Progressive Visual Analytics

A special issue of Informatics (ISSN 2227-9709).

Deadline for manuscript submissions: closed (28 January 2019) | Viewed by 6574

Special Issue Editor


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Guest Editor
Department of Computer Science, Università di Roma "La Sapienza", 00185 Roma, Italy
Interests: HCI; visualization; visual analytics; progressive visual analytics; cyber security

Special Issue Information

Dear Colleagues,

This Special Issue of the Informatics Journal welcomes submissions on the topic of Progressive Visual Analytics (PVA). PVA is motivated by both data size and complexity and computationally expensive analysis methods. The main idea behind PVA is to produce a sequence of partial results, either subdividing the data into chunks that are individually processed or designing the analytic process into computational steps that iteratively refine the analytic results. Using such strategies allows a PVA application to meet specific time constraints, which can range from tenths of seconds, to ensure interaction fluidity, to minutes or hours, to complete sub-tasks within application dependent timeframes. Dealing with partial results of increasing completeness and correctness calls for effective methods that inform the user of the usefulness and completeness of the intermediated results they are forced to deal with using a PVA solution.

We encourage authors to submit their original research articles, works in progress, surveys, reviews, and viewpoint articles in this field. This Special Issue welcomes applications, theories, models, and frameworks—whether conceptual, analytical, prescriptive, predictive, design-related, or otherwise—that are concerned with (but not limited to) the following topics as they relate to PVA:

  • Adaptive data sampling/chunking
  • Approximate computation
  • Approximation
  • Clustering
  • Computational steering
  • Continuous interaction
  • Data subdivision
  • Device adaptation
  • Dimension reduction
  • Human–computer interaction
  • Incremental computation
  • Incremental visualization
  • Infrastructure
  • Interaction
  • Interaction traces
  • Interactive algorithms
  • Interactive exploration
  • Latency
  • Multi-threading
  • On line algorithms
  • Partial results approximation and significance
  • Partial results characterization and management
  • Progressive data analysis
  • Progressive data querying
  • Progressive refinement
  • Progressive visualization
  • Real time interaction
  • Real time updating
  • Toolkit
  • Uncertainty
  • Uncertainty and approximation visualization

Prof. Giuseppe Santucci
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Informatics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

21 pages, 465 KiB  
Article
Selective Wander Join: Fast Progressive Visualizations for Data Joins
by Marianne Procopio, Carlos Scheidegger, Eugene Wu and Remco Chang
Informatics 2019, 6(1), 14; https://doi.org/10.3390/informatics6010014 - 25 Mar 2019
Cited by 7 | Viewed by 6244
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Progressive Visual Analytics)
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