1,720,961 research outputs found
Process and Resource-aware Responsible Recommender Systems
Full text linkIn recent years, machine learning techniques have seen growing adoption for business improvement across various fields. Organizations are increasingly leveraging predictive models to enhance the performance of their business processes. Predictive analytics, which combines machine learning and data analytics, allows organizations to forecast the future outcomes of processes based on historical data. Its objective is to identify future trends and detect potential issues and anomalies before they occur, enabling proactive interventions to prevent them and optimize overall process performance. Beyond predictive analytics, prescriptive analytics takes things further by generating predictions and advising users on whether and how to intervene in real-time processes to improve outcomes. These outcomes can vary depending on specific business goals and often involve measuring Key Performance Indicators (KPIs) such as costs, execution times, or customer satisfaction. By using data, organizations can make informed decisions to optimize their processes for better performance. This thesis focuses on predictive and prescriptive analytics, particularly enhancing the quality of predictions and recommendations. First, we introduce a framework for enhancing predictive analytics. It aims to ensure fairness in producing predictions, which indeed need to be ethical and not driven by considerations based on ethnicity, gender, background and similar highly discriminative characteristics. Since the recommendation module leverages a prediction model to determine the effects of recommendations, this problem can be translated into ensuring that predictions are fair. An existing framework in Deep Learning for the problem has been adapted to provide fair recommendations for a process-aware recommender system, leveraging adversarial learning. The experiments illustrate that the framework is indeed capable of largely reducing the influence of the undesired characteristics on the predictions. Then, the problem of augmenting event logs has been tackled: this problem is particularly relevant in training recommendation models when the original event log is limited in size or shows unbalanced distributions of events, which makes it challenging to build recommendation models. Extensive experiments of the existing approaches for event-log augmentation have been done, along with the provision of an alternative solution based on Markov models. The experiments show that the alternative solution generally outperforms the state-of-the-art in generating augmented event logs that remain more similar to the real logs. Later, the problem of accompanying the recommendations with explanations is addressed. Indeed, it is unlikely that a process actor passively accepts the recommendations without being explained the rationale behind the choice. An explanation framework based on the recommendation module previously developed is presented and extended by leveraging the theory of the Shapley values. The experiments illustrated that the explanation framework was indeed able to provide reasonable explanations in several process domains. Given that resources are usually shared among a large number of running process instances, the local recommendations provided may not be the best because it could be preferable to sacrifice the best resource for a given activity and instance and move to a less good resource so that the best resource can be assigned to a different activity and instance where no satisfactory alternative resource can be found. To address this problem, two different frameworks are presented: the first one provides a list of recommendations for resources that are globally optimal, with the aim to leave a certain degree of freedom while maintaining good global KPI values, the second delivers recommendations with the aim to ensure that the resource assignment to activities retains a balanced workload among different process participants was balanced
Achieving Fairness in Predictive Process Analytics via Adversarial Learning
No full textPredictive business process analytics has become important for organizations, offering real-time operational support for their processes. However, these algorithms often perform unfair predictions because they are based on biased variables (e.g., gender or nationality), namely variables embodying discrimination. This paper addresses the challenge of integrating a debiasing phase into predictive business process analytics to ensure that predictions are not influenced by biased variables. Our framework leverages on adversial debiasing is evaluated on four use cases, showing a significant reduction in the contribution of biased variables to the predicted value. The proposed technique is also compared with the state of the art in fairness in process mining, illustrating that our framework allows for a more enhanced level of fairness, while retaining a better prediction quality
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Full text linkWe 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
Experience-Based Resource Allocation for Remaining Time Optimization
Full text linkPrescriptive process analytics aims to suggest interventions for those process instances that are predicted to not achieve a satisfactory outcome. Typical interventions are recommending a task to be performed by a specific resource. State-of-the-art prescriptive resource allocation techniques typically propose interventions that allocate the best-fitting resources at a given time. This may result in those resources to become more skilled at the task over time whereas other less experienced resource are rarely allocated. In the long run, such system may result in a unbalanced situation in which some expert resources are overloaded with very high workload and the less experienced resource are assigned fewer tasks and fail to improve. This paper proposes an approach for resource allocation to process instances that aims at a more balanced workload distribution among the resources, even if this means slightly lower process improvements in the short term. Experiments on event logs related to two real processes show that we indeed achieve a more balanced workload distribution, which often yields an overall higher improvement of the whole set of running process instances
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