381 research outputs found
Interactive visualization of event logs for cybersecurity
Hidden cyber threats revealed with new visualization software Eventpa
Reusable Annotations for Matching of Event Sequences to Construct Firewall Policies
Organizations of all types use firewall systems to protect their networks from threats. Those firewalls are governed by the policies used to configure them. The PEACE (Policy Enforcement and Access Control for End-points) system is a new combination, network-plus-host based firewall that gives analysts a novel new set of data to build policy attributes for. This data are semi-structured strings that represent the hierarchy of graphical user interface components that have been interacted with around the time that host sent a network request. The multivariate, hierarchical, semi-structured nature of this data can make it a laborious or non-intuitive task to create the string matching rules that are used by the firewall policies. We present a targeted, interactive, event-sequence based \cite{cappers2017exploring} tool for the purpose of building policies for the PEACE firewall system\u27s graphical user interface data
FlexEvent:going beyond Case-Centric Exploration and Analysis of Multivariate Event Sequences
In many domains, multivariate event sequence data is collected focused around an entity (the case). Typically, each event has multiple attributes, for example, in healthcare a patient has events such as hospitalization, medication, and surgery. In addition to the multivariate events, also the case (a specific attribute, e.g., patient) has associated multivariate data (e.g., age, gender, weight). Current work typically only visualizes one attribute per event (label) in the event sequences. As a consequence, events can only be explored from a predefined case-centric perspective. However, to find complex relations from multiple perspectives (e.g., from different case definitions, such as doctor), users also need an event- and attribute-centric perspective. In addition, support is needed to effortlessly switch between and within perspectives. To support such a rich exploration, we present FlexEvent: an exploration and analysis method that enables investigation beyond a fixed case-centric perspective. Based on an adaptation of existing visualization techniques, such as scatterplots and juxtaposed small multiples, we enable flexible switching between different perspectives to explore the multivariate event sequence data needed to answer multi-perspective hypotheses. We evaluated FlexEvent with three domain experts in two use cases with sleep disorder and neonatal ICU data that show our method facilitates experts in exploring and analyzing real-world multivariate sequence data from different perspectives
Visual Analysis of High-Dimensional Event Sequence Data via Dynamic Hierarchical Aggregation
Temporal event data are collected across a broad range of domains, and a
variety of visual analytics techniques have been developed to empower analysts
working with this form of data. These techniques generally display aggregate
statistics computed over sets of event sequences that share common patterns.
Such techniques are often hindered, however, by the high-dimensionality of many
real-world event sequence datasets because the large number of distinct event
types within such data prevents effective aggregation. A common coping strategy
for this challenge is to group event types together as a pre-process, prior to
visualization, so that each group can be represented within an analysis as a
single event type. However, computing these event groupings as a pre-process
also places significant constraints on the analysis. This paper presents a
dynamic hierarchical aggregation technique that leverages a predefined
hierarchy of dimensions to computationally quantify the informativeness of
alternative levels of grouping within the hierarchy at runtime. This allows
users to dynamically explore the hierarchy to select the most appropriate level
of grouping to use at any individual step within an analysis. Key contributions
include an algorithm for interactively determining the most informative set of
event groupings from within a large-scale hierarchy of event types, and a
scatter-plus-focus visualization that supports interactive hierarchical
exploration. While these contributions are generalizable to other types of
problems, we apply them to high-dimensional event sequence analysis using
large-scale event type hierarchies from the medical domain. We describe their
use within a medical cohort analysis tool called Cadence, demonstrate an
example in which the proposed technique supports better views of event sequence
data, and report findings from domain expert interviews.Comment: To Appear in IEEE Transactions on Visualization and Computer Graphics
(TVCG), Volume 26 Issue 1, 2020. Also part of proceedings for IEEE VAST 201
DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways
Clinical researchers use disease progression models to understand patient
status and characterize progression patterns from longitudinal health records.
One approach for disease progression modeling is to describe patient status
using a small number of states that represent distinctive distributions over a
set of observed measures. Hidden Markov models (HMMs) and its variants are a
class of models that both discover these states and make inferences of health
states for patients. Despite the advantages of using the algorithms for
discovering interesting patterns, it still remains challenging for medical
experts to interpret model outputs, understand complex modeling parameters, and
clinically make sense of the patterns. To tackle these problems, we conducted a
design study with clinical scientists, statisticians, and visualization
experts, with the goal to investigate disease progression pathways of chronic
diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's
disease, and chronic obstructive pulmonary disease (COPD). As a result, we
introduce DPVis which seamlessly integrates model parameters and outcomes of
HMMs into interpretable and interactive visualizations. In this study, we
demonstrate that DPVis is successful in evaluating disease progression models,
visually summarizing disease states, interactively exploring disease
progression patterns, and building, analyzing, and comparing clinically
relevant patient subgroups.Comment: to appear at IEEE Transactions on Visualization and Computer Graphic
Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining
Personalized head and neck cancer therapeutics have greatly improved survival
rates for patients, but are often leading to understudied long-lasting symptoms
which affect quality of life. Sequential rule mining (SRM) is a promising
unsupervised machine learning method for predicting longitudinal patterns in
temporal data which, however, can output many repetitive patterns that are
difficult to interpret without the assistance of visual analytics. We present a
data-driven, human-machine analysis visual system developed in collaboration
with SRM model builders in cancer symptom research, which facilitates
mechanistic knowledge discovery in large scale, multivariate cohort symptom
data. Our system supports multivariate predictive modeling of post-treatment
symptoms based on during-treatment symptoms. It supports this goal through an
SRM, clustering, and aggregation back end, and a custom front end to help
develop and tune the predictive models. The system also explains the resulting
predictions in the context of therapeutic decisions typical in personalized
care delivery. We evaluate the resulting models and system with an
interdisciplinary group of modelers and head and neck oncology researchers. The
results demonstrate that our system effectively supports clinical and symptom
research
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COPE: Interactive Exploration of Co-occurrence Patterns in Spatial Time Series.
Spatial time series is a common type of data dealt with in many domains, such as economic statistics and environmental science. There have been many studies focusing on finding and analyzing various kinds of events in time series; the term 'event' refers to significant changes or occurrences of particular patterns formed by consecutive attribute values. We focus on a further step in event analysis: finding and exploring events that frequently co-occurred with a target class of similar events having occurred repeatedly over a period of time. This type of analysis can provide important clues for understanding the formation and spreading mechanisms of events and interdependencies among spatial locations. We propose a visual exploration framework COPE (Co-Occurrence Pattern Exploration), which allows users to extract events of interest from data and detect various co-occurrence patterns among them. Case studies and expert reviews were conducted to verify the effectiveness and scalability of COPE using two real-world datasets
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