MaVis: Machine Learning Aided Multi-Model Framework for Time Series Visual Analytics

Zhao, Kaiyu; Ward, Matthew O.; Rundensteiner, Elke A.; Higgins, Huong N.

doi:10.2352/issn.2470-1173.2016.1.vda-493

Cited by 6 publications

(4 citation statements)

References 45 publications

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“…As a result, ontological datasets can become very large and complex, supporting countless complex objects describing ontology entities and relations. When interacting with highly complex spaces like ontologies, the limitations of human cognition can create a bottleneck in human-facing analytic workflows [23]. Therefore, a leading challenge for those who look to use ontologies is maintaining an ontology dataset which accurately describes its domain while still being useful for both computation and human-facing tasks.…”

Section: Ontologiesmentioning

confidence: 99%

Forming Cognitive Maps of Ontologies Using Interactive Visualizations

Demelo

Sedig

2021

MTI

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Ontology datasets, which encode the expert-defined complex objects mapping the entities, relations, and structures of a domain ontology, are increasingly being integrated into the performance of challenging knowledge-based tasks. Yet, it is hard to use ontology datasets within our tasks without first understanding the ontology which it describes. Using visual representation and interaction design, interactive visualization tools can help us learn and develop our understanding of unfamiliar ontologies. After a review of existing tools which visualize ontology datasets, we find that current design practices struggle to support learning tasks when attempting to build understanding of the ontological spaces within ontology datasets. During encounters with unfamiliar spaces, our cognitive processes align with the theoretical framework of cognitive map formation. Furthermore, designing encounters to promote cognitive map formation can improve our performance during learning tasks. In this paper, we examine related work on cognitive load, cognitive map formation, and the use of interactive visualizations during learning tasks. From these findings, we formalize a set of high-level design criteria for visualizing ontology datasets to promote cognitive map formation during learning tasks. We then perform a review of existing tools which visualize ontology datasets and assess their interface design towards their alignment with the cognitive map framework. We then present PRONTOVISE (PRogressive ONTOlogy VISualization Explorer), an interactive visualization tool which applies the high-level criteria within its design. We perform a task-based usage scenario to illustrate the design of PRONTOVISE. We conclude with a discussion of the implications of PRONTOVISE and its use of the criteria towards the design of interactive visualization tools which help us develop understanding of the ontological space within ontology datasets.

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Section: Ontologiesmentioning

confidence: 99%

Forming Cognitive Maps of Ontologies Using Interactive Visualizations

Demelo

Sedig

2021

MTI

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“…By combining visualizations, interaction mechanisms, ML techniques, and analytical models, VASes are capable of providing both computational and cognitive possibilities [6,48,49]. Not only through these possibilities is the analyst equipped with more robust analytical tools, but also a cognitive coupling of the system and the human analyst is created [28][29][30]42,45].…”

Section: Visual Analyticsmentioning

confidence: 99%

“…Since ML techniques help in mitigating and reducing the cognitive load of analysts' data-intensive tasks, their use in VASes is strongly advocated [30,48,[50][51][52]. Theoretically, in a VAS, the combination of these techniques with data processing techniques, as a whole, can be regarded as the analytics engine.…”

Section: Visual Analyticsmentioning

confidence: 99%

VARTTA: A Visual Analytics System for Making Sense of Real-Time Twitter Data

Haghighati

Sedig

2020

Data

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Through social media platforms, massive amounts of data are being produced. As a microblogging social media platform, Twitter enables its users to post short updates as “tweets” on an unprecedented scale. Once analyzed using machine learning (ML) techniques and in aggregate, Twitter data can be an invaluable resource for gaining insight into different domains of discussion and public opinion. However, when applied to real-time data streams, due to covariate shifts in the data (i.e., changes in the distributions of the inputs of ML algorithms), existing ML approaches result in different types of biases and provide uncertain outputs. In this paper, we describe VARTTA (Visual Analytics for Real-Time Twitter datA), a visual analytics system that combines data visualizations, human-data interaction, and ML algorithms to help users monitor, analyze, and make sense of the streams of tweets in a real-time manner. As a case study, we demonstrate the use of VARTTA in political discussions. VARTTA not only provides users with powerful analytical tools, but also enables them to diagnose and to heuristically suggest fixes for the errors in the outcome, resulting in a more detailed understanding of the tweets. Finally, we outline several issues to be considered while designing other similar visual analytics systems.

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“…For a long time, its functionality was concentrated on exploratory manipulation of records in these visualizations. Recently, its focus has been extended to support data mining (version 9.0, 2015), including interactive parameter space exploration for association rules [9], interactive pattern exploration in streaming [10], and time series [11].…”

Section: Introductionmentioning

confidence: 99%

Constructing Interactive Visual Classification, Clustering and Dimension Reduction Models for n-D Data

Kovalerchuk

Dovhalets

2017

Informatics

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Abstract:The exploration of multidimensional datasets of all possible sizes and dimensions is a long-standing challenge in knowledge discovery, machine learning, and visualization. While multiple efficient visualization methods for n-D data analysis exist, the loss of information, occlusion, and clutter continue to be a challenge. This paper proposes and explores a new interactive method for visual discovery of n-D relations for supervised learning. The method includes automatic, interactive, and combined algorithms for discovering linear relations, dimension reduction, and generalization for non-linear relations. This method is a special category of reversible General Line Coordinates (GLC). It produces graphs in 2-D that represent n-D points losslessly, i.e., allowing the restoration of n-D data from the graphs. The projections of graphs are used for classification. The method is illustrated by solving machine-learning classification and dimension-reduction tasks from the domains of image processing, computer-aided medical diagnostics, and finance. Experiments conducted on several datasets show that this visual interactive method can compete in accuracy with analytical machine learning algorithms.

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MaVis: Machine Learning Aided Multi-Model Framework for Time Series Visual Analytics

Cited by 6 publications

References 45 publications

Forming Cognitive Maps of Ontologies Using Interactive Visualizations

Forming Cognitive Maps of Ontologies Using Interactive Visualizations

VARTTA: A Visual Analytics System for Making Sense of Real-Time Twitter Data

Constructing Interactive Visual Classification, Clustering and Dimension Reduction Models for n-D Data

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