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Providing appropriate structures around human resources can streamline operations and thus facilitate the competitiveness of an organization. To achieve this goal, modern organizations need to acquire an accurate and timely understanding of…

In today's digitalized world, where software systems are becoming increasingly ubiquitous and complex, the quality aspect of explainability is gaining relevance. A major challenge in achieving adequate explanations is the elicitation of…

A common challenge for improving business processes in large organizations is that business people in charge of the operations are lacking a fact-based understanding of the execution details, process variants, and exceptions taking place in…

Process discovery aims to discover descriptive process models from event logs. These discovered process models depict the actual execution of a process and serve as a foundational element for conformance checking, performance analyses, and…

Process mining involves discovering, monitoring, and improving real processes by extracting knowledge from event logs in information systems. Process mining has become an important topic in recent years, as evidenced by a growing number of…

Process mining aims to diagnose and improve operational processes. Process mining techniques allow analyzing the event data generated and recorded during the execution of (business) processes to gain valuable insights. Process discovery is…

Decisions and the underlying rules are indispensable for driving process execution during runtime, i.e., for routing process instances at alternative branches based on the values of process data. Decision rules can comprise unary data…

Event data, often stored in the form of event logs, serve as the starting point for process mining and other evidence-based process improvements. However, event data in logs are often tainted by noise, errors, and missing data. Recently, a…

Process discovery is one of the primary process mining tasks and starting point for process improvements using event data. Existing process discovery techniques aim to find process models that best describe the observed behavior. The focus…

Process mining, a technique turning event data into business process insights, has traditionally operated on the assumption that each event corresponds to a singular case or object. However, many real-world processes are intertwined with…

Process mining in healthcare presents a range of challenges when working with different types of data within the healthcare domain. There is high diversity considering the variety of data collected from healthcare processes: operational…

Web usage mining: automatic discovery of patterns in clickstreams and associated data collected or generated as a result of user interactions with one or more Web sites. This paper describes web usage mining for our college log files to…

Topic models are widely used analysis techniques for clustering documents and surfacing thematic elements of text corpora. These models remain challenging to optimize and often require a "human-in-the-loop" approach where domain experts use…

The visual analytics community has proposed several user modeling algorithms to capture and analyze users' interaction behavior in order to assist users in data exploration and insight generation. For example, some can detect exploration…

Recommender systems are one of the most successful applications of data mining and machine learning technology in practice. Academic research in the field is historically often based on the matrix completion problem formulation, where for…

Asking clarifying questions in response to search queries has been recognized as a useful technique for revealing the underlying intent of the query. Clarification has applications in retrieval systems with different interfaces, from the…

The applicability of process mining techniques hinges on the availability of event logs capturing the execution of a business process. In some use cases, particularly those involving customer-facing processes, these event logs may contain…

Process mining aims to extract and analyze insights from event logs, yet algorithm metric results vary widely depending on structural event log characteristics. Existing work often evaluates algorithms on a fixed set of real-world event…

Process mining traditionally relies on input consisting of low-level events that capture individual activities, such as filling out a form or processing a product. However, many of the complex problems inherent in processes, such as…

Process discovery algorithms learn process models from executed activity sequences, describing concurrency, causality, and conflict. Concurrent activities require observing multiple permutations, increasing data requirements, especially for…