Process-aware Information Systems Research Articles

Exploring user editing preferences in electronic forms: an empirical study

Electronic forms, such as order entry or tax declarations, frequently serve as the primary point of contact between users and information systems. Given their significance, it’s crucial that these forms are intuitive and not burdensome for users to complete. One key aspect influencing the intuitiveness of forms is the sequence in which individual fields must be filled. This article reports on an empirical study involving 162 participants, which explored the intuitiveness of user forms across diverse scenarios. The study had two main objectives. Firstly, it sought to understand how users perceive different sequences of form fields in terms of intuitiveness. Secondly, it investigated the possibility of an intuitive sequence for form fields. The study found significant differences in intuitiveness among various ways of organizing form field sequences. Furthermore, it revealed a common understanding among users about the (sub-)sequence in which form fields should be arranged, notably that fields requiring file uploads should be located at the end of the form. The findings of this study provide valuable insights for developing more intuitive user forms in process-aware information systems, thereby enhancing the overall user-friendliness of such systems.

Transformer models for mining intents and predicting activities from emails in knowledge-intensive processes

Process mining is an interdisciplinary field that combines Artificial Intelligence and Business Process Management to extract insights from historical event data. Knowledge-intensive processes, which predominantly involve knowledge work, are often inadequately monitored by process-aware information systems. Consequently, the event data necessary for applying process mining techniques are frequently unavailable. Emails are widely used in knowledge-intensive processes for scheduling meetings, sharing documents, and reporting on the completion of outstanding tasks, which makes them suitable candidates for replacing event logs as the primary data source. In this work, we focus on the task of extracting the set of next recommended activities from incoming emails. Yet, we face two major challenges. Firstly, emails do not express process information explicitly but rather contain subtext that implies what the next best actions would be. Secondly, email data lacks domain-specific labels that would enable the use of machine learning. We overcome these limitations by utilizing an email taxonomy to represent user intents, thus bridging the gap between textual information and process semantics, as well as leveraging pre-trained transformer models applied in zero-shot and few-shot settings that require little to no labeled email data. An evaluation of our method on real-world unlabeled email communications demonstrates its effectiveness in recognizing intents and extracting activities.

GRASPED: A GRU-AE Network Based Multi-Perspective Business Process Anomaly Detection Model

In process-aware information systems (PAISs), anomalies are ubiquitous, having a number of different underlying causes, such as software malfunctions or operator errors. The presence of anomalies not only has an enormous impact on the economic well-being of the business, but also interferes with our ability to mine useful information from event logs. In this paper, we propose GRASPED, a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GR</b> U- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> E Network based multi-per <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> pective business <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b> ROC <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> ss anomaly <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> etection model. GRASPED can detect anomalies not only from the control flow but also from the data perspective of a business process. GRASPED is based on an autoencoder (AE) with gated recurrent units (GRU) which is trained in an unsupervised fashion (i.e., does not require any labeling of the data). In addition, GRASPED introduces the teacher forcing method as well as the attention mechanism to improve its detection performance. GRASPED does not require training on a clean log (i.e., it can be trained and perform anomaly detection directly on logs containing anomalies). We conduct extensive experiments on synthetic logs as well as real-life logs. The experiment results show that GRASPED outperforms the state-of-the-art methods for both trace-level and attribute-level anomaly detection.

Process Discovery Techniques Recommendation Framework

In a competitive environment, organizations need to continuously understand, analyze and improve the behavior of processes to maintain their position in the market. Process mining is a set of techniques that allows organizations to have an X-ray view of their processes by extracting process related knowledge from the information recorded in today’s process aware information systems such as ‘Enterprise Resource Planning’ systems, ‘Business Process Management’ systems, ‘Supply Chain Management’ systems, etc. One of the major categories of process mining techniques is the process of discovery. This later allows for automatically constructing process models just from the information stored in the system representing the real behavior of the process discovered. Many process discovery algorithms have been proposed today which made users and businesses, in front of many techniques, unable to choose or decide the appropriate mining algorithm for their business processes. Moreover, existing evaluation and recommendation frameworks have several important drawbacks. This paper proposes a new framework for recommending the most suitable process discovery technique to a given process taking into consideration the limitations of existing frameworks.

