Lars Ackermann

Simulation of Multi-Perspective Declarative Process Models

Flexible business processes can often be represented more easily using a declarative process modeling language (DPML) rather than an imperative language. Process mining techniques can be used to automate the discovery of process models. One way to evaluate process mining techniques is to synthesize event logs from a source model via simulation techniques and to compare the discovered model with the source model. Though there are several declarative process mining techniques, there is a lack of simulation approaches. Process models also involve multiple aspects, like the flow of activities and resource assignment constraints. The simulation approach at hand automatically synthesizes event logs that conform to a given model specified in the multi-perspective, declarative language DPIL. Our technique translates DPIL constraints to a logic language called Alloy. A formula-analysis step is the actual log generation. We evaluate our technique with a concise example and describe an alternative configuration to simulate event logs based on an assumed partial execution as well as on properties that are intended to be checked. We complement the quality evaluation by a performance analysis.

Towards an Implementation of Data and Resource Patterns in Constraint-based Process Models.

A Process-Aware Information System (PAIS) is a system that executes processes involving people, applications, and data on the basis of process models. Two representations for processes can be distinguished: procedural models prescribe exactly the execution order of process steps. Declarative process models allow flexible process executions that are restricted by constraints. Especially in application areas of knowledge driven processes, this flexibility is required. Foundations of declarative approaches have been extensively discussed in research. From a practitioner’s point of view, however, an open question still remains: is it possible to implement established functionality in contemporary declarative PAIS, especially data and resource handling? In this paper, we tackle this open research question by introducing the declarative process modelling and execution framework DPIL that covers resource and data modelling. Expressiveness and functionality of the framework are evaluated by means of the well-known Workflow Data and Resource Patterns.

Towards Simulation-and Mining-based Translation of Process Models

Process modeling is usually done using imperative modeling languages like BPMN or EPCs. In order to cope with the complexity of human-centric and flexible business processes several declarative process modeling languages (DPMLs) have been developed during the last years. DPMLs allow for the specification of constraints that restrict execution flows. They differ widely in terms of their level of expressiveness and tool support. Furthermore, research has shown that the understandability of declarative process models is rather low. Since there are applications for both classes of process modeling languages, there arises a need for an automatic translation of process models from one language into another. Our approach is based upon well-established methodologies in process management for process model simulation and process mining without requiring the specification of model transformation rules. In this paper, we present the technique in principle and evaluate it by transforming process models between two exemplary process modeling languages.

Towards Simulation- and Mining-based Translation of Resource-aware Process Models

Imperative languages like BPMN are eminently suitable for representing routine processes and are likewise cumbersome in case of flexible processes. The latter are easier to describe using declarative process modeling languages (DPMLs). However, understandability and tool support of DPMLs are comparatively poor. Additionally, there may be an affinity to a particular language caused by existing company infrastructure or individual preferences. Hence, a technique for automatically translating process models between different languages is required. Process models usually describe several aspects of a process, such as activity orderings and role assignments. Therefore, our approach focuses on translating resource-aware process models. We utilize well-established techniques for process simulation and mining to avoid the definition of cumbersome model transformation rules. Our implementation is based on a discussion of general configuration principles and a concrete configuration suggestion. The whole translation approach is discussed and evaluated at the example of BPMN and DPIL.

model[NL]generation: natural language model extraction

In this paper, we describe a novel approach of extracting models from natural language text sources. This requires linguistic analysis as well as techniques for interpreting and using the analysis results. Our linguistic analysis engine provides feature analysis for a rule-based model element detection. Furthermore, the presented approach enables users to generate domain- and application-specific model element detection rules based on natural language sample sentences. Detection rules also have to be connected to instantiation rules for the respective type of model element. This is done through a highly system-supported mapping step where users are able to choose elements from arbitrary meta models and to connect their properties with functions over natural language sentence parts. As both, the definition and application of detection rules is always a sensitive balancing act between precision and recall, these steps are highly interactive. That is why our current prototype also supports detection rule adaption and iterative rule set completion -- always to the level of current need.

