Designing Progressive and Interactive Analytics Processes for High-Dimensional Data Analysis

In interactive data analysis processes, the dialogue between the human and the computer is the enabling mechanism that can lead to actionable observations about the phenomena being investigated. It is of paramount importance that this dialogue is not interrupted by slow computational mechanisms that do not consider any known temporal human-computer interaction characteristics that prioritize the perceptual and cognitive capabilities of the users. In cases where the analysis involves an integrated computational method, for instance to reduce the dimensionality of the data or to perform clustering, such non-optimal processes are often likely. To remedy this, progressive computations, where results are iteratively improved, are getting increasing interest in visual analytics. In this paper, we present techniques and design considerations to incorporate progressive methods within interactive analysis processes that involve high-dimensional data. We define methodologies to facilitate processes that adhere to the perceptual characteristics of users and describe how online algorithms can be incorporated within these. A set of design recommendations and according methods to support analysts in accomplishing high-dimensional data analysis tasks are then presented. Our arguments and decisions here are informed by observations gathered over a series of analysis sessions with analysts from finance. We document observations and recommendations from this study and present evidence on how our approach contribute to the efficiency and productivity of interactive visual analysis sessions involving high-dimensional data

A Review and Characterization of Progressive Visual Analytics

Progressive Visual Analytics (PVA) has gained increasing attention over the past years. It brings the user into the loop during otherwise long-running and non-transparent computations by producing intermediate partial results. These partial results can be shown to the user for early and continuous interaction with the emerging end result even while it is still being computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth various interpretations and instantiations that have created a research domain of competing terms, various definitions, as well as long lists of practical requirements and design guidelines spread across different scientific communities. This makes it more and more difficult to get a succinct understanding of PVA’s principal concepts, let alone an overview of this increasingly diverging field. The review and discussion of PVA presented in this paper address these issues and provide (1) a literature collection on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical recommendations for implementing and using PVA-based visual analytics solutions

Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis

Exploring data requires a fast feedback loop from the analyst to the system, with a latency below about 10 seconds because of human cognitive limitations. When data becomes large or analysis becomes complex, sequential computations can no longer be completed in a few seconds and data exploration is severely hampered. This article describes a novel computation paradigm called Progressive Computation for Data Analysis or more concisely Progressive Analytics, that brings at the programming language level a low-latency guarantee by performing computations in a progressive fashion. Moving this progressive computation at the language level relieves the programmer of exploratory data analysis systems from implementing the whole analytics pipeline in a progressive way from scratch, streamlining the implementation of scalable exploratory data analysis systems. This article describes the new paradigm through a prototype implementation called ProgressiVis, and explains the requirements it implies through examples.Comment: 10 page

대용량 데이터 탐색을 위한 점진적 시각화 시스템 설계

학위논문(박사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2020. 2. 서진욱.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)—a visual analytics paradigm that can provide intermediate results during computation and allow visual exploration of these results—to 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.현대 데이터 사이언스에서 인터랙티브한 시각화를 통해 데이터를 이해하는 것은 필수적인 분석 방법 중 하나이다. 그러나, 최근 데이터의 크기가 폭발적으로 증가하면서 데이터 크기로 인해 발생하는 지연 시간이 인터랙티브한 시각적 분석에 큰 걸림돌이 되었다. 본 연구에서는 이러한 확장성 문제를 해결하기 위해 점진적 시각적 분석(Progressive Visual Analytics)을 지원하는 일련의 시스템을 디자인하고 개발한다. 이러한 점진적 시각적 분석 시스템은 데이터 처리가 완전히 끝나지 않더라도 중간 분석 결과를 사용자에게 제공함으로써 데이터의 크기로 인해 발생하는 지연 시간 문제를 완화할 수 있다. 첫째로, 수십억 건의 행을 가지는 데이터를 시각적으로 탐색할 수 있는 SwiftTuna 시스템을 제안한다. 데이터 처리 및 시각적 표현의 확장성을 목표로 개발된 이 시스템은, 약 40억 건의 행을 가진 데이터에 대한 시각화를 전처리 없이 수 초마다 업데이트할 수 있는 것으로 나타났다. 둘째로, 근사적 k-최근접점(Approximate k-Nearest Neighbor) 문제를 점진적으로 계산하는 PANENE 알고리즘을 제안한다. 근사적 k-최근접점 문제는 여러 기계 학습 기법에서 쓰임에도 불구하고 초기 계산 시간이 길어서 인터랙티브한 시스템에 적용하기 힘든 한계가 있었다. PANENE 알고리즘은 이러한 긴 초기 계산 시간을 획기적으로 개선하여 다양한 기계 학습 기법을 시각적 분석에 활용할 수 있도록 한다. 특히, 유용한 비선형적 차원 감소 기법인 t-분포 확률적 임베딩(t-Distributed Stochastic Neighbor Embedding)을 가속하여 수백 개의 차원을 가지는 데이터를 빠른 시간 내에 사영할 수 있다. 위의 두 시스템과 알고리즘이 데이터의 행 또는 열의 개수로 인한 확장성 문제를 해결하고자 했다면, 세 번째 시스템에서는 점진적 시각적 분석의 신뢰도 문제를 개선하고자 한다. 점진적 시각적 분석에서 사용자에게 주어지는 중간 계산 결과는 최종 결과의 근사치이므로 불확실성이 존재한다. 본 연구에서는 세이프가드를 이용한 점진적 시각적 분석(Progressive Visual Analytics with Safeguards)이라는 새로운 개념을 제안한다. 이 개념은 사용자가 점진적 탐색에서 마주하는 불확실한 중간 지식에 세이프가드를 남길 수 있도록 하여 탐색에서 얻은 지식의 정확도를 추후 검증할 수 있도록 한다. 또한, 이러한 개념을 실제로 구현하여 탑재한 ProReveal 시스템을 소개한다. ProReveal를 이용한 사용자 실험에서 사용자들은 세이프가드를 성공적으로 만들 수 있었을 뿐만 아니라, 중간 지식의 불확실성을 다루기 위해 세이프가드를 자발적으로 이용한다는 것을 알 수 있었다. 마지막으로, 위 세 가지 연구의 결과를 종합하여 점진적 시각적 분석 시스템을 구현할 때의 디자인적 난제와 향후 연구 방향을 모색한다.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

A Visual Analytics Framework for Reviewing Streaming Performance Data

Understanding and tuning the performance of extreme-scale parallel computing systems demands a streaming approach due to the computational cost of applying offline algorithms to vast amounts of performance log data. Analyzing large streaming data is challenging because the rate of receiving data and limited time to comprehend data make it difficult for the analysts to sufficiently examine the data without missing important changes or patterns. To support streaming data analysis, we introduce a visual analytic framework comprising of three modules: data management, analysis, and interactive visualization. The data management module collects various computing and communication performance metrics from the monitored system using streaming data processing techniques and feeds the data to the other two modules. The analysis module automatically identifies important changes and patterns at the required latency. In particular, we introduce a set of online and progressive analysis methods for not only controlling the computational costs but also helping analysts better follow the critical aspects of the analysis results. Finally, the interactive visualization module provides the analysts with a coherent view of the changes and patterns in the continuously captured performance data. Through a multi-faceted case study on performance analysis of parallel discrete-event simulation, we demonstrate the effectiveness of our framework for identifying bottlenecks and locating outliers.Comment: This is the author's preprint version that will be published in Proceedings of IEEE Pacific Visualization Symposium, 202