Photodetachment study of the 1s3s4s ^4S resonance in He^-

A Feshbach resonance associated with the 1s3s4s ^{4}S state of He^{-} has been observed in the He(1s2s ^{3}S) + e^- (\epsilon s) partial photodetachment cross section. The residual He(1s2s ^{3}S) atoms were resonantly ionized and the resulting He^+ ions were detected in the presence of a small background. A collinear laser-ion beam apparatus was used to attain both high resolution and sensitivity. We measured a resonance energy E_r = 2.959 255(7) eV and a width \Gamma = 0.19(3) meV, in agreement with a recent calculation.Comment: LaTeX article, 4 pages, 3 figures, 21 reference

The biggest business process management problems to solve before we die

It may be tempting for researchers to stick to incremental extensions of their current work to plan future research activities. Yet there is also merit in realizing the grand challenges in one's field. This paper presents an overview of the nine major research problems for the Business Process Management discipline. These challenges have been collected by an open call to the community, discussed and refined in a workshop setting, and described here in detail, including a motivation why these problems are worth investigating. This overview may serve the purpose of inspiring both novice and advanced scholars who are interested in the radical new ideas for the analysis, design, and management of work processes using information technology.</p

The biggest business process management problems to solve before we die

It may be tempting for researchers to stick to incremental extensions of their current work to plan future research activities. Yet there is also merit in realizing the grand challenges in one’s field. This paper presents an overview of the nine major research problems for the Business Process Management discipline. These challenges have been collected by an open call to the community, discussed and refined in a workshop setting, and described here in detail, including a motivation why these problems are worth investigating. This overview may serve the purpose of inspiring both novice and advanced scholars who are interested in the radical new ideas for the analysis, design, and management of work processes using information technology

The biggest business process management problems to solve before we die

It may be tempting for researchers to stick to incremental extensions of their current work to plan future research activities. Yet there is also merit in realizing the grand challenges in one's field. This paper presents an overview of the nine major research problems for the Business Process Management discipline. These challenges have been collected by an open call to the community, discussed and refined in a workshop setting, and described here in detail, including a motivation why these problems are worth investigating. This overview may serve the purpose of inspiring both novice and advanced scholars who are interested in the radical new ideas for the analysis, design, and management of work processes using information technology

Legislative Documents

Also, variously referred to as: House bills; House documents; House legislative documents; legislative documents; General Court documents

Adaptive Process Model Matching: Improving the Effectiveness of Label-Based Matching through Automated Configuration and Expert Feedback

Process model matchers automate the detection of activities that represent similar functionality in different models. Thus, they provide support for various tasks related to the management of business processes including model collection management and process design. Yet, prior research primarily demonstrated the matchers’ effectiveness, i.e., the accuracy and the completeness of the results. In this context (i) the size of the empirical data is often small, (ii) all data is used for the matcher development, and (iii) the validity of the design decisions is not studied. As a result, existing matchers yield a varying and typically low effectiveness when applied to different datasets, as among others demonstrated by the process model matching contests in 2013 and 2015. With this in mind, the thesis studies the effectiveness of matchers by separating development from evaluation data and by empirically analyzing the validity and the limitations of design decisions. In particular, the thesis develops matchers that rely on different sources of information. First, the activity labels are considered as natural-language descriptions and the Bag-of-Words Technique is introduced which achieves a high effectiveness in comparison to the state of the art. Second, the Order Preserving Bag-of-Words Technique analyzes temporal dependencies between activities in order to automatically configure the Bag-of-Words Technique and to improve its effectiveness. Third, expert feedback is used to adapt the matchers to the domain characteristics of process model collections. Here, the Adaptive Bag-of-Words Technique is introduced which outperforms the state-of-the-art matchers and the other matchers from this thesis

A Method for Debugging Process Discovery Pipelines to Analyze the Consistency of Model Properties

Event logs have become a valuable information source for business process management, e.g., when analysts discover process models to inspect the process behavior and to infer actionable insights. To this end, analysts configure discovery pipelines in which logs are filtered, enriched, abstracted, and process models are derived. While pipeline operations are necessary to manage log imperfections and complexity, they might, however, influence the nature of the discovered process model and its properties. Ultimately, not considering this possibility can negatively affect downstream decision making. We hence propose a framework for assessing the consistency of model properties with respect to the pipeline operations and their parameters, and, if inconsistencies are present, for revealing which parameters contribute to them. Following recent literature on software engineering for machine learning, we refer to it as debugging. From evaluating our framework in a real-world analysis scenario based on complex event logs and third-party pipeline configurations, we see strong evidence towards it being a valuable addition to the process mining toolbox

Similarity resonance for improving process model matching accuracy

\u3cp\u3eComparing and matching process models is a common task in many process engineering applications such as in querying and refactoring large process model repositories. The first and most challenging task in process model matching is to establish a similarity measure between process activities. Existing works use label similarity techniques to compute activities' similarity. In this paper, we propose a contextual similarity measure that, in addition to label similarity, exploits the similarity of the context surrounding activities. We introduce similarity resonance, a recursive algorithm that computes a global contextual similarity between process activities. Our intuition is that two activities are similar if they are executed in similar contexts which are represented by the surrounding activity neighbors. In turn, the activity neighbors are similar if their neighbors are similar, and so on. In this way, the pairwise similarity between all activities is iteratively computed and updated based on the similarity between their neighbors until the similarity scores have been stabilized and have been propagated to the whole graph. The approach has been implemented as a ProM plugin and was evaluated using several real-life datasets.\u3c/p\u3