Visual Analytics Meets Process Mining: Challenges and Opportunities.

Visual analytics (VA) integrates the outstanding capabilities of humans in terms of visual information exploration with the enormous processing power of computers to form a powerful knowledge discovery environment. In other words, VA is the science of analytical reasoning facilitated by interactive interfaces, capturing the information discovery process while keeping humans in the loop. Process mining (PM) is a data-driven and process centric approach that aims to extract information and knowledge from event logs to discover, monitor, and improve processes in various application domains.

City 5.0: Citizen involvement in the design of future cities.

A citizen-centric view is key to channeling technological affordances into the development of future cities in which improvements are made with the quality of citizens' life in mind. This paper proposes City 5.0 as a new citizen-centric design paradigm for future cities, in which cities can be seen as markets connecting service providers with citizens as consumers.

Visual Drift Detection for Event Sequence Data of Business Processes.

Event sequence data is increasingly available in various application domains, such as business process management, software engineering, or medical pathways. Processes in these domains are typically represented as process diagrams or flow charts. So far, various techniques have been developed for automatically generating such diagrams from event sequence data.

Matching events and activities by integrating behavioral aspects and label analysis.

Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during the execution of a process. These event data can be used to analyze the process using process mining techniques to discover the real process, measure conformance to a given process model, or to enhance existing models with performance information. Mapping the produced events to activities of a given process model is essential for conformance checking, annotation and understanding of process mining results.