Information Dimension Research Articles

Machine Learning techniques for predicting inpatient probability and LOS from EU Injury Database

Abstract Background Unlike statistical inference Machine Learning (ML) makes repeatable predictions without prior assumptions about the underlying relationships among variables. The Full Data Set (FDS) of the EU-IDB (European Injury DataBase) consent to explore risk of hospitalization due to injuries using many predictors. The Length Of Stay of patients in hospital (LOS) is a relevant proxy of complexity of treatment and resources consumption. Methods The IDB-FDS provides more than 3.800.000 ED records, in years 2008-19 for 19 Countries. LASSO (Least Absolute Shrinkage and Selection Operator) cross-validated linearized regression technique (linear for LOS) was used for variable selection and parameter regularization. Inpatients are those admitted or transferred to hospital. Days of hospitalization were used for LOS. A cross-validated Generalized Linear Model was performed on 5 folds randomly sampled assigning 80% of records to training and 20% to testing samples. Results The strongest predictors of hospital admission risk, selected by the model were in order of importance: EUROCOST-39 diagnoses categories, Age Group, Intent, Mechanism Of Injury, Activity When Injured, Transport Injury Event, Sex Of Patient, Place Of Occurrence. EUROCOST-39 represents 61,9% of explained variability and age group 19,4%. The strongest predictors of LOS were substantially the same: EUROCOST-39 86,2% of explained variability and age group 8,6%. Conclusions The main part of variability in the ML model is explained by diagnoses reclassified according to a disability standardization method. For instance, in the maximum training sample risk of hospitalization ranges from odd 0,76% for hand/fingers sprain up to 154,02% for brain concussion. In the median sample LOS ranges from 0,45 days for strain of hand/fingers up to 11,65 for multi-trauma. A combination of more disabling injury, older age and mechanism of injury (i.e. threat to breathing) increases enormously the risk of hospitalization and LOS. Key messages • Machine Learning techniques applied on EU-IDB can provide identification of relevant risk factors of hospital admission from injuries for targeting preventive measures and organizing health care. • Artificial Intelligence consents to analyse big amount of data as dimension or analytical detail of information for identifying patterns and predictors of specific indicators for injuries or diseases.

Enhanced stimulated Raman and fluorescence imaging by single-frame trained BDN

Hyperspectral and multispectral imaging capture an expanded dimension of information that facilitates discoveries. However, image features are frequently obscured by noise generated from the limited photodamage threshold of the specimen. Although machine learning approaches demonstrate considerable promise in addressing this challenge, they typically require extensive datasets, which can be difficult to obtain. Here, we introduce BiFormer denoising network (BDN), designed to effectively and efficiently extract image features by utilizing both local and global level connections, sparse architectures, and fine-tuning. Experimental results indicate that BDN enhances the quality of stimulated Raman scattering (SRS) images by up to 16-fold in signal-to-noise ratio (SNR), particularly improving subtle features at higher spatial frequencies. Furthermore, BDN is successfully adapted to fluorescence imaging, achieving significant improvements in SNR and order-of-magnitude reduction in exposure time, thereby showcasing its versatility across various imaging modalities. Collectively, BDN exhibits substantial potential for spectroscopic imaging applications in the fields of biomedicine and materials science.

Fractal analysis of urban growth in complex systems

A complex network consists of number of elements which may interact with each other to form a complex system. The fractal feature is one of the most important characteristics in complex networks. This study examines the fractal analysis of Madaba City Centre as a complex system through the period to 1953-2022. In order to reveal the fractal characteristics of the study area box-counting (Db) and information (Di) dimensions were calculated using Benoit for five satellite images for years 1953, 1981, 1992, 2000, and 2022. Results showed that Madaba road networks have fractal characteristics for the study period, and the calculated fractal dimensions are between 1 and 2. Db values for years 1953, 1981, 1992, 2000, and 2022 are 1.38, 1.51, 1.55, 1.58, and 1.65, while Di values are 1.37, 1.52, 1.57, 1.60, and 1.67 respectively. The difference between Db and Di values for the same year was small, indicating the similarity between the two methods. Increasing the complexity of the system components and dynamic activities are other indicators of its growth, and there is a significant positive relationship between the fractal dimension value and the complexity level of the city in terms of population growth and area expansion; which increase the importance of fractal theory in studying the growth of the complex system like cities.

