1,720,960 research outputs found
A machine learning approach to understand business processes
Get PDFBusiness processes (industries, administration, hospitals, etc.) become nowadays more and more complex and it is difficult to have a complete understanding of them. The goal of the thesis is to show that machine learning techniques can be used successfully for understanding a process on the basis of data, by means of clustering process related measures, induction of predictive models, and process discovery. This goal is achieved by means of two approaches: (i) classify process cases (e.g. patients) into logistic homogeneous groups and induce models that assign a new case to a logistic group and (ii) discover the underlying process. By doing so, the process can be modelled, analysed and improved. Another benefit is that systems can be designed more efficiently to support and control the processes more effectively. We target on the analysis of two sorts of data, namely aggregated data and sequence data. Aggregated data result from performing some transformations on raw data, focusing on a specific concept, that is not yet explicit in the raw data. This aggregation is similar to feature construction, as used in the machine learning domain. In this thesis, aggregated data are the variables that result from operationalizing the concept of process complexity. These aggregated data are used to develop logistic homogeneous clusters. This means that elements in different clusters will differ from the routing complexity point of view. We show that developing homogeneous clusters for a given process is relevant in connection with the induction of predictive models. Namely, the routing in the process can be predicted using the logistic clusters. We do not aim to provide concrete directives for building control systems, rather our models should be taken as indicatives of their potential. Sequence data describe the sequence of activities over time in a process execution. They are recorded in a process log, during the execution of the process steps. Due to exceptions, missing or incomplete registration and errors, the data can be noisy. By using sequence data, the goal is to derive a model explaining the events recorded. In situations without noise and sufficient information, we provide a method for building a process model from the process log. Moreover, we discuss the class of models for which it is possible to accurately rediscover the model by looking at the process log. Machine learning techniques are especially useful when discovering a process model from noisy sequence data. Such a model can be further analyzed and eventually improved, but these issues are beyond the scope of this thesis. Through the applications of our proposed methods on different data (e.g. hospital data, workflow data and administrative governmental data), we have shown that our methods result in useful models and subsequently can be used in practice. We applied our methods on data-sets for which (i) it was possible to aggregate relevant information and (ii) sequence data were available
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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