44 research outputs found
From Data to Knowledge Graphs: A Multi-Layered Method to Model User's Visual Analytics Workflow for Analytical Purposes
The importance of knowledge generation drives much of Visual Analytics (VA).
User-tracking and behavior graphs have shown the value of understanding users'
knowledge generation while performing VA workflows. Works in theoretical
models, ontologies, and provenance analysis have greatly described means to
structure and understand the connection between knowledge generation and VA
workflows. Yet, two concepts are typically intermixed: the temporal aspect,
which indicates sequences of events, and the atemporal aspect, which indicates
the workflow state space. In works where these concepts are separated, they do
not discuss how to analyze the recorded user's knowledge gathering process when
compared to the VA workflow itself. This paper presents Visual Analytic
Knowledge Graph (VAKG), a conceptual framework that generalizes existing
knowledge models and ontologies by focusing on how humans relate to computer
processes temporally and how it relates to the workflow's state space. Our
proposal structures this relationship as a 4-way temporal knowledge graph with
specific emphasis on modeling the human and computer aspect of VA as separate
but interconnected graphs for, among others, analytical purposes. We compare
VAKG with relevant literature to show that VAKG's contribution allows VA
applications to use it as a provenance model and a state space graph, allowing
for analytics of domain-specific processes, usage patterns, and users'
knowledge gain performance. We also interviewed two domain experts to check, in
the wild, whether real practice and our contributions are aligned.Comment: 9 pgs, submitted to VIS 202
Overlap Removal of Dimensionality Reduction Scatterplot Layouts
Dimensionality Reduction (DR) scatterplot layouts have become a ubiquitous
visualization tool for analyzing multidimensional data items with presence in
different areas. Despite its popularity, scatterplots suffer from occlusion,
especially when markers convey information, making it troublesome for users to
estimate items' groups' sizes and, more importantly, potentially obfuscating
critical items for the analysis under execution. Different strategies have been
devised to address this issue, either producing overlap-free layouts, lacking
the powerful capabilities of contemporary DR techniques in uncover interesting
data patterns, or eliminating overlaps as a post-processing strategy. Despite
the good results of post-processing techniques, the best methods typically
expand or distort the scatterplot area, thus reducing markers' size (sometimes)
to unreadable dimensions, defeating the purpose of removing overlaps. This
paper presents a novel post-processing strategy to remove DR layouts' overlaps
that faithfully preserves the original layout's characteristics and markers'
sizes. We show that the proposed strategy surpasses the state-of-the-art in
overlap removal through an extensive comparative evaluation considering
multiple different metrics while it is 2 or 3 orders of magnitude faster for
large datasets.Comment: 11 pages and 9 figure
Explainable Patterns: Going from Findings to Insights to Support Data Analytics Democratization
In the past decades, massive efforts involving companies, non-profit
organizations, governments, and others have been put into supporting the
concept of data democratization, promoting initiatives to educate people to
confront information with data. Although this represents one of the most
critical advances in our free world, access to data without concrete facts to
check or the lack of an expert to help on understanding the existing patterns
hampers its intrinsic value and lessens its democratization. So the benefits of
giving full access to data will only be impactful if we go a step further and
support the Data Analytics Democratization, assisting users in transforming
findings into insights without the need of domain experts to promote
unconstrained access to data interpretation and verification. In this paper, we
present Explainable Patterns (ExPatt), a new framework to support lay users in
exploring and creating data storytellings, automatically generating plausible
explanations for observed or selected findings using an external (textual)
source of information, avoiding or reducing the need for domain experts. ExPatt
applicability is confirmed via different use-cases involving world demographics
indicators and Wikipedia as an external source of explanations, showing how it
can be used in practice towards the data analytics democratization.Comment: 8 Figures, 10 pages, submitted to VIS 202
Multivariate Data Explanation by Jumping Emerging Patterns Visualization
Visual Analytics (VA) tools and techniques have been instrumental in
supporting users to build better classification models, interpret models'
overall logic, and audit results. In a different direction, VA has recently
been applied to transform classification models into descriptive mechanisms
instead of predictive. The idea is to use such models as surrogates for data
patterns, visualizing the model to understand the phenomenon represented by the
data. Although very useful and inspiring, the few proposed approaches have
opted to use low complex classification models to promote straightforward
interpretation, presenting limitations to capture intricate data patterns. In
this paper, we present VAX (multiVariate dAta eXplanation), a new VA method to
support the identification and visual interpretation of patterns in
multivariate datasets. Unlike the existing similar approaches, VAX uses the
concept of Jumping Emerging Patterns to identify and aggregate several
diversified patterns, producing explanations through logic combinations of data
variables. The potential of VAX to interpret complex multivariate datasets is
demonstrated through use-cases employing two real-world datasets covering
different scenarios
From Data to Knowledge Graphs: A Multi-Layered Method to Model User's Visual Analytics Workflow for Analytical Purposes
The primary goal of Visual Analytics (VA) is knowledge generation. In this process, VA knowledge models and ontologies have shown to be beneficial to better understand how users obtain new insights when executing a VA workflow. Yet, the gap between theoretical models and the practice of knowledge generation analysis is wide, and theory has mainly been used as a baseline for practical works. Also, two concepts are typically ambiguous and intermixed when analyzing VA workflows: the temporal aspect, which indicates sequences of events, and the atemporal aspect, which indicates the workflow's state-space, which is the set of all states of the VA tool and its user occupied during a VA workflow. Also, the lack of guidelines on how to analyze the recorded user's knowledge-gathering process when compared to the VA workflow itself is apparent. We bridge this gap by presenting Visual Analytics Knowledge Graph (VAKG), a conceptual framework to bridge the gap between VA workflow modeling theory and application. Through a novel Set-Theory formalization of knowledge modeling, VAKG structures a VA workflow by temporal sequences of human and machine changes over time and how they relate to the workflow's state-space. This structure is then used as a schema for storing VA workflow data and can be used to analyze user behavior and knowledge generation. VAKG is designed following the needs and limitations of relevant literature, allowing for modeling, structuring, storing, and providing analysis guidelines for user behavior and knowledge generation, enabling comparison of users and VA tools
HiPP: A Novel Hierarchical Point Placement Strategy and its Application to the Exploration of Document Collections
Point placement strategies aim at mapping data points represented in higher dimensions to bi-dimensional spaces and are frequently used to visualize relationships amongst data instances. They have been valuable tools for analysis and exploration of data sets of various kinds. Many conventional techniques, however, do not behave well when the number of dimensions is high, such as in the case of documents collections. Later approaches handle that shortcoming, but may cause too much clutter to allow flexible exploration to take place. In this work we present a novel hierarchical point placement technique that is capable of dealing with these problems. While good grouping and separation of data with high similarity is maintained without increasing computation cost, its hierarchical structure lends itself both to exploration in various levels of detail and to handling data in subsets, improving analysis capability and also allowing manipulation of larger data sets.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)FAPESP research financial agency, Sao Paulo, Brazil[04/07866-4]FAPESP research financial agency, Sao Paulo, Brazil[04/09888-5]Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)CAPES research financial agency, Brazil[2214-07-5]Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)CNPq research financial, Brazil[304758/2005-1]CNPq research financial, Brazil[484256/2007-6]Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq