Sandra Zilker

Technische Hochschule Nürnberg Georg Simon Ohm | OHM · Department of Computer Science

The number of information systems (IS) studies dealing with explainable artificial intelligence (XAI) is currently exploding as the field demands more transparency about the internal decision logic of machine learning (ML) models. However, most techniques subsumed under XAI provide post-hoc-analytical explanations, which have to be considered with...

With the ongoing trend of servitization nurtured through digital technologies, the analysis of services as a starting point for improvement is gaining more and more importance. Service analytics has been defined as a concept to analyze the data generated during service execution to create value for providers and customers. To create more useful ins...

Risk-based artificial intelligence (AI) regulations define risk categories for AI-enabled systems. The operators of such systems must determine the risk category applicable to their AI systems. This requires detailed knowledge of the classification rules defined in the regulations. Only a few supporting tools have been developed to facilitate the t...

Machine learning is permeating every conceivable domain to promote data-driven decision support. The focus is often on advanced black-box models due to their assumed performance advantages, whereas interpretable models are often associated with inferior predictive qualities. More recently, however, a new generation of generalized additive models (G...

In business process management, business process redesign (BPR) aims to improve business processes. In the past, BPR was mainly a manual task, with little computational power and typically high labor and time intensity. The increasing amount of stored process data and great advancements in generative machine learning (GML) and other analytical appr...

Event log data reflects the behavior of business processes that are mapped in organizational information systems. Predictive process monitoring (PPM) transforms this data into value; it creates process-related predictions to provide insights required for proactive interventions at process runtime. Existing PPM techniques require sufficient amounts...

Machine learning (ML) provides algorithms to create computer programs based on data without explicitly programming them. In business process management (BPM), ML applications are used to analyse and improve processes efficiently. Three frequent examples of using ML are providing decision support through predictions, discovering accurate process mod...

Machine learning (ML) provides algorithms to create computer programs based on data without explicitly programming them. In business process management (BPM), ML applications are used to analyse and improve processes efficiently. Three frequent examples of using ML are providing decision support through predictions, discovering accurate process mod...

Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage a patient’s complete health history to make informed decisions about future events. However, previous work has mostly relied on so-called black-box models, which are unin...

Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage a patient's complete health history to make informed decisions about future events. However, previous work has mostly relied on so-called black-box models, which are unin...

Organizations aim to achieve operational excellence to reduce costs and improve the quality of their business processes. Business process management (BPM) enables continuous improvement of business processes. Business process management systems (BPMS) serve as an entry point to BPM activities and afford firms to manage, execute, and automate busine...

The huge amount of data recorded during business process executions in today's organizations creates the need to leverage this data. While most existing business process monitoring methods are capable of including structured context information, the incorporation of unstructured information, for example, text, has rarely been researched. Recent adv...

The performance of a service process can be improved by the early anticipation of future behavior, such as predicting the next activity using predictive business process monitoring (PBPM). Recent PBPM techniques are based on deep neural networks (DNNs) and consider the process context to create accurate predictions. To provide explainability of the...

Proactively analyzing patient pathways can help healthcare providers to anticipate treatment-related risks, detect undesired outcomes, and allocate resources quickly. For this purpose, modern methods from the field of predictive business process monitoring can be applied to create data-driven models that capture patterns from past behavior to provi...

Predictive process monitoring (PPM) is the discipline of exploiting event logs of business processes to construct predictive models for anticipating different properties of running business processes. The event logs used contain control flow information of past process executions and, often, additional information about the context in which a proce...

Workarounds are performed intentionally by employees to bypass obstacles constraining their day-to-day work. These obstacles manifest from latent misfits in the interplay of information systems, organizational structure, and human agency. While workarounds are often mandatory for employees to perform their work, they can yield positive and negative...

The number of information systems (IS) studies dealing with explainable artificial intelligence (XAI) is currently exploding as the field demands more transparency about the internal decision logic of machine learning (ML) models. However, most techniques subsumed under XAI provide post-hoc-analytical explanations, which have to be considered with...