An unsupervised machine learning-based profile system of Chinese researchers

The construction of researcher profiles is crucial for modern research management and talent assessment. Given the decentralized nature of researcher information and evaluation challenges, we propose a profile system for Chinese researchers based on unsupervised machine learning and algorithms. This system builds comprehensive profiles based on researchers’ basic and behavior information dimensions. It employs Selenium and Web Crawler for real-time data retrieval from academic platforms, utilizes TF-IDF and BERT for expertise recognition, DTM for academic dynamics, and K-means clustering for profiling. The experimental results demonstrate that these methods are capable of more accurately mining the academic expertise of researchers and performing domain clustering scoring, thereby providing a scientific basis for the selection and academic evaluation of research talents. This interactive analysis system aims to provide an intuitive platform for profile construction and analysis.

Collective Wisdom in Constitutional, Informational and Digital Dimensions: on the Concept and Nature of Popular Deliberative Constitutionalism for Public Law

The article discusses the scientific and intellectual origins of collective wisdom in the constitutional, information and digital dimensions, reveals the doctrinal approaches and normative foundations of popular deliberative constitutionalism, the development of an inclusive constitutional paradigm and its significance for the use of procedures and technologies for citizen participation, expert opinion and civil society institutions in the constitutional development, in social transformations, in constitutional changes brought to life by the needs of society and the state. Popular deliberative constitutionalism is identified in the article as a public legal meta-concept that explains the process and need for constitutional involvement of citizens. The author concludes that popular deliberative constitutionalism allows us to better understand the problems of implementing the principle of popular sovereignty in modern democratic states, create an optimal model and consider the forms of exercising the constituent power of the people when implementing constitutional changes. In the article, the author notes that the future of democratic constitutionalism is associated with the creation and use of collective intelligence technologies in the public legal sphere, which, in the context of the development of an algorithmic and information society, combine classical procedures for discussing constitutional issues and the use of aggregated preferences. The practical application of the achievements of natural and artificial intelligence is intended to discuss and resolve issues of public legal significance, is based on co-evolutionary development and leads to the emergence of mini-publics, civil assemblies, and constitutional crowdsourcing.

Exploring the Impact of E-WOM Information via Social Media on Customer Purchasing Decision: A Mediating Role of Customer Satisfaction

The study investigates the impact of E-WOM information on purchasing decisions, using customer satisfaction as a mediator. It examines E-WOM information as independent variables, such as quality, quantity, and credibility, and as dependent variables, such as consumer satisfaction and purchasing decisions, with customer satisfaction serving as a mediator to investigate the relationship between E-WOM information and purchasing decisions. A questionnaire was issued to 307 social media-active clients, and the hypotheses were tested using quantitative methods. Data analysis comprised descriptive statistics, Cronbach's alpha, skewness and kurtosis, and Pearson correlation coefficient, as well as a fit model for measuring questionnaire reliability and validity, regression for sub-hypotheses, and a path model for evaluating main hypotheses. The findings revealed that all three dimensions of E-WOM information had a positive impact on customer satisfaction and purchase decisions, both individually and jointly. Customer satisfaction has a positive influence on purchasing decisions. Furthermore, E-WOM information has been shown to positively impact purchasing decisions via consumer satisfaction. The study suggests that organizations should understand the dimensions that impact customer satisfaction and purchasing decisions in order to fulfill their goals, remain ahead of the competition, and obtain a competitive advantage. Proper tracking of social media reviews, comments, and recommendations may help organizations deliver answers, increase customer satisfaction, and aid in making purchasing decisions

Optimised GRS coded cooperation: sliding relay selection design and improved joint decoding

