1,335 research outputs found
Synthetic Event Streams
No full textThis package contains 942 synthetic event streams that simulate concept drift in business processes. Each stream has only one drift. Different stream sizes, types and perspective of drift, and noise percentual are applied. Each event in the stream contains four main attributes: case identification, event name, event start time, event completion time
Analysis of Language Inspired Trace Representation for Anomaly Detection
No full textA great concern for organizations is to detect anomalous process instances within their business processes. For that, conformance checking performs model-aware analysis by comparing process logs to business models for the detection of anomalous process executions. However, in several scenarios, a model is either unavailable or its generation is costly, which requires the employment of alternative methods to allow a confident representation of traces. This work supports the analysis of language inspired process analysis grounded in the word2vec encoding algorithm. We argue that natural language encodings correctly model the behavior of business processes, supporting a proper distinction between common and anomalous behavior. In the experiments, we compared accuracy and time cost among different word2vec setups and classic encoding methods (token-based replay and alignment features), addressing seven different anomaly scenarios. Feature importance values and the impact of different anomalies in seven event logs were also evaluated to bring insights on the trace representation subject. Results show the proposed encoding overcomes representational capability of traditional conformance metrics for the anomaly detection task
Tavares Bastos: a liberdade política a partir da descentralização
No full textDissertação (mestrado) - Universidade Federal de Santa Catarina, Centro de Ciências Jurídicas, Programa de Pós-Graduação em Direito, Florianópolis, 2012.Nesse trabalho será explorada a ideia de descentralização política e administrativa no Brasil no período do Segundo Reinado, no pensamento político de Tavares Bastos. Esse autor inova ao apresentar uma proposta de reforma que pretende solucionar de forma conjunta os problemas políticos e sociais de sua época. Questões como a educação, a abolição da escravidão, a imigração estrangeira, a liberdade de cabotagem, são alguns dos temas levantados pelo autor nesse sentido. Pretendia propor um modelo de Estado que tinha como principal objetivo o desenvolvimento moral e material do país. Para tanto adotou a via da descentralização política e administrativa, como forma de promoção de uma política voltada para a liberdade, preocupada com a sorte do povo e comprometida com o desenvolvimento do país. Assim, pretendeu-se relacionar os direitos fundamentais defendidos e promovidos pelo autor para demonstrar que a descentralização aparece como aspecto fundamental para o exercício da liberdade e à promoção do progresso. Abstract: In this paper we explore the idea of political and administrative decentralization in Brazil during the Empire, the political thoughts of Tavares Bastos. This author innovates to propose a reform intended to address jointly the political and social problems of his time. Issues such as education, the abolition of slavery, foreign immigration, freedom of cabotage between ports, are some of the issues brought by the author in this regard. He intended to propose a model of state which had as main objective the moral and material development of the country. For both adopted the path of political and administrative decentralization as a way of promoting a political freedom, worried about the fate of the people and committed to the development of the country. Thus, we sought to relate the fundamental rights protected and promoted by the author to demonstrate that decentralization appears as a fundamental aspect to the exercise of freedom and the promotion of progress
Evaluating Trace Encoding Methods in Process Mining
No full textEncoding methods affect the performance of process mining tasks but little work in the literature focused on quantifying their impact. In this paper, we compare 10 different encoding methods from three different families (trace replay and alignment, graph embeddings, and word embeddings) using measures to evaluate the overlaps in the feature space, the accuracy obtained, and the computational resources (time) consumed with a classification task. Across hundreds of event logs representing four variations of five scenarios and five anomalies, it was possible to identify the edge2vec method as the most accurate and effective in reducing class overlapping in the feature space
Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles
No full textUnderstanding the decisions of tree-based ensembles and their relationships is pivotal for machine learning model interpretation. Recent attempts to mitigate the human-in-the-loop interpretation challenge have explored the extraction of the decision structure underlying the model taking advantage of graph simplification and path emphasis. However, while these efforts enhance the visualisation experience, they may either result in a visually complex representation or compromise the interpretability of the original ensemble model. In addressing this challenge, especially in complex scenarios, we introduce the Decision Predicate Graph (DPG) as a model-specific tool to provide a global interpretation of the model. DPG is a graph structure that captures the tree-based ensemble model and learned dataset details, preserving the relations among features, logical decisions, and predictions towards emphasising insightful points. Leveraging well-known graph theory concepts, such as the notions of centrality and community, DPG offers additional quantitative insights into the model, complementing visualisation techniques, expanding the problem space descriptions, and offering diverse possibilities for extensions. Empirical experiments demonstrate the potential of DPG in addressing traditional benchmarks and complex classification scenarios
