Mining structured Petri nets for the visualization of process behavior

Visualization is essential for understanding the models obtained by process mining. Clear and efficient visual representations make the embedded information more accessible and analyzable. This work presents a novel approach for generating process models with structural properties that induce visually friendly layouts. Rather than generating a single model that captures all behaviors, a set of Petri net models is delivered, each one covering a subset of traces of the log. The models are mined by extracting slices of labelled transition systems with specific properties from the complete state space produced by the process logs. In most cases, few Petri nets are sufficient to cover a significant part of the behavior produced by the log.Peer ReviewedPostprint (author's final draft

Inferring Unusual Crowd Events From Mobile Phone Call Detail Records

The pervasiveness and availability of mobile phone data offer the opportunity of discovering usable knowledge about crowd behaviors in urban environments. Cities can leverage such knowledge in order to provide better services (e.g., public transport planning, optimized resource allocation) and safer cities. Call Detail Record (CDR) data represents a practical data source to detect and monitor unusual events considering the high level of mobile phone penetration, compared with GPS equipped and open devices. In this paper, we provide a methodology that is able to detect unusual events from CDR data that typically has low accuracy in terms of space and time resolution. Moreover, we introduce a concept of unusual event that involves a large amount of people who expose an unusual mobility behavior. Our careful consideration of the issues that come from coarse-grained CDR data ultimately leads to a completely general framework that can detect unusual crowd events from CDR data effectively and efficiently. Through extensive experiments on real-world CDR data for a large city in Africa, we demonstrate that our method can detect unusual events with 16% higher recall and over 10 times higher precision, compared to state-of-the-art methods. We implement a visual analytics prototype system to help end users analyze detected unusual crowd events to best suit different application scenarios. To the best of our knowledge, this is the first work on the detection of unusual events from CDR data with considerations of its temporal and spatial sparseness and distinction between user unusual activities and daily routines.Comment: 18 pages, 6 figure

Comparative Evaluation of the Antibacterial, Anti-biofilm and Anti-spore Effects of Theaflavins and Palmitoyl-EGCG

Tea, one of the most common beverages, originates from the leaves of the Camellia sinensis plant. Two major groups of tea are fermented black tea and unfermented green tea. Theaflavins (TFs) are the major polyphenols present in black tea, while mono-palmitoyl-epigallocatechin-gallate (pEGCG) is a modified green tea polyphenol. In this study, the antibacterial effects of TF and pEGCG were evaluated against six selected bacteria, Staphylococcus epidermidis, Streptococcus mutans, Bacillus cereus, Escherichia coli, Pseudomonas aeruginosa and Proteus vulgaris, using an antibacterial assay. A viability assay using SYTOX® staining and flow cytometry was also used to determine the effect of these compounds on S. epidermidis and S. mutans. The anti-biofilm effects of the compounds were also investigated using Congo red assay, resazurin assay, polymerase chain reaction and flow cytometry. Finally, TF and pEGCG were also evaluated on the effects of inhibiting sporulation and germination in Bacillus spp. The results indicate that 0.2% TF and 0.2% pEGCG contain strong antibacterial effects against all bacteria tested with an IC50 range of approximately 0.05-0.1%. In addition, the viability assay showed that both compounds effectively inhibit the growth of the bacteria as early as 3 hours and can maintain their effect for 24 hours. Results from anti-biofilm assays showed that TF and pEGCG are also highly effective in inhibiting the formation of biofilm. Qualitatively, the compounds prevented the formation of biofilm, indicated by the absence of black colonies on the Congo red assay. Quantitatively 0.5% and 1% concentrations of both TF and pEGCG inhibited biofilm formation in the four biofilm forming bacteria tested. PCR and gel electrophoresis was also performed to study the effect of TF and pEGCG on biofilm forming genes in S. epidermidis and S. mutans. Results indicated the presence of bands in the control sets, but absent in the treated samples for both sets of genes, aap and brpA. In addition, flow cytometry was used to further understand the effects of these compounds on S. epidermidis and S. mutans. Results showed that 0.5% TF is able to inhibit approximately 86% of biofilm formation in S. epidermidis and 88% in S. mutans, and 1% pEGCG is able to inhibit about 53% in S. epidermidis and 85% in S. mutans. Finally, results from sporulation and germination assays indicate that both compounds are capable of inhibiting these processes in spore-forming bacteria. 1% TF and 1% pEGCG inhibit sporulation as well as the germination process. 1% TF inhibits approximately 77% of germination whereas 1% pEGCG inhibits 99.9% of germination in B. cereus. These results suggest that tea polyphenols are not only viable antibacterial alternatives, but also potentially promising anti-biofilm and anti-spore agents

Comparative Evaluation of the Antibacterial, Anti-biofilm and Anti-spore Effects of Theaflavins and Palmitoyl-EGCG

