Journal ArticleDOI
Sampling for Scalable Visual Analytics
TLDR
The goal is to better understand how users interact with sampling to enable wider adoption of sampling for scalable visual analytics.Abstract:
Sampling is becoming an essential tool for scalable interactive visual analysis. After outlining prior work by the database community on sampling for visualization of aggregation queries, this article considers how these results might be improved and extended to a broader setting. The goal is to better understand how users interact with sampling to enable wider adoption of sampling for scalable visual analytics.read more
Citations
More filters
Journal ArticleDOI
Visual Abstraction of Large Scale Geospatial Origin-Destination Movement Data
TL;DR: A characterization of OD flows is established based on an analogy between OD flows and natural language processing (NPL) terms, and an iterative multi-objective sampling scheme is designed to select OD flows in a vectorized representation space to enhance the readability of sampled OD flows.
Journal ArticleDOI
Random Sample Partition: A Distributed Data Model for Big Data Analysis
TL;DR: The Random Sample Partition (RSP) distributed data model is proposed to represent a big data set as a set of disjoint data blocks, called RSP blocks, which have a probability distribution similar to that of the entire data set.
Journal ArticleDOI
Preserving Minority Structures in Graph Sampling
Ying Zhao,Jiang Haojin,Qi'an Chen,Yaqi Qin,Huixuan Xie,Yitao Wu,Shixia Liu,Zhiguang Zhou,Jiazhi Xia,Fangfang Zhou +9 more
TL;DR: This work conducts a pilot user study and performs an experimental study to evaluate the performance of existing graph sampling algorithms regarding minority structure preservation, and suggests key points for designing a new graph sampling approach named mino-centric graph sampling (MCGS).
Journal ArticleDOI
A Review and Characterization of Progressive Visual Analytics
TL;DR: The review and discussion of PVA presented in this paper address issues and provide a literature collection on this topic, a conceptual characterization of Pva, as well as a consolidated set of practical recommendations for implementing and using PVA-based visual analytics solutions.
Journal ArticleDOI
Exploring and cleaning big data with random sample data blocks
TL;DR: The experimental results of three real data sets show that the approximate results from RSP-Explore can rapidly converge toward the true values, and cleaning a sample of RSP blocks is sufficient to estimate the statistical properties of the unknown clean data.
References
More filters
Proceedings ArticleDOI
Online aggregation
TL;DR: In this article, the authors propose an online aggregation interface that allows users to both observe the progress of their aggregation queries and control execution on the fly, and present a suite of techniques that extend a database system to meet these requirements.
Proceedings ArticleDOI
BlinkDB: queries with bounded errors and bounded response times on very large data
TL;DR: BlinkDB allows users to trade-off query accuracy for response time, enabling interactive queries over massive data by running queries on data samples and presenting results annotated with meaningful error bars.
Proceedings ArticleDOI
New sampling-based summary statistics for improving approximate query answers
Phillip B. Gibbons,Yossi Matias +1 more
TL;DR: This paper introduces two new sampling-based summary statistics, concise samples and counting samples, and presents new techniques for their fast incremental maintenance regardless of the data distribution, and considers their application to providing fast approximate answers to hot list queries.
Journal ArticleDOI
imMens : real-time visual querying of big data
TL;DR: Methods for interactive visualization of big data, following the principle that perceptual and interactive scalability should be limited by the chosen resolution of the visualized data, not the number of records are presented.
Journal ArticleDOI
Knowledge Generation Model for Visual Analytics
TL;DR: A knowledge generation model for visual analytics is proposed that ties together these diverse frameworks, yet retains previously developed models (e.g., KDD process) to describe individual segments of the overall visual analytic processes.