ABSTRACT In this paper, the novel optimised generalised Reed-Solomon coded cooperative (OGRSCC) scheme is developed with a flexible selection of the code length and information dimension. In the OGRSCC scheme, two GRS codes with different dimensions are deployed at the source and relay, respectively, where the information symbols of the relay are dependent on those of the source. At the relay, different symbol selections may contribute to distinct joint codes at the destination. As a result, to achieve a maximum free distance (FD) and the optimised weight distribution of the final code, the global search method is first proposed to obtain the optimal selection pattern. However, as the code grows longer, its complexity rises dramatically. To make the selection more practical, the low-complexity sliding search method is then investigated to obtain the local optimal symbol selection pattern. Monte-Carlo simulated results indicate the OGRSCC scheme with two selection methods demonstrates almost identical performance. Furthermore, based on this special selection method and the unique algebraic structure of GRS code, the improved joint decoding algorithm is first proposed to boost the overall system performance. In addition, the theoretical upper bound is analysed, verifying the effectiveness of the proposed OGRSCC scheme. Numerical simulations reveal that the proposed OGRSCC scheme outperforms its corresponding non-cooperative scheme by a gain of over 3.2 dB and its existing comparable schemes by a margin of approximately more than 4 dB at high signal-to-noise ratio (SNR), respectively.

Textual Dimensions of Sustainability Information, Stock Price Informativeness, and Proprietary Costs: Evidence from Integrated Reports

We examine whether integrated report quality, IRQ, is negatively associated with stock price synchronicity, an inverse measure of firm-specific information, and the extent to which the relation between IRQ and synchronicity is attenuated by proprietary costs. We measure IRQ using machine-based textual analysis along four dimensions: textual attributes, topical content, integrated reporting capitals, and financial versus sustainability information. We find that measures of IRQ based on seven textual attributes are negatively related to synchronicity, which is consistent with higher quality text containing more firm-specific content. Using PhraseLDA to identify topics in integrated reports, we find that contents related to the three most common categories—governance, performance, and risks and opportunities—are negatively associated with synchronicity. We find similar results for all integrated report capitals, except manufactured capital. Further, we find that sustainability information has a larger negative association with synchronicity than financial information. We also find that proprietary costs stemming from product market competition attenuate the association between IRQ and synchronicity, which suggests the informativeness of integrated reports varies with a firm’s competitive environment. Our results may inform the International Sustainability Standards Board as it considers the role of the Integrated Reporting Framework in developing sustainability standards.

Comparing the effects of premarital booklet- and video-based educations on the reproductive health literacy of engaged couples.

The aim of this study was to compare the effects of premarital booklet-based education (BBE) and video-based education (VBE) on engaged couples' RHL. This quasi-experimental study was conducted in 2021-2022. The study population consisted of engaged couples who referred to Amirkola Premarital Counseling Center, Babol, Iran, to receive premarital counseling. One hundred and ten couples were purposefully recruited and randomly allocated to a 55-couple BBE group and a 55-couple VBE group. Participants in the BBE group received RH-related education through an educational booklet, while participants in the VBE group received RH-related education through 4 weekly RH-related educational videos shared through WhatsApp. The Sexual and Reproductive Health Literacy questionnaire was used to assess RHL at three measurement time points, i.e., before, 4 weeks after, and 2 months after the intervention onset. The data were analyzed using the SPSS software (v. 23.0). The mean score of RHL significantly increased in both BBE and VBE groups (p < 0.001). The time-group interaction was also significant for the mean scores of the access to information, evaluation of information, and decision-making and application dimensions (p < 0.05) and insignificant for the reading and understanding dimension of RHL (p = 0.56). Both premarital BBE and VBE are effective in significantly improving RHL among engaged couples, while the effects of VBE on some RHL dimensions are significantly more than BBE.

Aspects of effective communication (european experience): digital, informational, and sociolinguistic dimensions

Introduction: The modern era of digital technologies and globalization has brought about significant transformations in communication methods. This article explores how digital, informational, and sociolinguistic dimensions influence the processes of effective communication, focusing particularly on European experiences.Objective: The objective of this study is to analyze the impact of digital transformation, information transparency, linguistic diversity, and cultural adaptation on effective communication in modern European society. The research aims to uncover both the benefits and challenges posed by these factors in the context of communication processes.Methods: This research employed a theoretical approach, synthesizing a wide range of literature and conducting psychological and sociolinguistic analyses. Comparative methods were used to assess different strategies of communication in European contexts, while particular attention was paid to understanding how digitalization and sociolinguistic factors interact.Results: The findings suggest that digitalization enhances accessibility and the speed of communication but introduces challenges, such as the digital divide and data protection issues. Information transparency and accessibility have been identified as crucial in building trust and ensuring the effective exchange of information. Moreover, sociolinguistic factors, especially cultural and language adaptation, have proven essential for effective intercultural communication in Europe.Conclusions: The study concludes that successful communication strategies must incorporate digital, informational, and sociolinguistic aspects, particularly in multilingual and culturally diverse contexts. Awareness and adaptation to cultural norms and linguistic nuances are vital for creating more effective communication practices in both international and local interactions. Practical recommendations include focusing on cultural sensitivity and data transparency to further enhance communication processes