Internet of Things meets BPM : A Conceptual Integration Framework

Business process management (BPM) is considered as a powerful technology to design, control, and improve processes. Recently, organizations have started contemplating the value that combining the inter-networking of all kinds of physical devices, i.e., the Internet of Things (IoT) with BPM could bring to an organization. BPM provides intelligent control over IoT devices by integrating and managing devices and data generated by them in business operations. Here, data from IoT devices needs to be analyzed and actions need to be taken based on that data. Since the real world as context of a BPM application changes drastically through the advent of IoT, it is worthwhile to investigate how the enactment of a BPM application changes or must be customized. In this paper, we first describe benefits and necessary adaptions w.r.t. the integration of IoT and BPM systems. Furthermore, we tackle two concrete adaption tasks, i.e., we introduce concepts for IoT enhanced process modeling as well as a technological integration architecture. Both approaches have successfully been evaluated in production industry.

Deep Learning Process Prediction with Discrete and Continuous Data Features

Process prediction is a well known method to support participants in performing business processes. These methods use event logs of executed cases as a knowledge base to make predictions for running instances. A range of such techniques have been proposed for different tasks, e.g., for predicting the next activity or the remaining time of a running instance. Neural networks with Long Short-Term Memory architectures have turned out to be highly customizable and precise in predicting the next activity in a running case. Current research, however, focuses on the prediction of future activities using activity labels and resource information while further event log information, in particular discrete and continuous event data is neglected. In this paper, we show how prediction accuracy can significantly be improved by incorporating event data attributes. We regard this extension of conventional algorithms as a substantial contribution to the field of activity prediction. The new approach has been validated with a recent real-life event log.

Execution of Multi-perspective Declarative Process Models

A Process-Aware Information System is a system that executes processes involving people, applications, and data on the basis of process models. At least two process modeling paradigms can be distinguished: procedural models define exactly the execution order of process steps. Declarative process models allow flexible process executions that are restricted by constraints. Execution engines for declarative process models have been extensively investigated in research with a strong focus on behavioral aspects. However, execution approaches for multi-perspective declarative models that involve constraints on data values and resource assignments are still not existing. In this paper, we present an approach for the execution of multi-perspective declarative process models in order to close this gap. The approach builds on a classification strategy for different constraint types evaluating their relevance in different execution contexts. For execution, all constraints are transformed into the execution language Alloy that is used to solve satisfiability (SAT) problems. We implemented a modeling tool including the transformation functionality and the process execution engine itself. The approach has been evaluated in terms of expressiveness and efficiency.

An Integrated Architecture for IoT-Aware Business Process Execution.

Business processes are frequently executed within application systems that involve humans, computer systems as well as objects of the Internet of Things (IoT). While several works are emerging on combining BPM and the IoT, the exploitation of IoT technology for system supported process execution is still constrained by the absence of a common system architecture that manages the communication between both worlds. In this paper, we introduce an integrated approach for IoT-aware business process execution that exploits IoT for BPM by providing IoT data in a process-aware way, providing an IoT data provenance framework, considering IoT data for interaction in a pre-defined process model, and providing wearable user interfaces with context specific IoT data provision. The approach has been implemented and evaluated extensively in production industry. The results show that the application of IoT enhanced BPM leads to less machine stops.

Natural Language Generation for Declarative Process Models

Two different types of processes can be distinguished: well-structured routine processes and agile processes where the control-flow cannot be predefined a priori. In a similar way, two modeling paradigms exist whereby procedural models are more adequate for routine processes and declarative models are more suitable for agile processes. Often business analysts are not confident in understanding process models; this holds even more for declarative process models. Natural language support for this kind of processes in order to improve their readability is desirable. In the work at hand we define a technique that transforms declarative models to intuitive natural language texts. Hereof, the approach focuses on content determination and structuring the output texts.