We investigate declarative process specifications over event data containing both discrete events and continuous data streams (e.g., from IoT sensors). For example, it might be desirable to check LTL-like properties not only over event traces but also over sensor data streams, e.g., a CO2 value not staying over a certain threshold for some period. As existing LTL approaches are based on discrete state systems, they are not suitable for this hybrid/continuous setting. Therefore, we propose using Signal Time Logic (STL) for verifying temporal properties over a mix of discrete and continuous behaviors. We show how important topics from conformance checking can be expressed in terms of results from signaling and thus be used to check temporal constraints over discrete/continuous systems. In this context, we also introduce a catalogue of Declare templates over STL, as counterpart to the traditional Declare but for continuous settings.

Already for several decades, organizations strive to better understand, analyze, improve, and automate their business processes.Notably, recent advancements in the area of Business Process Technology, such as process mining and robotic process automation (RPA), have equipped organizations with entirely new means to achieve this goal.Process mining allows organizations to exploit transactional data recorded by information systems to improve business processes with respect to performance dimensions such as efficiency, quality, and compliance.RPA provides the means to automate repetitive and routine-like work by employing so-called software robots.Furthermore, by combining developments from areas such as machine learning and predictive analytics on the one hand with Business Process Technology on the other, process weaknesses, such as bottlenecks, cannot only be automatically identified, but also remedied by taking preventative actions.Given the large interest in this topic in both academia and practice, the goal of this minitrack is to promote scientific exchanges on Business Process Technology.As such, the minitrack enables researchers to present and discuss innovative approaches, techniques, methodologies, and models to design, adopt, implement, operate, evaluate, and govern technology in the context of business processes.It is the first time we are organizing this minitrack.Nonetheless, we received six valuable research paper submissions, of which we accepted three papers after the reviewing process.Two out of the three accepted papers deal with the topic of process automation.From our perspective this is not surprising, since automation is on the agenda of almost every organization these days.These two papers address particularly interesting issues in this context.Specifically, the paper by Koch et al. considers which factors actually impede automation efforts in organizations where stakeholders are afraid of losing control.The paper from Kedziora and Smolander explores how RPA can help to respond to disasters, such as the Covid-19 pandemic.Going beyond automation, the third paper in the minitrack takes a look at how process mining can be enabled in a blockchain context through a systematic review of existing works concerned with the logging of event data in environments supported by blockchains.Below, we provide an overview of the accepted papers, their authors, and the corresponding abstracts.

We investigate a new form of (privacy-preserving) inconsistency measurement for multi-party communication. Intuitively, for two knowledge bases K_A,K_B (of two agents A, B), our results allow to quantitatively assess the degree of inconsistency for K_A ∪ K_B without having to reveal the actual contents of the knowledge bases. Using secure multi-party computation (SMPC) and cryptographic protocols, we develop two concrete methods for this use-case and show that they satisfy important properties of SMPC protocols—notably, input privacy, i.e., jointly computing the inconsistency degree without revealing the inputs.

Detecting undesired process behavior is one of the main tasks of process mining and various conformance-checking techniques have been developed to this end. These techniques typically require a normative process model as input, specifically designed for the processes to be analyzed. Such models are rarely available, though, and their creation involves considerable manual effort.However, reference process models serve as best-practice templates for organizational processes in a plethora of domains, containing valuable knowledge about general behavioral relations in well-engineered processes. These general models can thus mitigate the need for dedicated models by providing a basis to check for undesired behavior. Still, finding a perfectly matching reference model for a real-life event log is unrealistic because organizational needs can vary, despite similarities in process execution. Furthermore, event logs may encompass behavior related to different reference models, making traditional conformance checking impractical as it requires aligning process executions to individual models. To still use reference models for conformance checking, we propose a framework for mining declarative best-practice constraints from a reference model collection, automatically selecting constraints that are relevant for a given event log, and checking for best-practice violations. We demonstrate the capability of our framework to detect best-practice violations through an evaluation based on real-world process model collections and event logs.