Quantum ghost imaging microscopy depth-of-field study

Quantum ghost imaging approaches have been proposed to enhance biological microscopy, for example, using 2D visible detectors to provide IR images or providing additional dimensions of spatial or spectral information. Toward the goal of making such imaging schemes practical, we compare image quality and depth-of-field between traditional images and ghost images at the same excitation levels. We measure how image quality and depth-of-field depend on the parameters of the entangled light produced using type-I spontaneous parametric down-conversion (SPDC). We use a pair of time-synchronized, photon-timing single-photon avalanche diode (SPAD) array detectors to capture two distinct microscope imaging paths simultaneously on a photon-pair-by-photon-pair basis: one in a traditional imaging pathway and the other a quantum ghost imaging pathway. We calculate the depth-of-field, resolution, contrast, and signal-to-noise ratio (SNR) through the parameter space of a β-Barium Borate (BBO) type-I bulk non-linear crystal length and angle. Our results provide a basis for choosing parameters for quantum ghost imaging with type-I SPDC sources.

SMALE: Hyperspectral Image Classification via Superpixels and Manifold Learning

As an extremely efficient preprocessing tool, superpixels have become more and more popular in various computer vision tasks. Nevertheless, there are still several drawbacks in the application of hyperspectral image (HSl) processing. Firstly, it is difficult to directly apply superpixels because of the high dimension of HSl information. Secondly, existing superpixel algorithms cannot accurately classify the HSl objects due to multi-scale feature categorization. For the processing of high-dimensional problems, we use the principle of PCA to extract three principal components from numerous bands to form three-channel images. In this paper, a novel superpixel algorithm called Seed Extend by Entropy Density (SEED) is proposed to alleviate the seed point redundancy caused by the diversified content of HSl. It also focuses on breaking the dilemma of manually setting the number of superpixels to overcome the difficulty of classification imprecision caused by multi-scale targets. Next, a space–spectrum constraint model, termed Hyperspectral Image Classification via superpixels and manifold learning (SMALE), is designed, which integrates the proposed SEED to generate a dimensionality reduction framework. By making full use of spatial context information in the process of unsupervised dimension reduction, it could effectively improve the performance of HSl classification. Experimental results show that the proposed SEED could effectively promote the classification accuracy of HSI. Meanwhile, the integrated SMALE model outperforms existing algorithms on public datasets in terms of several quantitative metrics.

Factors influencing user's health information discernment abilities in online health communities: based on SEM and fsQCA.

Online health communities have become the main source for people to obtain health information. However, the existence of poor-quality health information, misinformation, and rumors in online health communities increases the challenges in governing information quality. It not only affects users' health decisions but also undermines social stability. It is of great significance to explore the factors that affect users' ability to discern information in online health communities. This study integrated the Stimulus-Organism-Response Theory, Information Ecology Theory and the Mindsponge Theory to constructed a model of factors influencing users' health information discernment abilities in online health communities. A questionnaire was designed based on the variables in the model, and data was collected. Utilizing Structural Equation Modeling (SEM) in conjunction with fuzzy-set Qualitative Comparative Analysis (fsQCA), the study analyzed the complex causal relationships among stimulus factors, user perception, and the health information discernment abilities. The results revealed that the dimensions of information, information environment, information technology, and information people all positively influenced health information discernment abilities. Four distinct configurations were identified as triggers for users' health information discernment abilities. The core conditions included information source, informational support, technological security, technological facilitation, and perceived risk. It was also observed that information quality and emotional support can act as substitutes for one another, as can informational support and emotional support. This study provides a new perspective to study the influencing factors of health information discernment abilities of online health community users. It can provide experiences and references for online health community information services, information resource construction and the development of users' health information discernment abilities.

