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Abstract. Today's fast and competitive markets require businesses to react faster to changes in its environment, and sometimes even before the changes actually happen. Changes can occur on almost every level, e.g. change in demand of customers, change of law, or change of the corporate strategy. Not adapting to these changes can result in financial and legal consequences for any business organisation. IT-controlled business processes are essential parts of modern organisations which motivates why business processes are required to efficiently adapt to these changes in a quick and flexible way. This requirement suggests a more dynamic handling of business processes and their models, moving from design-time business process models to run-time business process models. One general approach to address this problem is provided by the community of models@run.time, in which models reflect the system's current state at any point in time and allow immediate reasoning and adaptation mechanisms. This paper examines the potential role of business process models at run-time by: (1) discussing the state-of the art of both, business process modelling and models@run.time, (2) reflecting on the nature of business processes at run-time, and (3) most importantly, highlighting key research challenges that need addressing to make this step.

In-memory database systems are among the technological drivers of big data processing. In this paper we apply analytical modeling to enable efficient sizing of in-memory databases. We present novel response time approximations under online analytical processing workloads to model thread-level forkjoin and per-class memory occupation. We combine these approximations with a non-linear optimization program to minimize memory swapping in in-memory database clusters. We compare our approach with state-of-the-art response time approximations and trace-driven simulation using real data from an SAP HANA in-memory system and show that our optimization model is significantly more accurate than existing approaches at similar computational costs.

Easy-to-understand and up-to-date models of business processes are important for enterprises, as they aim to describe how work is executed in reality and provide a starting point for process analysis and optimization. With an increasing amount of event data logged by information systems today, the automatic discovery of process models from process logs has become possible. Whereas most existing techniques focus on the discovery of well-formalized models (e.g. Petri nets) which are popular among researchers, business analysts prefer business domain-specific models (such as Business Process Model Notation, BPMN) which are not well formally specified. We present and evaluate an approach for discovering the latter type of process models by formally specifying a hierarchical view on business process models and applying an evolution strategy on it. The evolution strategy efficiently finds process models which best represent a given event log by using fast methods for process model conformance checking, and is partly guided by the diversity of the process model population. The approach contributes to the field of evolutionary algorithms by showing that they can be successfully applied in the real-world use case of process discovery, and contributes to the process discovery domain by providing a promising alternative to existing methods.