We investigate the use of Answer Set Programming (ASP) for the problem of conformance checking of LTL-based Declarative Process Specifications. In particular, we propose ASP solutions that are independent of automata. That is: in related works, the semantics of the declarative process specifications are often captured by means of finite state automata. This means that for conformance checking, the constraints of the specification first have to be transformed into a corresponding automata representation, which introduces a computational burden. In this work, we present a new ASP-based approach which encodes the constraint semantics directly and therefore can be used to check conformance without the need of performing automata operations. We implement our approach and perform experiments with real-life datasets, comparing our approach to a selection of state-of-the-art approaches. Our experiments show that our approach can outperform existing approaches in some cases. Furthermore, our approach can easily be extended to check whether the considered constraint sets are satisfiable (i.e., consistent).

(Business) Rule Bases are knowledge representation formalisms with a distinction between rules and facts. In this work, we address the issue of analyzing potential issues in rule bases, i. e., sets of rules which cause an inconsistency only if they are activated together. The problem of dealing with potential issues is very common in industrial domains of knowledge representation and reasoning, in particular in business rules management. Here, it is often not clear during modelling which rules will be activated together. We introduce a formalization of the above-mentioned problem and present means for the prioritization of such issues. Also, we investigate central aspects related to computational complexity and present an ad-hoc (online) approach for computing the set of the most severe potential issues. We implement and evaluate our approach with real-life datasets.

While the Decision Model and Notation standard (DMN) is considered to be an increasingly popular standard, there is a broad consensus that human modelling errors can easily occur in the creation of DMN models. Yet, while this consensus is clear, there is only limited evidence of which error types exactly may occur in practice. In this work, we therefore present some empirical evidence on DMN errors in the wild. Specifically, we analyze the SAP-SAM dataset by SAP Signavio, containing over 500 000 real-world conceptual models. Our results show that modelling errors, such as missing rules, occur frequently in real-life settings (36.1% of all models contained some form of issue). Furthermore, we analyze the distribution of which error types have occurred (relative to an existing classification of DMN error types from a previous work). To the best of our knowledge, this is the largest DMN study conducted to date (N = 5 668 DMN models).

Declarative process specifications, such as Declare, provide a natural framework to capture flexible business processes. However, the specification may be more, or less flexible, depending on how much freedom it provides – for example, a specification that is so strict it allows only one exact behavior (i.e., trace) can be seen as inflexible. Surprisingly, little attention has been given to this key feature in declarative process specifications, and how to measure it. In this paper, we therefore close this gap by investigating how to measure the degree of flexibility of declarative process specifications. We show how techniques for measuring the density of infinite regular languages can be effectively employed to define a measure of flexibility for constraint-based process specifications, focusing in particular on Declare. Also, we show when our measure (which is a limit) is guaranteed to exist for Declare specifications, and present how to actually compute our measure using known techniques.

We investigate means for the explainability of Decision Model and Notation (DMN) models. These are especially relevant for industrial settings, where the scale and complexity of DMN models can otherwise quickly make it unfeasible for companies to understand and maintain their decision logic. To this aim, we present a formal approach for measuring the impact of decision inputs on the decision output. In particular, we show how the decision logic of a DMN model can be transformed into a coalitional game based on (Datalog) queries over the decision tables, which allows one to apply the game-theoretic underpinning of Shapley values for measuring impact. Intuitively, the inputs of the decision act as the players of a coalitional game, and the payoff is the impact of an input/player on the decision output. The motivation of this work stems from real-life settings where means for understanding decision models are crucial, e.g., models of industrial complexity and domains such as fraud management. We implement our approach and evaluate it with real-life DMN models from the SAP-SAM dataset.

We investigate the problem of measuring inconsistency in linear temporal logic on finite traces (LTLf). In particular, we present Answer Set Programming-based approaches to compute a selection of traditional inconsistency measures w.r.t. LTLf knowledge bases. In contrast to existing works (mostly on propositional logic), these approaches are novel in the sense that they allow to assess logical inconsistency in presence of temporal operators, as offered by LTLf. In an experimental evaluation on real-world data from the area of business process management, we show that our approaches are practically feasible.

