highly complex when representing many alternative paths, and therefore, the miners need to either sacrifice on simplicity, fitness, or precision. It has been proposed that one should discover the constraints of the process instead, based on the assumption that such a constraint-based, declarative process model can describe highly varied behaviour more concisely. More recently, it has been observed that many processes do not neatly fall in one category or the other; instead, they contain both flexible and rigid parts. In such cases, it may be helpful to identify these parts and mine constraints for some and flow for others, resulting in a hybrid model. In this paper, we provide an overview of recent advances in both declarative and hybrid process discovery, discuss a number of open challenges that still remain, and propose directions for future research.

Automated discovery of declarative process models with correlated data conditions

Automated process discovery techniques enable users to generate business process models from event logs extracted from enterprise information systems. Traditional techniques in this field generate procedural process models (e.g., in the BPMN notation). When dealing with highly variable processes, the resulting procedural models are often too complex to be practically usable. An alternative approach is to discover declarative process models, which represent the behavior of the process as a set of constraints. Declarative process discovery techniques have been shown to produce simpler models than procedural ones, particularly for processes with high variability. However, the bulk of approaches for automated discovery of declarative process models focus on the control-flow perspective, ignoring the data perspective. This paper addresses the problem of discovering declarative process models with data conditions. Specifically, the paper tackles the problem of discovering constraints that involve two activities of the process such that each of these two activities is associated with a condition that must hold when the activity occurs. The paper presents and compares two approaches to the problem of discovering such conditions. The first approach uses clustering techniques in conjunction with a rule mining technique, while the second approach relies on redescription mining techniques. The two approaches (and their variants) are empirically compared using a combination of synthetic and real-life event logs. The experimental results show that the former approach outperforms the latter when it comes to re-discovering constraints artificially injected in a log. Also, the former approach is in most of the cases more computationally efficient. On the other hand, redescription mining discovers rules with higher confidence (and lower support) suggesting that it may be used to discover constraints that hold for smaller subsets of cases of a process.

A Framework-Driven Comparison of Automata-Based Tools for Identifying Business Rule Conflicts

Drawing on business rules for constructing business process models by a constraint-driven methodology is a distinct characteristic of declarative process modeling. Given the intricacies of business rules, there is a pragmatic need to conduct conflict-free assessments for business rules in an automatic manner. In this paper, business rules are stated in terms of restricted English by harnessing a group of predefined business rule templates. With linear temporal logic that serves as a semantic foundation for the business rule templates, a pair of business rules represented as a linear temporal logic specification is translated into an associated Büchi automaton via LTL2BA, LTL3BA and ltl2tgba. A Büchi automaton that accepts the empty language signifies that the two business rules are in conflict with each other. The suitability of the formal framework and the three automated tools is evaluated by an industry-level case study.

Full Support for Efficiently Mining Multi-Perspective Declarative Constraints from Process Logs

Declarative process management has emerged as an alternative solution for describing flexible workflows. In turn, the modelling opportunities with languages such as Declare are less intuitive and hard to implement. The area of process discovery covers the automatic discovery of process models. It has been shown that the performance of process mining algorithms, particularly when considering the multi-perspective declarative process models, are not satisfactory. State-of-the-art mining tools do not support multi-perspective declarative models at this moment. We address this open research problem by proposing an efficient mining framework that leverages the latest big data analysis technology and builds upon the distributed processing method MapReduce. The paper at hand further completes the research on multi-perspective declarative process mining by extending our previous work in various ways; in particular, we introduce algorithms and descriptions for the full set of commonly accepted types of MP-Declare constraints. Additionally, we provide a novel implementation concept allowing an easy introduction and discovery of customised constraint templates. We evaluated the mining performance and effectiveness of the presented approach on several real-life event logs. The results highlight that, with our efficient mining technique, multi-perspective declarative process models can be extracted in reasonable time.

On the relevance of a business constraint to an event log

Declarative process modeling languages such as declare describe the behavior of processes by means of constraints. Such constraints exert rules on the execution of tasks upon the execution of other tasks called activations. The constraint is thus fulfilled both if it is activated and the consequent rule is respected, or if it is not activated at all. The latter case, named vacuous satisfaction, is clearly less interesting than the former. Such a distinction becomes of utmost importance in the context of declarative process mining techniques, where processes are analyzed based on the identification of the most relevant constraints valid in an event log. Unfortunately, this notion of relevance has never been formally defined, and all the proposals existing in the literature use ad-hoc definitions that are only applicable to a pre-defined set of constraint patterns. This makes existing declarative process mining techniques inapplicable when the target constraint language is extensible, and may contain formulae that go beyond the pre-defined patterns. In this paper, we tackle this open challenge, and show how the notion of constraint activation and vacuous satisfaction can be captured semantically, in the case of constraints expressed in arbitrary temporal logics over finite traces. Our solution relies on the annotation of finite-state automata to incorporate relevance-related information. We discuss the formal grounding of our approach and describe the implementation thereof. We finally report on experimental results gathered from the application of our approach to real-life data, which show the advantages and feasibility of our solution.

