Today’s business processes are often controlled and supported by information systems. These systems record real-time information about business processes during their executions. This enables the analysis at runtime of the process behavior. However, many modern systems produce “big data”, i.e., collections of data sets so large and complex that it becomes impossible to store and process all of them. Moreover, few processes are in steady-state but, due to changing circumstances, they evolve and systems need to adapt continuously. In this paper, we present a novel framework for the discovery of LTL-based declarative process models from streaming event data in settings where it is impossible to store all events over an extended period of time or where processes evolve while being analyzed. The framework continuously updates a set of valid business constraints based on the events occurred in the event stream. In addition, our approach is able to provide meaningful information about the most significant concept drifts, i.e., changes occurring in a process during its execution. We report about experimental results obtained using synthetic logs and a real-life event log pertaining to the treatment of patients diagnosed with cancer in a large Dutch academic hospital

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Cross-system bug fixing propagation is frequent among systems having similar characteristics, using a common framework, or, in general, systems with cloned source code fragments. While previous studies showed that clones tend to be properly maintained within a single system, very little is known about cross-system bug management.This paper describes an approach to mine explicitly documented cross-system bug fixings, and to relate their occurrences to social characteristics of contributors discussing through the project mailing lists-e.g., degree, betweenness, and brokerage-as well as to the contributors' activity on source code.The paper reports results of an empirical study carried out on FreeBSD and OpenBSD kernels. The study shows that the phenomenon of cross-system bug fixing between these two projects occurs often, despite the limited overlap of contributors. The study also shows that cross-system bug fixings mainly involve contributors with the highest degree, betweenness and brokerage level, as well as contributors that change the source code more than others.