Tea, one of the most common beverages, originates from the leaves of the Camellia sinensis plant. Two major groups of tea are fermented black tea and unfermented green tea. Theaflavins (TFs) are the major polyphenols present in black tea, while mono-palmitoyl-epigallocatechin-gallate (pEGCG) is a modified green tea polyphenol. In this study, the antibacterial effects of TF and pEGCG were evaluated against six selected bacteria, Staphylococcus epidermidis, Streptococcus mutans, Bacillus cereus, Escherichia coli, Pseudomonas aeruginosa and Proteus vulgaris, using an antibacterial assay. A viability assay using SYTOX® staining and flow cytometry was also used to determine the effect of these compounds on S. epidermidis and S. mutans. The anti-biofilm effects of the compounds were also investigated using Congo red assay, resazurin assay, polymerase chain reaction and flow cytometry. Finally, TF and pEGCG were also evaluated on the effects of inhibiting sporulation and germination in Bacillus spp. The results indicate that 0.2% TF and 0.2% pEGCG contain strong antibacterial effects against all bacteria tested with an IC50 range of approximately 0.05-0.1%. In addition, the viability assay showed that both compounds effectively inhibit the growth of the bacteria as early as 3 hours and can maintain their effect for 24 hours. Results from anti-biofilm assays showed that TF and pEGCG are also highly effective in inhibiting the formation of biofilm. Qualitatively, the compounds prevented the formation of biofilm, indicated by the absence of black colonies on the Congo red assay. Quantitatively 0.5% and 1% concentrations of both TF and pEGCG inhibited biofilm formation in the four biofilm forming bacteria tested. PCR and gel electrophoresis was also performed to study the effect of TF and pEGCG on biofilm forming genes in S. epidermidis and S. mutans. Results indicated the presence of bands in the control sets, but absent in the treated samples for both sets of genes, aap and brpA. In addition, flow cytometry was used to further understand the effects of these compounds on S. epidermidis and S. mutans. Results showed that 0.5% TF is able to inhibit approximately 86% of biofilm formation in S. epidermidis and 88% in S. mutans, and 1% pEGCG is able to inhibit about 53% in S. epidermidis and 85% in S. mutans. Finally, results from sporulation and germination assays indicate that both compounds are capable of inhibiting these processes in spore-forming bacteria. 1% TF and 1% pEGCG inhibit sporulation as well as the germination process. 1% TF inhibits approximately 77% of germination whereas 1% pEGCG inhibits 99.9% of germination in B. cereus. These results suggest that tea polyphenols are not only viable antibacterial alternatives, but also potentially promising anti-biofilm and anti-spore agents

Transductive hyperspectral image classification: toward integrating spectral and relational features via an iterative ensemble system

Remotely sensed hyperspectral image classification is a very challenging task due to the spatial correlation of the spectral signature and the high cost of true sample labeling. In light of this, the collective inference paradigm allows us to manage the spatial correlation between spectral responses of neighboring pixels, as interacting pixels are labeled simultaneously. The transductive inference paradigm allows us to reduce the inference error for the given set of unlabeled data, as sparsely labeled pixels are learned by accounting for both labeled and unlabeled information. In this paper, both these paradigms contribute to the definition of a spectral-relational classification methodology for imagery data. We propose a novel algorithm to assign a class to each pixel of a sparsely labeled hyperspectral image. It integrates the spectral information and the spatial correlation through an ensemble system. For every pixel of a hyperspectral image, spatial neighborhoods are constructed and used to build application-specific relational features. Classification is performed with an ensemble comprising a classifier learned by considering the available spectral information (associated with the pixel) and the classifiers learned by considering the extracted spatio-relational information (associated with the spatial neighborhoods). The more reliable labels predicted by the ensemble are fed back to the labeled part of the image. Experimental results highlight the importance of the spectral-relational strategy for the accurate transductive classification of hyperspectral images and they validate the proposed algorithm

A novel spectral-spatial co-training algorithm for the transductive classification of hyperspectral imagery data

The automatic classification of hyperspectral data is made complex by several factors, such as the high cost of true sample labeling coupled with the high number of spectral bands, as well as the spatial correlation of the spectral signature. In this paper, a transductive collective classifier is proposed for dealing with all these factors in hyperspectral image classification. The transductive inference paradigm allows us to reduce the inference error for the given set of unlabeled data, as sparsely labeled pixels are learned by accounting for both labeled and unlabeled information. The collective inference paradigm allows us to manage the spatial correlation between spectral responses of neighboring pixels, as interacting pixels are labeled simultaneously. In particular, the innovative contribution of this study includes: (1) the design of an application-specific co-training schema to use both spectral information and spatial information, iteratively extracted at the object (set of pixels) level via collective inference; (2) the formulation of a spatial-aware example selection schema that accounts for the spatial correlation of predicted labels to augment training sets during iterative learning and (3) the investigation of a diversity class criterion that allows us to speed-up co-training classification. Experimental results validate the accuracy and efficiency of the proposed spectral-spatial, collective, co-training strategy

Network regression with predictive clustering trees

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Leveraging multi-view deep learning for next activity prediction

Predicting the next activity in a running trace is a fundamental problem in business process monitoring since such predictive information may allow analysts to intervene proactively and prevent undesired behaviors. This paper describes a predictive process approach that couples multi-view learning and deep learning, in order to gain accuracy by accounting for the variety of information possibly recorded in event logs. Experiments with benchmark event logs show the accuracy of the proposed approach compared to several recent state-of-the-art methods

Dealing with spatial autocorrelation when learning predictive clustering trees

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ORANGE: Outcome-Oriented Predictive Process Monitoring Based on Image Encoding and CNNs

The outcome-oriented predictive process monitoring is a family of predictive process mining techniques that have witnessed rapid development and increasing adoption in the past few years. Boosted by the recent successful applications of deep learning in predictive process mining, we propose ORANGE, a novel deep learning method for learning outcome-oriented predictive process models. The main innovation of this study is that we adopt an imagery representation of the ongoing traces, which delineates potential data patterns that arise at neighbour pixels. Leveraging a collection of images representing ongoing traces, we train a Convolutional Neural Network (CNN) to predict the outcome of an ongoing trace. The empirical study shows the feasibility of the proposed method by investigating its accuracy on different benchmark outcome prediction problems in comparison to state-of-art competitor methods. In addition, we show how ORANGE can be integrated as an Intelligent Assistant into a CVM realized by MTM Project srl company to support sales agents in their negotiations. This case study shows that ORANGE can be effectively used to smartly monitor the outcome of ongoing negotiations by early highlighting negotiations that are candidate to be completed successfully