Since process mining started to reveal the potential of event logs, it has been applied in various process settings ranging from healthcare to production. Every single setting poses its challenges to process analysts who want to apply process mining. The present paper aims at minimizing such challenges for enterprises in the industrial sector by pr...

Organizations are increasingly adopting artificial intelligence (AI) for business processes. AI-based recommendations aim at supporting users in decision-making, e.g., by pre-filtering options. However, users can often hardly understand how these recommendations are developed. This issue is called “black box problem”. In the context of Human Resour...

Business processes run at the core of an organisation’s value creation and are often the target of optimisation efforts. Organisations aim at adhering to their optimised processes. However, deviations from the optimised process still occur and may potentially impede efficiency in process executions. Conformance checking can provide valuable insight...

Predictive business process monitoring (PBPM) provides a set of techniques to perform different prediction tasks in running business processes, such as the next activity, the process outcome, or the remaining time. Nowadays, deep-learning-based techniques provide more accurate predictive models. However, the explainability of these models has long...

Since process mining started to reveal the potential of event logs, it has been applied in various process settings ranging from healthcare to production. Every single setting poses its challenges to process analysts who want to apply process mining. The present paper aims at minimizing such challenges for enterprises in the industrial sector by pr...

Artificial intelligence (AI) offers promising tools to support the job-seeking process by providing automatic and user-centered job recommendations. However, job seekers often hesitate to trust AI-based recommendations in this context given the far-reaching consequences of the importance of the decision for a job on their future career and life. Th...

Predictive business process monitoring (PBPM) techniques predict future process behaviour based on historical event log data to improve operational business processes. Concerning the next activity prediction, recent PBPM techniques use state-of-the-art deep neural networks (DNNs) to learn predictive models for producing more accurate predictions in...

Predictive business process monitoring (PBPM) techniques predict future process behaviour based on historical event log data to improve operational business processes. Concerning the next activity prediction, recent PBPM techniques use state-of-the-art deep neural networks (DNNs) to learn predictive models for producing more accurate predictions in...

Predictive business process monitoring (PBPM) techniques predict future process behaviour based on historical event log data to improve operational business processes. Concerning the next activity prediction, recent PBPM techniques use state-of-the-art deep neural networks (DNNs) to learn predictive models for producing more accurate predictions in...

Predictive business process monitoring (PBPM) is a class of techniques designed to predict behaviour, such as next activities, in running traces. PBPM techniques aim to improve process performance by providing predictions to process analysts, supporting them in their decision making. However, the PBPM techniques` limited predictive quality was cons...

Predictive business process monitoring (PBPM) is a class of techniques designed to predict behaviour, such as next activities, in running traces. PBPM techniques aim to improve process performance by providing predictions to process analysts, supporting them in their decision making. However, the PBPM techniques' limited predictive quality was cons...

“Spaghetti-like” process models discovered through process mining are challenging to comprehend, especially, for inexperienced users. But, at the same time, they contain potential insights for decisionmakers. Designing process discovery techniques that work well in both aspects – being comprehensible and providing valuable information, for various...

Researchers have proposed a variety of predictive business process monitoring (PBPM) techniques aiming to predict future process behaviour during the process execution. Especially, techniques for the next activity prediction anticipate great potential in improving operational business processes. To gain more accurate predictions, a plethora of thes...

Predictive business process monitoring (PBPM) deals with predicting a process's future behavior based on historical event logs to support a process's execution. Many of the recent techniques utilize a machine-learned model to predict which event type is the next most likely. Beyond PBPM, prescriptive BPM aims at finding optimal actions based on con...

Software companies that offer web-based services instead of local installations can record the user's interactions with the system from a distance. This data can be analyzed and subsequently improved or extended. A recommender system that guides users through a business process by suggesting next clicks can help to improve user satisfaction, and he...