NeuralCubes: Deep Representations for Visual Data Exploration

@article{Wang2018NeuralCubesDR,
  title={NeuralCubes: Deep Representations for Visual Data Exploration},
  author={Zhe Wang and Dylan Cashman and Mingwei Li and Jixian Li and Matthew Berger and Joshua A. Levine and Remco Chang and Carlos Eduardo Scheidegger},
  journal={2021 IEEE International Conference on Big Data (Big Data)},
  year={2018},
  pages={550-561}
}
Visual exploration of large multidimensional datasets has seen tremendous progress in recent years, allowing users to express rich data queries that produce informative visual summaries, all in real time. [] Key Method NeuralCubes learns a function that takes as input a given query, for instance, a geographic region and temporal interval, and outputs the result of the query. The learned function serves as a real-time, low-memory approximator for aggregation queries. NeuralCubes models are small enough to be…
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8 Citations
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8 Citations

Approximate Query Processing for Data Exploration using Deep Generative Models

This work uses deep generative models, an unsupervised learning based approach, to learn the data distribution faithfully such that aggregate queries could be answered approximately by generating samples from the learned model.

Approximate Query Processing using Deep Generative Models

This work uses deep generative models, an unsupervised learning based approach, to learn the data distribution faithfully such that aggregate queries could be answered approximately by generating samples from the learned model.

ML-AQP: Query-Driven Approximate Query Processing based on Machine Learning

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An open-source distributed visualization framework based on a generic multi-dimensional aggregation pyramid model based on two well-known graphics concepts, namely the Spatio-temporal Cube and 2D Tile Pyramid is developed.

IDLat: An Importance-Driven Latent Generation Method for Scientific Data

A novel importance-driven latent representation to facilitate domain-interest-guided scientific data visualization and analysis and qualitatively and quantitatively evaluate the effectiveness and efficiency of latent representations generated by the method with data from multiple scientific visualization applications.

A Visual Analytics System for Profiling Urban Land Use Evolution

This paper presents a meta-modelling system that automates the very labor-intensive and therefore time-heavy and expensive and therefore expensive and expensive process of computer programming called “hibernation”.

A hybrid prediction and search approach for flexible and efficient exploration of big data

This paper presents a hybrid prediction and search approach (HPS) for building visualization systems of big data. The basic idea is training a regression model to predict a coarse range on the…

Tinycubes: A modular technology for interactive visual analysis of historical and continuously updated spatiotemporal data

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