Modelling processes with declarative process models, i.e. sets of constraints, allows for a great degree of flexibility in process execution. However, having behavior specified by means of symbolic (textual) constraints comes along with the problem that it is often hard for humans to understand which exact behavior is allowed, and which is not (think for example of checking relationships between constraints). This becomes especially problematic when modellers need to carry out changes to a model. For example, a modeller must make sure that any alteration to a model does not introduce any unwanted or non-compliant behavior. As this is often difficult for humans, editing declarative process models currently bears the risk of (accidentally) inducing unforeseen compliance breaches due to some overlooked changes in behavior. In this work, we therefore present an approach to efficiently compute the behavioral changes between a declarative process model M and a corresponding (edited) model $$M'$$ . This supports modellers in understanding the behavioral changes induced by an alteration to the constraints. We implement our approach and show that behavioral changes can be computed within milliseconds even for real-life data-sets.

We present a framework of answer set programming-based solutions for various problems related to declarative process specifications. Specifically, the framework offers implementations for conformance checking, satisfiability checking, and two different inconsistency measures. Since the aforementioned problems are represented in a fragment of linear temporal logic, the framework could also prove useful for a broader range of applications beyond process specifications.

Regardless of the domain, forecasting the future behaviour of a running process instance is a question of interest for decision makers, especially when multiple instances interact. Fostered by the recent advances in machine learning research, several methods have been proposed to predict the next activity, outcome or remaining time of a process automatically. Still, building a model with high predictive power requires both - intrinsic knowledge of how to extract meaningful features from the event log data and a model that captures complex patterns in data. This work builds upon the recent progress in inter-case Predictive Process Monitoring (PPM) and comprehensively benchmarks the impact of inter-case features on prediction accuracy. Moreover, it includes quantum machine learning models, which are expected to provide an advantage over classical models with a scaling amount of feature dimensions. The evaluation on real-world training data from the BPI challenge shows that the inter-case features provide a significant boost by more than four percent in accuracy and quantum algorithms are indeed competitive in a handful of feature configurations. Yet, as quantum hardware is still in its early stages of development, this paper critically discusses these findings in the light of runtime, noise and the risk to overfit on the training data. Finally, the implementation of an open-source plugin demonstrates the technical feasibility to connect a state-of-the-art workflow engine such as Camunda to an IBM quantum computing cloud service.

Business rules are currently evolving into a central artifact for enabling Business Process Management and Compliance Management.While standards for rule modeling such as Decision Model and Notation (DMN) are more and more maturing, the actual management of business rules seems strongly neglected in research.This track therefore welcomes submissions that address business rules management (BRM) in organizations

In the scope of predictive process monitoring (PPM), there exist many tools and techniques for monitoring individual running instances. However, in real-life settings, companies might have to monitor multiple, maybe thousands of instances simultaneously. Here, companies need to be presented with an overview of monitoring results in an aggregated form (i.e., over ALL instances). In this paper, we present a web-based dashboard tool that supports companies with exactly this form of aggregated insights - also referred to as simultaneous PPM. Our tool allows users to easily create metrics, visualizations and dashboards for aggregated predictive insights, and is integrated into Camunda. The tool was conceptually developed in interviews with industrial partners and has been evaluated in an initial user-study.

We address the problem of measuring inconsistency in declarative process specifications, with an emphasis on linear temporal logic on fixed traces (LTLff). As we will show, existing inconsistency measures for classical logic cannot provide a meaningful assessment of inconsistency in LTL in general, as they cannot adequately handle the temporal operators. We therefore propose a novel paraconsistent semantics as a framework for inconsistency measurement. We then present two new inconsistency measures based on these semantics and show that they satisfy important desirable properties. We show how these measures can be applied to declarative process models and investigate the computational complexity of the introduced approach.