Applying Insights from China: A Typology for Subnational Comparative Politics

AbstractIn this study, we draw on theories of subnational analysis and comparison to introduce a novel framework for better understanding central-local dynamics in China. We argue that careful consideration along the dimensions of interests and information of national and subnational actors can allow us to predict how the central state evaluates local actors, variation in local implementation, and the potential for findings to be generalized to settings beyond China. We apply this 2$$\times$$ × 2 typology to several policy areas—social stability, social security, environmental governance, and economic development—to derive expectations about the behavior of central and local states, and identify when researchers can (and cannot) treat subnational units as independent observations. By identifying the underlying dynamics of central-local relations in different policy domains, this typology provides researchers with tools to construct a more precise analytical framework and helps scholars of Chinese politics conducting subnational analyses design research agendas better able to contribute to the larger comparative politics literature.

Scientific exploration and hypotheses concerning the meridian system in traditional Chinese medicine

The meridian theory is an important component of traditional Chinese medicine, playing a crucial role in disease diagnosis, treatment, and health preservation. Serving as the media for the effects of acupuncture, moxibustion, herbal medicine, and acupressure massage, meridians exert undeniable impact on the human body. However, the essence of meridians remains a topic of debate. Recent research has primarily focused on their anatomical structures, leading to numerous hypotheses. Simultaneously, other researchers have approached this subject from an energetic perspective, discovering information interactions within the meridian system. These findings suggest that meridians possess both physical and information dimensions, indicating that a singular approach to their study is insufficient. To bridge this gap, a shift from purely structural research toward an exploration of the information aspects of meridians is necessary. By integrating this information approach with traditional meridian theory, it may be possible to develop a new, modernized meridian theory that is aligned with contemporary concepts, making it more accessible and applicable in clinical settings.

Cardiac-Sympathetic Contractility and Neural Alpha-Band Power: Cross-Modal Collaboration during Approach-Avoidance Conflict.

As evidence mounts that the cardiac-sympathetic nervous system reacts to challenging cognitive settings, we ask if these responses are epiphenomenal companions or if there is evidence suggesting a more intertwined role of this system with cognitive function. Healthy male and female human participants performed an approach-avoidance paradigm, trading off monetary reward for painful electric shock, while we recorded simultaneous electroencephalographic and cardiac-sympathetic signals. Participants were reward sensitive but also experienced approach-avoidance "conflict" when the subjective appeal of the reward was near equivalent to the revulsion of the cost. Drift-diffusion model parameters suggested that participants managed conflict in part by integrating larger volumes of evidence into choices (wider decision boundaries). Late alpha-band (neural) dynamics were consistent with widening decision boundaries serving to combat reward sensitivity and spread attention more fairly to all dimensions of available information. Independently, wider boundaries were also associated with cardiac "contractility" (an index of sympathetically mediated positive inotropy). We also saw evidence of conflict-specific "collaboration" between the neural and cardiac-sympathetic signals. In states of high conflict, the alignment (i.e., product) of alpha dynamics and contractility were associated with a further widening of the boundary, independent of either signal's singular association. Cross-trial coherence analyses provided additional evidence that the autonomic systems controlling cardiac-sympathetics might influence the assessment of information streams during conflict by disrupting or overriding reward processing. We conclude that cardiac-sympathetic control might play a critical role, in collaboration with cognitive processes, during the approach-avoidance conflict in humans.

A nationwide digital maturity assessment of hospitals – Results from the German DigitalRadar

ObjectivesIn 2019, the German government established the Hospital Future Fund, allocating 4.3 billion Euros, to support investments in the digital infrastructure of hospitals. The DigitalRadar consortium was commissioned by the German Ministry of Health in 2020 to develop a holistic digital maturity model and evaluate the current state of digitalization and the impact of the funding program. To date, the nationwide digitalization of German hospitals has remained a relatively understudied phenomenon. This study aims to address this gap in knowledge by examining the influence of various factors identified by the DigitalRadar maturity model on the digital maturity of hospitals in Germany. In doing so, it seeks to elucidate the implications these findings have for the development of a digital, patient-centred, safe, and high-quality hospital landscape in the country. MethodsThe model was developed through a scoping review of digital maturity models, requirements set forth in the Hospital Future Act, analysis of components from existing models and feedback from a sounding board. Ultimately, the model includes 234 questions (items) categorized into 7 dimensions of digitalization. It was piloted in 12 hospitals and revised accordingly. 1,624 hospitals (91% of all German hospitals) participated in this self-assessment, as participation was mandatory to receive funding. ResultsThe average DigitalRadar score on a 100-point scale is 33. Maturity is comparatively high in the structures and systems dimension, but low in the clinical processes, exchange of information, telemedicine and patient participation dimensions, suggesting that data exchange is hampered by a lack of interoperability. Drivers of digital maturity are teaching status, size, connectivity, and level of emergency services. ConclusionsThe transparency gained allows hospitals and regulators to identify areas for improvement and develop digital strategies. Additionally, it enables researchers to analyse, for example, the correlation between digitalization and the quality of care, as well as the mechanisms of action of large-scale funding programs for hospital digitization.

