Abstract Knowledge-intensive processes represent a particularly challenging scenario for process mining. The flexibility that such processes allow constitutes a hurdle as they are hard to capture in a single model. To tackle this problem, multiple visual representations of the same processes could be beneficial, each addressing different information dimensions according to the specific needs and background knowledge of the concrete process workers and stakeholders. In this paper, we propose, describe, and evaluate a framework, named , that leverages visual analytics for the interactive visualization of multi-faceted process information, aimed at supporting the investigation and insight generation of users in their process analysis tasks. is based on a multi-layer visualization methodology that includes a visual backdrop that provides context and an arbitrary number of superimposed and on-demand dimension layers. This arrangement allows our framework to display process information from different perspectives and to project this information onto a domain-friendly representation of the context in which the process unfolds. We provide an in-depth description of the approach’s founding principles, deeply rooted in visualization research, that justify our design choices for the whole framework. We demonstrate the feasibility of the framework through its application in two use-case scenarios in the context of healthcare and personal information management. Plus, we conducted qualitative evaluations with potential end users of both scenarios, gathering precious insights about the efficacy and applicability of our framework to various application domains.

Preference elicitation is a crucial step for every recommendation algorithm. Traditional interaction strategies for eliciting users' interests and needs range from button-based interfaces, where users have to select what they like among a set of fixed alternatives, to more recent conversational interfaces, where users have to reply to some questions formulated by the algorithm about their preferences. However, none of these strategies either mimics the dynamics of real-world open-ended interactions, or allows users to express their needs with an adequate level of expressiveness and control. In this paper, we present a strategy that allow users to express their preferences and needs through natural language statements. In particular, our natural language preference elicitation pipeline allows users to express preferences on objective movie features (e.g., actors, directors, etc.) that are extracted from a structured knowledge base, as well as on subjective features that are collected by mining user-written movie reviews. To validate our claims, we carried out a user study in the movie domain (N = 249). The main finding of our experiment is that users tend to express their preferences by using objective features, whose usage largely overcomes that of subjective features, that are more complicated to be expressed. However, the combination of objective and subjective features often leads to better recommendations, at the cost of a slightly longer conversation. We have also identified the main challenges that arise when users talk to the virtual assistant by using subjective features, and this paves the way for future developments of our methodology.

LatentOut\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\ extbf{Latent}}\\varvec{Out}}$$\\end{document} is a recently introduced algorithm for unsupervised anomaly detection which enhances latent space-based neural methods, namely (Variational) Autoencoders, GANomaly and ANOGan architectures. The main idea behind it is to exploit both the latent space and the baseline score of these architectures in order to provide a refined anomaly score performing density estimation in the augmented latent-space/baseline-score feature space. In this paper we investigate the performance of LatentOut\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\ extbf{Latent}}\\varvec{Out}}$$\\end{document} acting as a one-class classifier and we experiment the combination of LatentOut\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\ extbf{Latent}}\\varvec{Out}}$$\\end{document} with GAAL architectures, a novel type of Generative Adversarial Networks for unsupervised anomaly detection. Moreover, we show that the feature space induced by LatentOut\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\ extbf{Latent}}\\varvec{Out}}$$\\end{document} has the characteristic to enhance the separation between normal and anomalous data. Indeed, we prove that standard data mining outlier detection methods perform better when applied on this novel augmented latent space rather than on the original data space.