Big Data Management with Incremental K-Means Trees-GPU-Accelerated Construction and Visualization

Superconductor: GPU-accelerated Big Data Visualization for the Browser

There is need for data visualization tools which are both scalable and productive We show how previous work on parallel schedule synthesis for attribute grammars can be extended to this domain. Our results retain the flexibility of common tools in this area, while handling up to two magnitudes more data.

AVIST: A GPU-Centric Design for Visual Exploration of Large Multidimensional Datasets

This paper presents the Animated VISualization Tool (AVIST), an exploration-oriented data visualization tool that enables rapidly exploring and filtering large time series multidimensional datasets. AVIST highlights interactive data exploration by revealing fine data details. This is achieved through the use of animation and cross-filtering interactions. To support interactive exploration of bi...

GPU Based Methods for Interactive Information Visualization of Big Data

Designing a smart algorithm for determining stock exchange signals by data mining

One of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock exchange market. This research proposes a smart algorithm by means of valuable big data that is generated by stock exchange market and different kinds of methodology to present a smart model.In this paper, we investigate relationships between the data and access to their lat...

Distributed GPU-Based K-Means Algorithm for Data-Intensive Applications: Large-Sized Image Segmentation Case

K-means is a compute-intensive iterative algorithm. Its use in a complex scenario is cumbersome, specifically in data-intensive applications. In order to accelerate the K-means running time for data-intensive application, such as large sized image segmentation, we use a distributed multi-agent system accelerated by GPUs. In this K-means version, the input image data are divided into subsets of ...