Community-Based Resilience Analysis (CoBRA) to Hazard Disruption: Case Study of a Peri-Urban Agricultural Community in Thailand

The expansion of cities and land use changes have led to the emergence of peri-urban areas representing a transition between fully urbanized and agricultural regions in Southeast Asia. Peri-urban communities provide essential ecosystem services but are vulnerable to climate-related disruptions and socioeconomic challenges. Utilizing their unique characteristics, peri-urban communities can contribute to sustainable development and resilience. This study assesses the potential of peri-urban areas to meet future challenges for sustainable development in a changing world, focusing on the local pandan farming community of Pathum Thani, approximately 53 km north of Bangkok, using the Community-Based Resilience Analysis (CoBRA) approach. A formally established group of peri-urban farming households identified COVID-19, water quality, and solid waste as their primary disruptive challenges. The community identified economic stability and resources (land ownership, financial security, and government support), community and social support (collaborative community, and healthcare facilities), an environmental dimension (sufficient food and clean water), and an information dimension (news and knowledge update) as key community resilience characteristics, which highlight their comprehensive approach to hazard resilience. The study concludes that the community was moderately resilient to hazards and COVID-19 was the primary disrupting event over the past 10 years. To address future challenges in peri-urban agriculture, it is suggested to focus on enhancing economic diversification, strengthening social networks and support systems, implementing sustainable land management practices, and promoting access to timely and accurate information. Additionally, investing in infrastructure for water management and waste recycling, supporting small-scale farming initiatives, and fostering collaboration between farmers and local authorities can contribute to building resilience in peri-urban agricultural communities.

YOLO-FD: An accurate fish disease detection method based on multi-task learning

Fish diseases often exhibit high risks of contagion, resulting in substantial economic losses. Accurate assessment of fish disease severity during diagnosis using deep learning poses a considerable challenge. Currently, deep learning models mainly focus on single tasks in fish disease detection, such as classification, object detection and segmentation. However, the accurate assessment of fish disease severity requires the integration of multiple dimensions of information, which is beyond the capabilities of traditional single-task methods. Therefore, this paper proposes YOLO-FD, a multi-task learning network specifically designed for simultaneous detection and segmentation. YOLO-FD extends the YOLOv8 backbone by integrating a novel semantic segmentation branch dedicated to precisely segmenting infected areas in diseased fish, while retaining the original object detection branch for identifying infected fish. Weight uncertainty and PCGrad are employed to balance the weights of different losses and to optimize conflicting gradients during the training process. With only a negligible increase in network parameters, YOLO-FD, tested on our constructed Nocardiosis fish dataset, achieves a detection accuracy of 94.2% mAP50 and gets mIOU of 79.4%, showcasing a 0.5% improvement over the baseline YOLOv8 and surpassing the state-of-the-art semantic segmentation network Deeplabv3plus by 4%. Notably, compared to the adapted multi-task network YOLOP, YOLO-FD demonstrates substantial improvements, displaying a 13.7% increase in mAP50-95 and a 15.1% boost in mIOU. On the VOC2012 segmentation dataset, the proposed method exhibits a 3.2% increase in mAP50 and a 2.2% rise in mAP50-95 compared to the baseline. Furthermore, results of the ablation experiment validate the effectiveness and generalization of the proposed multi-task learning approach. Source code is available at https://github.com/feifei-Lee/YOLO-FD.

Tiramisù: making sense of multi-faceted process information through time and space

AbstractKnowledge-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.