AI-augmented Business Process Management Systems: A Research Manifesto

AI-augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems, empowered by trustworthy AI technology. An ABPMS enhances the execution of business processes with the aim of making these processes more adaptable, proactive, explainable, and context-sensitive. This manifesto presents a vision for ABPMSs and discusses research challenges that need to be surmounted to realize this vision. To this end, we define the concept of ABPMS, we outline the lifecycle of processes within an ABPMS, we discuss core characteristics of an ABPMS, and we derive a set of challenges to realize systems with these characteristics.

Process mining approach to performance analysis and bottleneck finding in electronic processes (case study: the billing process of hospital services)

Today, organisations are getting bigger and bigger. This creates a huge amount of event data. Using process-aware information systems such as enterprise resource planning, it is possible to record activities in the form of event logs. Organisations can use process mining to extract information from event logs and analyse business processes. Process mining is very important in terms of reducing costs, improving processes, and in particular reducing process time. This study focuses on practical features of process mining, namely process model discovery, performance analysis, and bottleneck finding. The case study is the billing process for healthcare services provided by a local hospital. The dataset was analysed using Disco. The results show that the process includes five main activities that had the most frequency. In addition, registering a new application to check the type of insurance is a bottleneck. Redesigning this pathway can have a significant impact on process efficiency.

Model-based decision support for knowledge-intensive processes

Process-aware information systems guide participants through the execution of processes. However, existing systems have limited support for knowledge-intensive processes, which are multi-variant and shaped by informed decisions of knowledge workers. Yet, making such decisions causes high cognitive load, as the effect of the decision on the future process execution must be considered. This may cause errors and/or slow down the process execution. We present an approach based on fragment-based Case Management. It supports the iterative decision making by (i) enabling knowledge workers to define goals and (ii) by giving recommendations on which decision outcomes align with the goals and which do not. For that, we use information from the process model and the running process instance. We show the technical feasibility with a proof-of-concept implementation and the value for knowledge workers in a preliminary user study.

Self-Service Registry Log Builder: A Case Study in National Trauma Registry of Iran.

Although the process-mining algorithms have evolved in the past decade, the lack of attention to extracting event logs from raw data of databases in an automatic manner is evident. These logs are available in a process-oriented manner in the process-aware information systems. Still, there are areas where their extraction is a challenge to address (e.g., trauma registries). The registry data are recorded manually and follow an unstructured ad hoc pattern; prone to high noises and errors; consequently, registry logs are classified at a maturity level of one, and extracting process-centric information is not a trivial task therein. The experiences made during the event log building from the trauma registry are the subjects to be studied. The result indicates that the three-phase self-service registry log builder tool can withstand the mentioned issues by filtering and enriching the raw data and making them ready for any level of process-mining analysis. This proposed tool is demonstrated through process discovery in the National Trauma Registry of Iran, and the encountered challenges and limitations are reported. This tool is an interactive visual event log builder for trauma registry data and is freely available for studies involving other registries. In conclusion, future research directions derived from this case study are suggested.

Model-Driven Engineering of Process-Aware Information Systems

Enterprise information systems created with model-driven software engineering methods need to handle not only data but also business processes in an automated way. This paper shows how to engineer process-aware information systems following the model-driven and generative software engineering paradigms. Existing approaches realize either the generation of automated or manual activities but do not employ model-driven engineering of all system aspects through systematic language composition. A generative approach that additionally uses process modeling languages allows developers to evolve generated data-centric information systems into process-aware information systems. To be usable within our generation process, we have developed a textual BPMN version and a corresponding language tooling to check the soundness of the models. We have included these process models into the generation process of an information system together with other domain-specific modeling languages, e.g., for data structures, and generate an extendable, process-aware information system that is open for continuous regeneration and hand-written additions. This approach allows us to lift a generated data-centric information system to a process-aware information system. Agile development enabled through the opportunity to validate assumptions automatically and adapt changes efficiently, enhances the engineering process as well as the generated systems themselves.

Shedding light on blind spots – Developing a reference architecture to leverage video data for process mining

Process mining is one of the most active research streams in business process management. In recent years, numerous methods have been proposed for analyzing structured process data. In many cases, however, only the digitized parts of processes are directly captured by process-aware information systems, whereas manual activities often leave blind spots in the process analysis. While video data can contain valuable process-related information that is not captured in information systems, a standardized approach to extracting event logs from unstructured video data remains lacking. To solve this problem and facilitate the systematic usage of video data in process mining, we have designed the ViProMiRA, a reference architecture that bridges the gap between computer vision and process mining. The various evaluation activities in our design science research process ensure that the proposed ViProMiRA allows flexible, use case-driven, and context-specific instantiations. Our results also show that a prototypical implementation of the ViProMiRA is capable of automatically extracting more than 70% of the process-relevant events from a real-world video dataset in a supervised learning scenario.

