Process mining techniques are able to extract knowledge from event logs commonly available in today’s information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. There are two main drivers for the growing interest in process mining. On the one hand, more and more events are being recorded, thus, providing detailed information about the history of processes. On the other hand, there is a need to improve and support business processes in competitive and rapidly changing environments. This manifesto is created by the IEEE Task Force on Process Mining and aims to promote the topic of process mining. Moreover, by defining a set of guiding principles and listing important challenges, this manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users. The goal is to increase the maturity of process mining as a new tool to improve the (re)design, control, and support of operational business processes

Abstract. Today's information systems record real-time information about business processes. This enables the monitoring of business constraints at runtime. In this paper, we present a novel runtime verification framework based on linear temporal logic and colored automata. The framework continuously verifies compliance with respect to a predefined constraint model. Our approach is able to provide meaningful diagnostics even after a constraint is violated. This is important as in reality people and organizations will deviate and in many situations it is not desirable or even impossible to circumvent constraint violations. As demonstrated in this paper, there are several approaches to recover after the first constraint violation. Traditional approaches that simply check constraints are unable to recover after the first violation and still foresee (inevitable) future violations. The framework has been implemented in the process mining tool ProM.

We present a hidden Markov model (HMM) for inferring gradual isolation between two populations during speciation, modelled as a time interval with restricted gene flow. The HMM describes the history of adjacent nucleotides in two genomic sequences, such that the nucleotides can be separated by recombination, can migrate between populations, or can coalesce at variable time points, all dependent on the parameters of the model, which are the effective population sizes, splitting times, recombination rate, and migration rate. We show by extensive simulations that the HMM can accurately infer all parameters except the recombination rate, which is biased downwards. Inference is robust to variation in the mutation rate and the recombination rate over the sequence and also robust to unknown phase of genomes unless they are very closely related. We provide a test for whether divergence is gradual or instantaneous, and we apply the model to three key divergence processes in great apes: (a) the bonobo and common chimpanzee, (b) the eastern and western gorilla, and (c) the Sumatran and Bornean orang-utan. We find that the bonobo and chimpanzee appear to have undergone a clear split, whereas the divergence processes of the gorilla and orang-utan species occurred over several hundred thousands years with gene flow stopping quite recently. We also apply the model to the Homo/Pan speciation event and find that the most likely scenario involves an extended period of gene flow during speciation.

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Abstract. Linear Temporal Logic (LTL) on finite traces has proven to be a good basis for the analysis and enactment of flexible constraintbased business processes. The Declare language and system benefit from this basis. Moreover, LTL-based languages like Declare can also be used for runtime verification. As there are often many interacting constraints, it is important to keep track of individual constraints and combinations of potentially conflicting constraints. In this paper, we operationalize the notion of conflicting constraints and demonstrate how innovative automatabased techniques can be applied to monitor running process instances. Conflicting constraints are detected immediately and our toolset (realized using Declare and ProM) provides meaningful diagnostics.

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Declarative workflow languages are easy for humans to understand and use for specifications, but difficult for computers to check for consistency and use for enactment. Therefore, declarative languages need to be translated to something a computer can handle. One approach is to translate the declarative language to linear temporal logic (LTL), which can be translated to finite automata. While computers are very good at handling finite automata, the translation itself is often a road block as it may take time exponential in the size of the input. Here, we present algorithms for doing this translation much more efficiently (around a factor of 10,000 times faster and handling 10 times larger systems on a standard computer), making declarative specifications scale to realistic settings.

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