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

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News organizations employ personalized recommenders to target news articles to speci c readers and thus foster engagement. Existing approaches rely on extensive user pro les. However frequently possible, readers rarely authenticate themselves on news publishers' websites. is paper proposes an approach for such cases. It provides a basic degree of personalization while complying with the key characteristics of news recommendation including news popularity, recency, and the dynamics of reading behavior. We extend existing research on the dynamics of news reading behavior by focusing both on the progress of reading interests over time and their relations. Reading interests are considered in three levels: short-, medium-, and long-term. Combinations of these are evaluated in terms of added value to the recommendation's performance and ensured news variety. Experiments with 17-month worth of logs from a German news publisher show that most frequent relations between news reading interests are constant in time but their probabilities change. Recommendations based on combined shortterm and long-term interests result in increased accuracy while recommendations based on combined short-term and medium-term interests yield higher news variety.

Abstract. As large companies are building up their enterprise architecture solutions, they need to relate business process descriptions to lengthy and formally structured documents of corporate policies and standards. However, these documents are usually not specific to particular tasks or processes, and the user is left to read through a substantial amount of irrelevant text to find the few fragments that are relevant to him. In this paper, we describe a text mining approach to establishing links between business process model elements and relevant parts of governing documents in Statoil, one of Norway's largest companies. The approach builds on standard IR techniques, gives us a ranked list of text fragments for each business process activity, and can easily be integrated with Statoil's enterprise architecture solution. With these ranked lists at hand, users can easily find the most relevant sections to read before carrying out their activities.