Process Aware Host-based Intrusion Detection Model

Nowadays, many organizations use Process Aware Information Systems (PAISs) to automate their business process. As any other information systems, security plays a major role in PAIS to provide a secure state and maintain the system in it. In order to provide security in a PAIS, a Process Aware Host-based Intrusion Detection (PAHID) model is proposed in this paper. The model detects host-based intrusions in a PAIS using process mining techniques.The proposed model uses both anomaly detection and misuse detection techniques for more efficiency, and organizational perspective of process mining is considered (rather than control-flow perspective) to detect more attack types. The model is automated and can deal with large logs and is suitable for flexible application domains. The PAHID model is implemented by the use of ProM framework and Java programming. It is evaluated by using a simulated log based on a real-world organization information system. Results demonstrate that the model provides high accuracy and low false positive rate.

Integrating process management and event processing in smart factories: A systems architecture and use cases

The developments of new concepts for an increased digitization of manufacturing industries in the context of Industry 4.0 have brought about novel system architectures and frameworks for smart production systems. These range from generic frameworks for Industry 4.0 to domain-specific architectures for Industrial Internet of Things (IIoT). While most of the approaches include a service-based architecture for selective integration with enterprise systems, a close two-way integration of the production control systems and IIoT sensors and actuators with Process-Aware Information Systems (PAIS) on the management level for automation and mining of production processes is rarely discussed. This fusion of Business Process Management (BPM) with IIoT can be mutually beneficial for both research areas, but is still in its infancy. We propose a systems architecture for IIoT that shows how to integrate the low-level hardware components–sensors and actuators–of a smart factory with BPM systems. We discuss the software components and their interactions to address challenges of device encapsulation, integration of sensor events, and interaction with existing BPM systems. This integration is demonstrated within several use cases regarding process modeling, automation and mining for a smart factory model, showing benefits of using BPM technologies to analyze, control, and adapt discrete production processes in IIoT.

Inauguration of a unique journal the Journal of Digital Health: a new beginning seeking innovative technology and research for digital health

As the Editor-in-Chief of the newly founded journal the Journal of Digital Health (JDH), I am honored to introduce this journal to you on behalf of Luminescience Press Ltd. JDH is an open access and peer-reviewed journal that publishes high-quality original articles from a wide variety of interdisciplinary perspectives concerning the theory and practice of artificial intelligence (AI), big data and informatics in the medical and healthcare industries to improve the quality and efficiency of healthcare. The journal publishes original articles, reviews, perspectives, research highlights and case reports that present the application of digital technologies in medical diagnostics and treatment, medical devices, machine learning-based decision support, medical record database and intelligent and process-aware information system in healthcare and medicine. We are committed to promoting the interdisciplinary research among clinicians, basic scientists and industrial experts and facilitating the clinical transformation of industrial technologies. Our ultimate mission is to enable the frontiers of science and technology better serve medicine and ultimately contribute to the healthcare of patients.

Inauguration of a unique journal the Journal of Digital Health: a new beginning seeking innovative technology and research for digital health

As the Editor-in-Chief of the newly founded journal the Journal of Digital Health (JDH), I am honored to introduce this journal to you on behalf of Luminescience Press Ltd. JDH is an open access and peer-reviewed journal that publishes high-quality original articles from a wide variety of interdisciplinary perspectives concerning the theory and practice of artificial intelligence (AI), big data and informatics in the medical and healthcare industries to improve the quality and efficiency of healthcare. The journal publishes original articles, reviews, perspectives, research highlights and case reports that present the application of digital technologies in medical diagnostics and treatment, medical devices, machine learning-based decision support, medical record database and intelligent and process-aware information system in healthcare and medicine. We are committed to promoting the interdisciplinary research among clinicians, basic scientists and industrial experts and facilitating the clinical transformation of industrial technologies. Our ultimate mission is to enable the frontiers of science and technology better serve medicine and ultimately contribute to the healthcare of patients.

Organisational resilience through the adaptation of process-aware information systems - a System Dynamics approach

Organisations, seen as socio-technical systems, are in close interaction with process-aware information systems, not only in an operational sense but also in terms of resilience. The ability of the latter to adapt to external or internal challenges affects the resilience of the whole organisation. This exploratory study aims at finding the resilience dynamics in such complex systems. To this end, the systemic perspective provides useful insights. Employing the concept of requisite variety, we treat disaster events and business challenges as 'variety deficiencies' that need to be addressed through an adaptation process. System Dynamics provides the means to create models that describe and simulate the resilience dynamics of the whole system. The study concludes that resilience and adaptation are a matter of knowledge leverage, while the simulation model highlights the effect of knowledge leverage in the adaptation process.

