Visual Analytics: Definition, Process, and Challenges

Keim, Daniel A.; Andrienko, Gennady; Fekete, Jean-Daniel; Görg, Carsten; Kohlhammer, Jörn; Mélançon, Guy

doi:10.1007/978-3-540-70956-5_7

Cited by 823 publications

(646 citation statements)

References 26 publications

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“…Several research works proposed the integration of data mining technology into decision support systems [13]. They confirm that the combination of data mining tools and decision support systems improved decision-making quality.…”

Section: A Visual Data Mining For Decision-makingmentioning

confidence: 81%

“…Visual data mining process span from human analytic tasks using domain knowledge to automatic tasks using data mining algorithms. Such visual analytic follows a specific process [13]. Figure2shows the different stages in this process.…”

Section: A Visual Data Mining For Decision-makingmentioning

confidence: 99%

“…2. Knowledege visualization framework [13] The visual analytics process gives the authority to the user to guide the analysis methods during the execution of his/her tasks from raw data to decision. It provides collaboration between automatic patterns extraction and visualization (of data and patterns) across different abstraction levels.…”

Section: A Visual Data Mining For Decision-makingmentioning

confidence: 99%

See 2 more Smart Citations

Visual Knowledge Generation from Data Mining Patterns for Decision-Making

Elouni¹,

Ltifi²,

Ayed³

et al. 2016

ijacsa

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Abstract-The visual data mining based decision support systems had already been recognized in literature. It allows users analysing large information spaces to support complex decisionmaking. Prior research provides frameworks focused on simply representing extracted patterns. In this paper, we present a new model for visually generating knowledge from these patterns and communicating it for intelligent decision-making. To prove the practicality of the proposed model, it was applied in the medical field to fight against nosocomial infections in the intensive care units.

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Section: A Visual Data Mining For Decision-makingmentioning

confidence: 81%

Section: A Visual Data Mining For Decision-makingmentioning

confidence: 99%

Section: A Visual Data Mining For Decision-makingmentioning

confidence: 99%

See 1 more Smart Citation

Visual Knowledge Generation from Data Mining Patterns for Decision-Making

Elouni¹,

Ltifi²,

Ayed³

et al. 2016

ijacsa

View full text Add to dashboard Cite

show abstract

“…Visual analytics provides a valuable tool that complements advanced human perception and analytical ability with automated data processing and mining. This lowers human cognitive load in complex data analysis while improving the recognition of patterns and information hidden within data [1,2,4,28,29], support especially valuable in the analysis of complex, high-dimensional data.…”

Section: Visual Ontology-guided Exploration and Analysismentioning

confidence: 99%

“…However, screen real estate and resolution, processing power and human cognition place limits on how much data may be usefully displayed at once [1,28]. Beyond approximately 5000 postings interactivity in the web-based multi-dimensional parallel coordinates plot deteriorates significantly.…”

Section: Task 1-job Role Creation Activitymentioning

confidence: 99%

Structuring visual exploratory analysis of skill demand

Dadzie

Sibarani

Novalija

et al. 2018

Journal of Web Semantics

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a b s t r a c tThe analysis of increasingly large and diverse data for meaningful interpretation and question answering is handicapped by human cognitive limitations. Consequently, semi-automatic abstraction of complex data within structured information spaces becomes increasingly important, if its knowledge content is to support intuitive, exploratory discovery. Exploration of skill demand is an area where regularly updated, multi-dimensional data may be exploited to assess capability within the workforce to manage the demands of the modern, technology-and data-driven economy. The knowledge derived may be employed by skilled practitioners in defining career pathways, to identify where, when and how to update their skillsets in line with advancing technology and changing work demands. This same knowledge may also be used to identify the combination of skills essential in recruiting for new roles. To address the challenges inherent in exploring the complex, heterogeneous, dynamic data that feeds into such applications, we investigate the use of an ontology to guide structuring of the information space, to allow individuals and institutions to interactively explore and interpret the dynamic skill demand landscape for their specific needs. As a test case we consider the relatively new and highly dynamic field of Data Science, where insightful, exploratory data analysis and knowledge discovery are critical. We employ context-driven and task-centred scenarios to explore our research questions and guide iterative design, development and formative evaluation of our ontology-driven, visual exploratory discovery and analysis approach, to measure where it adds value to users' analytical activity. Our findings reinforce the potential in our approach, and point us to future paths to build on.

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Decision‐Making Models, Decision Support, and Problem Solving

Lehto

Nah

2012

Handbook of Human Factors and Ergonomics

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Visual Analytics: Definition, Process, and Challenges

Cited by 823 publications

References 26 publications

Visual Knowledge Generation from Data Mining Patterns for Decision-Making

Visual Knowledge Generation from Data Mining Patterns for Decision-Making

Structuring visual exploratory analysis of skill demand

Decision‐Making Models, Decision Support, and Problem Solving

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