Discovering hidden dependencies in constraint-based declarative process models for improving understandability

Flexible systems and services require a solid approach for modeling and enacting dynamic behavior. Declarative process models gained plenty of traction lately as they have proven to provide a good fit for the problem at hand, i.e. visualizing and executing flexible business processes. These models are based on constraints that impose behavioral restrictions on process behavior. Essentially, a declarative model is a set of constraints defined over the set of activities in a process. While allowing for very flexible process specifications, a major downside is that the combination of constraints can lead to behavioral restrictions not explicitly visible when reading a model. These restrictions, so-called hidden dependencies, make the models much more difficult to understand. This paper presents a technique for discovering hidden dependencies and making them explicit by means of dependency structures. Experiments with novice process modelers demonstrate that the proposed technique lowers the cognitive effort necessary to comprehend a constraint-based process model.

Parallel algorithms for the automated discovery of declarative process models

The aim of process discovery is to build a process model from an event log without prior information about the process. The discovery of declarative process models is useful when a process works in an unpredictable and unstable environment since several allowed paths can be represented as a compact set of rules. One of the tools available in the literature for discovering declarative models from logs is the Declare Miner, a plug-in of the process mining tool ProM. Using this plug-in, the discovered models are represented using Declare, a declarative process modeling language based on ltl for finite traces. However, the high execution times of the Declare Miner when processing large sets of data hampers the applicability of the tool to real-life settings. Therefore, in this paper, we propose a new approach for the discovery of Declare models based on the combination of an Apriori algorithm and a group of algorithms for Sequence Analysis to enhance the time performance of the plug-in. The approach has been developed in a way that it is easy to be parallelized using two different partitioning methods: the search space partitioning, in which different groups of candidate constraints are processed in parallel, and the database partitioning, in which different chunks of the log are processed at the same time. The approach has been implemented in ProM in its sequential version and in two multi-threading implementations leveraging these two partitioning methods. All the new variants of the plug-in have been evaluated using a large set of synthetic and real-life event logs.

Replication, refinement & reachability: complexity in dynamic condition-response graphs

We explore the complexity of reachability and run-time refinement under safety and liveness constraints in event-based process models. Our study is framed in the DCR $$^\star $$ process language, which supports modular specification through a compositional operational semantics. DCR $$^\star $$ encompasses the “Dynamic Condition Response (DCR) graphs” declarative process model for analysis, execution and safe run-time refinement of process-aware information systems; including replication of sub-processes. We prove that event-reachability and refinement are np-hard for DCR $$^\star $$ processes without replication, and that these finite state processes recognise exactly the languages that are the union of a regular and an $$\omega $$ -regular language. Moreover, we prove that event-reachability and refinement are undecidable in general for DCR $$^\star $$ processes with replication and local events, and we provide a tractable approximation for refinement. A prototype implementation of the DCR $$^\star $$ language is available at http://dcr.tools/acta16 .

Detecting violation of business constraints in declarative process execution: a case study

PurposeDeclarative process modelling is a constraint-centric approach that treats business rules as first-class citizens in business process models. Augmenting the declarative process modelling technique with capability to detect the constraint violations during business process execution is of crucial importance. The purpose of this paper is to contribute to the modelling of business rules through a repository of pattern-oriented templates.Design/methodology/approachThe semantics of the business rule templates is underpinned by linear temporal logic (LTL). Automated temporal reasoning is then conducted for determining whether process executions adhere to the business rules through the utilisation of the Logics Workbench (LWB). An application of the methodological framework is illustrated by a realistic case study on degree requirements verification.FindingsTo access the practicality of the approach, the case study of this paper is based on the verification of degree requirements, which is different from the domain area of the case study in the author’s prior work. The findings indicated that the temporal framework could be applied to the declarative process modelling in a consistent and efficient manner.Originality/valueThis paper is an extended version of the author’s earlier study. More details on the LTL and LWB are provided in the current study. The author introduces 17 new business rule templates and illustrates the utilisation of the new templates via a case study that belongs to a different domain area.

Towards a decision-aware declarative process modeling language for knowledge-intensive processes

Abstract Modeling loosely framed and knowledge-intensive business processes with the currently available process modeling languages is very challenging. Some lack the flexibility to model this type of processes, while others are missing one or more perspectives needed to add the necessary level of detail to the models. In this paper we have composed a list of requirements that a modeling language should fulfil in order to adequately support the modeling of this type of processes. Based on these requirements, a metamodel for a new modeling language was developed that satisfies them all. The new language, called DeciClare, incorporates parts of several existing modeling languages, integrating them with new solutions to requirements that had not yet been met. Deciclare is a declarative modeling language at its core, and therefore, can inherently deal with the flexibility required to model loosely framed processes. The complementary resource and data perspectives add the capability to reason about, respectively, resources and data values. The latter makes it possible to encapsulate the knowledge that governs the process flow by offering support for decision modeling. The abstract syntax of DeciClare has been implemented in the form of an Ecore model. Based on this implementation, the language-domain appropriateness of the language was validated by domain experts using the arm fracture case as application scenario.