Organisational resilience through the adaptation of process-aware information systems - a System Dynamics approach

Organisational resilience through the adaptation of process-aware information systems - a System Dynamics approach

Parallel-Structure Deep Learning for Prediction of Remaining Time of Process Instances

Event logs generated by Process-Aware Information Systems (PAIS) provide many opportunities for analysis that are expected to help organizations optimize their business processes. The ability to monitor business processes proactively can allow an organization to achieve, maintain or enhance competitiveness in the market. Predictive Business Process Monitoring (PBPM) can provide measures such as the prediction of the remaining time of an ongoing process instance (case) by taking past activities in running process instances into account, as based on the event logs of previously completed process instances. With the prediction provided, we expect that organizations can respond quickly to deviations from the desired process. In the context of the growing popularity of deep learning and the need to utilize heterogeneous representation of data; in this study, we derived a new deep-learning approach that utilizes two types of data representation based on a parallel-structure model, which consists of a convolutional neural network (CNN) and a multi-layer perceptron (MLP) with an embedding layer, to predict the remaining time. Conducting experiments with real-world datasets, we compared our proposed method against the existing deep-learning approach to confirm its utility for the provision of more precise prediction (as indicated by error metrics) relative to the baseline method.

Self-Healing Event Logs

Event logs of process-aware information systems play an increasingly critical role in today's enterprises because they are the basis for a number of business intelligence applications such as complex event processing, provenance analysis, performance analysis, and process mining. However, due to incorrect manual recording, system errors, and resource constraints, event logs inevitably contain noise in the form of deviating event sequences with redundant, missing, or dislocated events. To repair event logs, existing approaches rely on predefined process models to obtain a minimum recovery for each deviating event sequence. However, process models are typically unavailable in practice, rendering existing approaches inapplicable. In this scenario, can event logs be self-healing? To address this problem, we propose an approach that leverages compliant event sequences to repair deviating sequences. Our approach is effective if the compliant event sequences contain sufficient knowledge for repair. We implement our approach in a prototype and employ the tool to conduct experiments. The experimental results demonstrate that our approach can achieve efficient repairs without the help of process models.

Robust and Reliable Process-Aware Information Systems

Over recent years, several sophisticated Process-Aware Information Systems (PAIS) have been proposed for managing business processes and automating large-scale scientific (e-Science) processes. Much of this success is due to their ability to provide generic functionality for modeling, execution and monitoring processes. These functionalities work well when process execution follows a well-behaved path towards achieving the models objectives. However, exceptions and anomalous situations that fall outside of the well-behaved execution path still pose a significant challenge to PAIS. The treatment for such exceptions usually involves interventions in systems by human operators, which result in significant additional cost for businesses. In this paper, we introduce a cost-aware recovery composition method that is able to find and follow recovery paths that reduce the cost of exception handling. From a practical point of view, our proposal reduces complexity and the need for manual interventions to handle exceptions. Finally, the feasibility of recovery mechanism is discussed from its implementation into WED-flow framework.

“VTMine for Visio”: Graphical Tool for Modeling in Process Mining

Process-Aware Information Systems (PAIS) is a special class of the IS intended for the support the tasks of initialization, end-to-end management and completion of business processes. During the operation such systems accumulate a large number of data that are recorded in the form of the event logs. Event logs are a valuable source of knowledge about the actual behavior of a system. For example, there can be found information about the discrepancy between the real and the prescribed behavior of the system; to identify bottlenecks and performance issues; to detect anti-patterns of building a business system. These problems are studied by the discipline called “Process Mining”.The practical application of the process mining methods and practices is carried out using the specialized software for data analysts. The subject area of the process analysis involves the work of an analyst with a large number of graphical models. Such work will be more efficient with a convenient graphical modeling tool. The paper discusses the principles of building a graphical tool “VTMine for Visio” for the process modeling, based on the widespread application for business intelligence Microsoft Visio. There are presented features of the architecture design of the software extension for application in the process mining domain and integration with the existing libraries and tools for working with data. The application of the developed tool for solving various types of tasks for modeling and analysis of processes is demonstrated on a set of experimental schemes.