64,510 research outputs found
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Όλ¬Έ(λ°μ¬)--μμΈλνκ΅ λνμ :곡과λν μ»΄ν¨ν°κ³΅νλΆ,2020. 2. μμ§μ±.Understanding data through interactive visualization, also known as visual analytics, is a common and necessary practice in modern data science. However, as data sizes have increased at unprecedented rates, the computation latency of visualization systems becomes a significant hurdle to visual analytics. The goal of this dissertation is to design a series of systems for progressive visual analytics (PVA)βa visual analytics paradigm that can provide intermediate results during computation and allow visual exploration of these resultsβto address the scalability hurdle. To support the interactive exploration of data with billions of records, we first introduce SwiftTuna, an interactive visualization system with scalable visualization and computation components. Our performance benchmark demonstrates that it can handle data with four billion records, giving responsive feedback every few seconds without precomputation. Second, we present PANENE, a progressive algorithm for the Approximate k-Nearest Neighbor (AKNN) problem. PANENE brings useful machine learning methods into visual analytics, which has been challenging due to their long initial latency resulting from AKNN computation. In particular, we accelerate t-Distributed Stochastic Neighbor Embedding (t-SNE), a popular non-linear dimensionality reduction technique, which enables the responsive visualization of data with a few hundred columns. Each of these two contributions aims to address the scalability issues stemming from a large number of rows or columns in data, respectively. Third, from the users' perspective, we focus on improving the trustworthiness of intermediate knowledge gained from uncertain results in PVA. We propose a novel PVA concept, Progressive Visual Analytics with Safeguards, and introduce PVA-Guards, safeguards people can leave on uncertain intermediate knowledge that needs to be verified. We also present a proof-of-concept system, ProReveal, designed and developed to integrate seven safeguards into progressive data exploration. Our user study demonstrates that people not only successfully created PVA-Guards on ProReveal but also voluntarily used PVA-Guards to manage the uncertainty of their knowledge. Finally, summarizing the three studies, we discuss design challenges for progressive systems as well as future research agendas for PVA.νλ λ°μ΄ν° μ¬μ΄μΈμ€μμ μΈν°λν°λΈν μκ°νλ₯Ό ν΅ν΄ λ°μ΄ν°λ₯Ό μ΄ν΄νλ κ²μ νμμ μΈ λΆμ λ°©λ² μ€ νλμ΄λ€. κ·Έλ¬λ, μ΅κ·Ό λ°μ΄ν°μ ν¬κΈ°κ° νλ°μ μΌλ‘ μ¦κ°νλ©΄μ λ°μ΄ν° ν¬κΈ°λ‘ μΈν΄ λ°μνλ μ§μ° μκ°μ΄ μΈν°λν°λΈν μκ°μ λΆμμ ν° κ±Έλ¦Όλμ΄ λμλ€. λ³Έ μ°κ΅¬μμλ μ΄λ¬ν νμ₯μ± λ¬Έμ λ₯Ό ν΄κ²°νκΈ° μν΄ μ μ§μ μκ°μ λΆμ(Progressive Visual Analytics)μ μ§μνλ μΌλ ¨μ μμ€ν
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μ ꡬνν λμ λμμΈμ λμ μ ν₯ν μ°κ΅¬ λ°©ν₯μ λͺ¨μνλ€.CHAPTER1. Introduction 2
1.1 Background and Motivation 2
1.2 Thesis Statement and Research Questions 5
1.3 Thesis Contributions 5
1.3.1 Responsive and Incremental Visual Exploration of Large-scale Multidimensional Data 6
1.3.2 ProgressiveComputation of Approximate k-Nearest Neighbors and Responsive t-SNE 7
1.3.3 Progressive Visual Analytics with Safeguards 8
1.4 Structure of Dissertation 9
CHAPTER2. Related Work 11
2.1 Progressive Visual Analytics 11
2.1.1 Definitions 11
2.1.2 System Latency and Human Factors 13
2.1.3 Users, Tasks, and Models 15
2.1.4 Techniques, Algorithms, and Systems. 17
2.1.5 Uncertainty Visualization 19
2.2 Approaches for Scalable Visualization Systems 20
2.3 The k-Nearest Neighbor (KNN) Problem 22
2.4 t-Distributed Stochastic Neighbor Embedding 26
CHAPTER3. SwiTuna: Responsive and Incremental Visual Exploration of Large-scale Multidimensional Data 28
3.1 The SwiTuna Design 31
3.1.1 Design Considerations 32
3.1.2 System Overview 33
3.1.3 Scalable Visualization Components 36
3.1.4 Visualization Cards 40
3.1.5 User Interface and Interaction 42
3.2 Responsive Querying 44
3.2.1 Querying Pipeline 44
3.2.2 Prompt Responses 47
3.2.3 Incremental Processing 47
3.3 Evaluation: Performance Benchmark 49
3.3.1 Study Design 49
3.3.2 Results and Discussion 52
3.4 Implementation 56
3.5 Summary 56
CHAPTER4. PANENE:AProgressive Algorithm for IndexingandQuerying Approximate k-Nearest Neighbors 58
4.1 Approximate k-Nearest Neighbor 61
