Steering the Craft: UI Elements and Visualizations for Supporting Progressive Visual Analytics

Badam, Sriram Karthik; Elmqvist, Niklas; Fekete, Jean-Daniel

doi:10.1111/cgf.13205

Cited by 26 publications

(39 citation statements)

References 32 publications

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“…They found progressive visualizations outperformed blocking visualizations, even giving comparable performance to ideal instantaneous visualizations. Similar results can be elicited from Badam et al's user study [32], where a progressive interface, InsightsFeed, and its instantaneous version showed similar performance in terms of answer accuracy and user preferences.…”

Section: Progressive Systemssupporting

confidence: 84%

PANENE: A Progressive Algorithm for Indexing and Querying Approximate k-Nearest Neighbors

Seo

Fekete

2020

IEEE Trans. Visual. Comput. Graphics

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We present PANENE, a progressive algorithm for approximate nearest neighbor indexing and querying. Although the use of k-nearest neighbor (KNN) libraries is common in many data analysis methods, most KNN algorithms can only be queried when the whole dataset has been indexed, i.e., they are not online. Even the few online implementations are not progressive in the sense that the time to index incoming data is not bounded and cannot satisfy the latency requirements of progressive systems. This long latency has significantly limited the use of many machine learning methods, such as t-SNE, in interactive visual analytics. PANENE is a novel algorithm for Progressive Approximate k-NEarest NEighbors, enabling fast KNN queries while continuously indexing new batches of data. Following the progressive computation paradigm, PANENE operations can be bounded in time, allowing analysts to access running results within an interactive latency. PANENE can also incrementally build and maintain a cache data structure, a KNN lookup table, to enable constant-time lookups for KNN queries. Finally, we present three progressive applications of PANENE, such as regression, density estimation, and responsive t-SNE, opening up new opportunities to use complex algorithms in interactive systems.

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Section: Progressive Systemssupporting

confidence: 84%

PANENE: A Progressive Algorithm for Indexing and Querying Approximate k-Nearest Neighbors

Seo

Fekete

2020

IEEE Trans. Visual. Comput. Graphics

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“…Similarly, Rosenbaum et al [17] allow the user to steer the visualization and prioritize data regions of interest. InsightsFeed [18] demonstrates interface controls and visualization augments for progressive visualization steering.…”

Section: Progressive Visual Analyticsmentioning

confidence: 99%

Selective Wander Join: Fast Progressive Visualizations for Data Joins

Procopio

Scheidegger

et al. 2019

Informatics

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Progressive visualization offers a great deal of promise for big data visualization; however, current progressive visualization systems do not allow for continuous interaction. What if users want to see more confident results on a subset of the visualization? This can happen when users are in exploratory analysis mode but want to ask some directed questions of the data as well. In a progressive visualization system, the online aggregation algorithm determines the database sampling rate and resulting convergence rate, not the user. In this paper, we extend a recent method in online aggregation, called Wander Join, that is optimized for queries that join tables, one of the most computationally expensive operations. This extension leverages importance sampling to enable user-driven sampling when data joins are in the query. We applied user interaction techniques that allow the user to view and adjust the convergence rate, providing more transparency and control over the online aggregation process. By leveraging importance sampling, our extension of Wander Join also allows for stratified sampling of groups when there is data distribution skew. We also improve the convergence rate of filtering queries, but with additional overhead costs not needed in the original Wander Join algorithm.

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“…But this is rarely done. One reason observed in recent user studies may be that users seem uneasy to judge the trustworthiness of partial data shown in unfinished visualizations at the same time [19].…”

Section: Figurementioning

confidence: 99%

“…This happens when employing an inadequate sampling technique, but can in particular be observed when performing no sampling at all and simply partitioning the data as is. One solution is to randomize the data in a preprocess before loading it into the PVA system [19], (Section 4). This simple, yet effective solution is suited for scenarios in which one does not have access to the source of a PVA system and cannot add improved sampling functionality to it.…”

Section: Support Handling Fluctuating Progressionsmentioning

confidence: 99%

“…Such a relative positioning is prone to change with every added data item, and it is thus not surprising that the idea of simulated annealing has also been transferred to network visualizations [48], (Chaper 12.2). If fluctuations cannot be avoided, it is common practice to at least try to smoothly animate between successive updates using animated transitions [7], (Section 2.4.1) or staged animations [19], (Section 4.2).…”

Section: Support Handling Fluctuating Progressionsmentioning

confidence: 99%

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A Review and Characterization of Progressive Visual Analytics

et al. 2018

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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.Keywords: visual analytics; progressive visualization; incremental visualization; online algorithms MotivationWith data growing in size and complexity, and analysis methods getting more sophisticated and computationally intensive, the idea of Progressive Visual Analytics (PVA) [1,2] becomes increasingly appealing. A PVA approach can either subdivide the data to process each data chunk individually, or it can subdivide the analytic process into computational steps that iteratively refine analytic results [3]. By doing so, PVA yields partial results of increasing completeness or approximative results of increasing correctness, respectively. This is useful in a wide range of visual analytics scenarios:• to realize responsive client-server visualizations using incremental data transmissions Because of its versatility, the progressive approach to data analysis and visualization is alternatively seen as a paradigm for computation, for interaction, for data transmission, or for visual presentation. It is thus not surprising that PVA-related research is distributed over multiple disciplines, motivated by various underlying problems, described in different, sometimes overloaded terms at different levels of detail for different audiences.

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Steering the Craft: UI Elements and Visualizations for Supporting Progressive Visual Analytics

Cited by 26 publications

References 32 publications

PANENE: A Progressive Algorithm for Indexing and Querying Approximate k-Nearest Neighbors

PANENE: A Progressive Algorithm for Indexing and Querying Approximate k-Nearest Neighbors

Selective Wander Join: Fast Progressive Visualizations for Data Joins

A Review and Characterization of Progressive Visual Analytics

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