Stefan Schönig

Mining the Organisational Perspective in Agile Business Processes

Agile processes depend on human resources, decisions and expert knowledge, and they are especially versatile and comprise rather complex scenarios. Declarative, i.e., rule-based, process models are well-suited for modelling these processes. Although there are several mining techniques to discover such declarative process models from event logs, they put less emphasis on the organisational perspective, which specifies how resources are involved in the activities. As a consequence, the resulting models do not specify who should execute which task and with which constraint (like separation of duties) in mind. In this paper, we propose a process mining approach to discover resource-aware, declarative process models. Our specific contribution is the extraction of complex rules for resource assignment that integrate the control-flow and organisational perspectives. Our experiments demonstrate the expressiveness of the mined rules with a reference to the Workflow Resource Patterns and a real-world use case.

A framework for efficiently mining the organisational perspective of business processes

Process mining aims at discovering processes by extracting knowledge from event logs. Such knowledge may refer to different business process perspectives. The organisational perspective deals, among other things, with the assignment of human resources to process activities. Information about the resources that are involved in process activities can be mined from event logs in order to discover resource assignment conditions, which is valuable for process analysis and redesign. Prior process mining approaches in this context present one of the following issues: (i) they are limited to discovering a restricted set of resource assignment conditions; (ii) they do not aim at providing efficient solutions; or (iii) the discovered process models are difficult to read due to the number of assignment conditions included.In this paper we address these problems and develop an efficient and effective process mining framework that provides extensive support for the discovery of patterns related to resource assignment. The framework is validated in terms of performance and applicability. A process mining approach for the organisational perspective is proposed.It supports the discovery of resource assignment patterns and how involvement of resources influences the control-flow.The framework consists of an event log pre-processing phase to increase efficiency.A model post-processing phase improves effectiveness by removing redundant rules.

Efficient and Customisable Declarative Process Mining with SQL

Flexible business processes can often be modelled more easily using a declarative rather than a procedural modelling approach. Process mining aims at automating the discovery of business process models. Existing declarative process mining approaches either suffer from performance issues with real-life event logs or limit their expressiveness to a specific set of constaint types. Lately, RelationalXES, a relational database architecture for storing event log data, has been introduced. In this paper, we introduce a mining approach that directly works on relational event data by querying the log with conventional SQL. By leveraging database performance technology, the mining procedure is fast without limiting itself to detecting certain control-flow constraints. Queries can be customised and cover process perspectives beyond control flow, e.g., organisational aspects. We evaluated the performance and the capabilities of our approach with regard to several real-life event logs.

Discovery of Multi-perspective Declarative Process Models

Process discovery is one of the main branches of process mining that allows the user to build a process model representing the process behavior as recorded in the logs. Standard process discovery techniques produce as output a procedural process model (e.g., a Petri net). Recently, several approaches have been developed to derive declarative process models from logs and have been proven to be more suitable to analyze processes working in environments that are less stable and predictable. However, a large part of these techniques are focused on the analysis of the control flow perspective of a business process. Therefore, one of the challenges still open in this field is the development of techniques for the analysis of business processes also from other perspectives, like data, time, and resources. In this paper, we present a full-fledged approach for the discovery of multi-perspective declarative process models from event logs that allows the user to discover declarative models taking into consideration all the information an event log can provide. The approach has been implemented and experimented in real-life case studies.

Dynamic guidance enhancement in workflow management systems

Todays workflow management systems have become increasingly powerful. Some prototypic approaches even tend to not patronise the users by providing a set of process steps to follow, but let them decide which step to choose next. The idea behind this approach is the impossibility to model every special case of a workflow, because a fixed process order would necessarily be inefficient or even incorrect in some cases. By admitting this freedom, the risk of confounding the users is taken. That is why we provide a qualified guidance instance through the process.

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.

Resource-Aware Process Model Similarity Matching

As business process models are widely used and essential for most organizations, the problem of redundantly modeled processes rises. This can happen when a process is modeled by different modelers or when organizations merge. In order to cope with this issue, typically process model similarity matching methods are used. Thereby, pure textual matching algorithms operating on single activities are often not suitable. One alternative is to include further information like data and resources and to check for M:N-matchings. The work at hand describes how to use resource information to match process models, even if they are modeled on different levels of granularity. The approach can be used for both human and non-human resources. Furthermore, the differences between intra- and inter-organizational matchings are pointed out.

Towards Multi-perspective Process Model Similarity Matching

Organizations increasingly determine process models to support documentation and redesign of workflows. In various situations correspondences between activities of different process models have to be found. The challenge is to find a similarity measure to identify similar activities in different process models. Current matching techniques predominantly consider lexical matching based on a comparison of activity labels and 1-to-1-matchings. However, label based matching probably fails, e.g., when modellers use different vocabulary or model activities at different levels of granularity. That is why we extend existing methods to compute candidate sets for N-to-M-matchings based on power-sets of nodes. Therefore, we impose higher demands on process models as we do not only consider labels, but also involved actors, data objects and the order of appearing. This information is used to identify similarities in process models that use different vocabulary and are modelled at different levels of granularity.

Digital Connected Production: Wearable Manufacturing Information Systems

A manufacturing information system is targeted for use anywhere production is taking place. Modern manufacturing information systems are generally computerized and are designed to collect and present the data which production operators need in order to plan and direct operations within the production. The application of mobile and wearable devices can support operators’ tasks without distracting them from their core duties. In this paper, we present an approach towards a wearable manufacturing information system that is able to implement decentralized production monitoring and control and supports users in their core tasks. Building upon acquired and digitally stored production data, these devices provide different user-specific information and services when required. A practical example from corrugation industry highlights advantages of mobile devices compared to conventional centralized systems in the field of manufacturing.

Comparing Declarative Process Modelling Languages from the Organisational Perspective

The spectrum of business processes can be divided into two types: well-structured routine processes and agile processes with control flow that evolves at run time. In a similar way, two different representational paradigms can be distinguished: procedural models and declarative models which define rules that a process has to satisfy. Agile processes can often be captured more easily using a declarative approach. While in procedural languages the organisational perspective can be modelled adequatly, in declarative languages, however, an adequate representation of organisational patterns is often still not possible. Agile processes, however, need to explicitly integrate organisational coherencies due to the importance of human decision-making. This paper presents a review of declarative modeling languages, outlines missing aspects and suggests research roadmaps for the future.