Figure 2 in An annotated catalogue of Echinodermata types in the Museu de Zoologia, Universidade de São Paulo, Brazil
No full textFigure 2. Type specimens. (A) Chantalia conandae (MZUSP 1896, holotype); (B) Cucumaria solangeae (MZUSP 286, paratype); (C) Parathyone itapuaensis (MZUSP 2089); (D) Euthyonidiella occidentalis (MZUSP 1139, neotype); (E) Gymnopipina ikamiaba (MZUSP 1514, holotype); (F) Havelockia mansoae (MZUSP 1525, holotype); (G) Havelockia oraneae (MZUSP 1636, holotype) and (H) Havelockia smirnovi (MZUSP 1352, holotype).Published as part of Martins, Luciana, Marques, Alexandre Oliveira, Fukuda, Marcelo Veronesi & Tavares, Marcos, 2022, An annotated catalogue of Echinodermata types in the Museu de Zoologia, Universidade de São Paulo, Brazil, pp. 1-11 in Papéis Avulsos de Zoologia 62 on page 5, DOI: 10.11606/1807-0205/2022.62.015, http://zenodo.org/record/717749
META LEARNING IN PROCESS MINING: TOWARD A SYSTEMATIC APPROACH TO DESIGN DATA ANALYTICS PIPELINES WITH EVENT LOGS
No full textWith the democratization of computational resources, organizations pay great attention to recording the execution of internal procedures to improve the quality of their services. Modern information systems track and record in-depth data regarding activities performed within their business processes. The event data describes the actual process performance along with several possible attributes. Process mining stands as a set of techniques that leverage insights from event data. With that, organizations can employ process mining-based approaches to understand the processes' behavior, increase the value of services, save resources and improve execution time.
Given the multitude of tasks within process mining and the plethora of algorithms and solutions, deciding which methods to apply is a complex effort. Notwithstanding that process mining techniques have now achieved the maturity level to cover the entire stack of the data science pipeline, from raw data to decisions. On the one hand, stakeholders detain business and domain knowledge. On the other hand, they often lack the technical expertise to guide choices. Moreover, many tasks require the application of a combination of several steps, i.e., a pipeline. Designing a suitable pipeline becomes then a complex task, enhanced by the fact that domain experts and technical experts are often not the same people.
In this thesis, we propose a task-agnostic framework to automate the design of process mining pipelines. Considering that there is no optimal pipeline for every observable phenomenon, we start from the hypothesis that process behavior might indicate which steps or algorithms are better suited. For that, we rely on a meta-learning approach that maps the relationships between event data and suitable solutions. The application of the proposed framework generates two main contributions. First, given a business process (event log) and a task (e.g., process discovery, trace clustering, anomaly detection), a user can retrieve a pipeline recommendation that best matches the underlying process behavior. The second byproduct of the framework is a systematic mapping of the relationship between event log characteristics and optimal pipelines. This mapping provides experts and data analysts with a solid foundation to better understand the task at hand. That is an enlightenment of the correlation between the problem space (event data), algorithm space (process mining pipelines), and performance space (quality criteria).
We instantiated the framework in three different process tasks. Results indicate that indeed there is a relationship between the different spaces since guided recommendations overcome the current baselines. Therefore, showing the importance of investigating guided solutions and that mapping the spaces can be of interest to organizations. Moreover, we investigate which process features are most decisive for each problem. The presented solution is also suitable for users of different knowledge levels. When applying the framework, an inexperienced user has data-based pipeline recommendations whereas an expert is provided with a quantitative mapping that can be used to leverage the knowledge regarding the process task
Artificial and Natural Topic Detection in Online Social Networks
No full textOnline Social Networks (OSNs), such as Twitter, offer attractive means of social interactions and communications, but also raise privacy and security issues. The OSNs provide valuable information to marketing and competitiveness based on users posts and opinions stored inside a huge volume of data from several themes, topics, and subjects. In order to mining the topics discussed on an OSN we present a novel application of Louvain method for TopicModeling based on communities detection in graphs by modularity. The proposed approach succeeded in finding topics in five different datasets composed of textual content from Twitter and Youtube. Another important contribution achieved was about the presence of texts posted by spammers. In this case, a particular behavior observed by graph community architecture (density and degree) allows the indication of a topic strength and the classification of it as natural or artificial. The later created by the spammers on OSNs
Evaluating Trace Encoding Methods in Process Mining
No full textEncoding methods affect the performance of process mining tasks but little work in the literature focused on quantifying their impact. In this paper, we compare 10 different encoding methods from three different families (trace replay and alignment, graph embeddings, and word embeddings) using measures to evaluate the overlaps in the feature space, the accuracy obtained, and the computational resources (time) consumed with a classification task. Across hundreds of event logs representing four variations of five scenarios and five anomalies, it was possible to identify the edge2vec method as the most accurate and effective in reducing class overlapping in the feature space
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