4.1.1 A Sequential Algorithm 62
4.1.2 An Online Algorithm 63
4.1.3 A Progressive Algorithm 66
4.1.4 Filtered AKNN Search 71
4.2 k-Nearest Neighbor Lookup Table 72
4.3 Benchmark. 78
4.3.1 Online and Progressive k-d Trees 78
4.3.2 k-Nearest Neighbor Lookup Tables 83
4.4 Applications 85
4.4.1 Progressive Regression and Density Estimation 85
4.4.2 Responsive t-SNE 87
4.5 Implementation 92
4.6 Discussion 92
4.7 Summary 93
CHAPTER5. ProReveal: Progressive Visual Analytics with Safeguards 95
5.1 Progressive Visual Analytics with Safeguards 98
5.1.1 Definition 98
5.1.2 Examples 101
5.1.3 Design Considerations 103
5.2 ProReveal 105
5.3 Evaluation 121
5.4 Discussion 127
5.5 Summary 130
CHAPTER6. Discussion 132
6.1 Lessons Learned 132
6.2 Limitations 135
CHAPTER7. Conclusion 137
7.1 Thesis Contributions Revisited 137
7.2 Future Research Agenda 139
7.3 Final Remarks 141
Abstract (Korean) 155
Acknowledgments (Korean) 157Docto
Visual analytics in FCA-based clustering
Visual analytics is a subdomain of data analysis which combines both human
and machine analytical abilities and is applied mostly in decision-making and
data mining tasks. Triclustering, based on Formal Concept Analysis (FCA), was
developed to detect groups of objects with similar properties under similar
conditions. It is used in Social Network Analysis (SNA) and is a basis for
certain types of recommender systems. The problem of triclustering algorithms
is that they do not always produce meaningful clusters. This article describes
a specific triclustering algorithm and a prototype of a visual analytics
platform for working with obtained clusters. This tool is designed as a testing
frameworkis and is intended to help an analyst to grasp the results of
triclustering and recommender algorithms, and to make decisions on
meaningfulness of certain triclusters and recommendations.Comment: 11 pages, 3 figures, 2 algorithms, 3rd International Conference on
Analysis of Images, Social Networks and Texts (AIST'2014). in Supplementary
Proceedings of the 3rd International Conference on Analysis of Images, Social
Networks and Texts (AIST 2014), Vol. 1197, CEUR-WS.org, 201
The CLAIRE visual analytics system for analysing IR evaluation data
In this paper, we describe Combinatorial visuaL Analytics system for Information Retrieval Evaluation (CLAIRE), a Visual Analytics (VA) system for exploring and making sense of the performances of a large amount of Information Retrieval (IR) systems, in order to quickly and intuitively grasp which system configurations are preferred, what are the contributions of the different components and how these components interact together
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Towards a Theory of Analytical Behaviour: A Model of Decision-Making in Visual Analytics
This paper introduces a descriptive model of the human-computer processes that lead to decision-making in visual analytics. A survey of nine models from the visual analytics and HCI literature are presented to account for different perspectives such as sense-making, reasoning, and low-level human-computer interactions. The survey examines the people and computers (entities) presented in the models, the divisions of labour between entities (both physical and role-based), the behaviour of both people and machines as constrained by their roles and agency, and finally the elements and processes which define the flow of data both within and between entities. The survey informs the identification of four observations that characterise analytical behaviour - defined as decision-making facilitated by visual analytics: bilateral discourse, divisions of labour, mixed-synchronicity information flows, and bounded behaviour. Based on these principles, a descriptive model is presented as a contribution towards a theory of analytical behaviour. The future intention is to apply prospect theory, a economic model of decision-making under uncertainty, to the study of analytical behaviour. It is our assertion that to apply prospect theory first requires a descriptive model of the processes that facilitate decision-making in visual analytics. We conclude it necessary to measure the perception of risk in future work in order to apply prospect theory to the study of analytical behaviour using our proposed model
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