刷和链接大数据-Python开发

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  • 2022-06-15 13:15
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猎鹰:大数据交互可视化分析交叉过滤数百万条记录,无延迟。 该项目正在进行中,尚未记录。 如有疑问,请与我们联系。 最大的e Falcon:大数据的交互式可视化分析交叉过滤数百万条没有延迟的记录。 该项目正在进行中,尚未记录。 如有疑问,请与我们联系。 迄今为止,我们进行的最大实验是浏览器中的飞行次数为1000万次,连接到OmniSciDB(以前称为MapD)时,飞行次数约为1.8亿次,或者约为1.70颗星。 我们已经撰写了一篇有关Falcon背后研究的论文。 如果您在出版物中使用Falcon,请引用我们。 @inproceedings {moritz2019f
falcon-master.zip
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内容介绍
<p align="center"> </p> # Falcon: Interactive Visual Analysis for Big Data [![npm version](https://img.shields.io/npm/v/falcon-vis.svg)](https://www.npmjs.com/package/falcon-vis) ![Tests](https://github.com/uwdata/falcon/workflows/Node.js%20CI/badge.svg) [![code style: prettier](https://img.shields.io/badge/code_style-prettier-ff69b4.svg?style=rounded)](https://github.com/prettier/prettier) Crossfilter millions of records without latencies. This project is work in progress and not documented yet. Please get in touch if you have questions. The largest experiments we have done so far is 10M flights in the browser and ~180M flights or [~1.7B stars](#falcon-with-17-billion-stars-from-the-gaia-dataset) when connected to [OmniSciDB](https://www.omnisci.com/platform/core/) (formerly known as MapD). We have written [a paper](https://osf.io/szpqm/) about the research behind Falcon. Please cite us if you use Falcon in a publication. ```bib @inproceedings{moritz2019falcon, doi = {10.1145/3290605}, year = {2019}, publisher = {{ACM} Press}, author = {Dominik Moritz and Bill Howe and Jeffrey Heer}, title = {Falcon: Balancing Interactive Latency and Resolution Sensitivity for Scalable Linked Visualizations}, booktitle = {Proceedings of the 2019 {CHI} Conference on Human Factors in Computing Systems - {CHI} {\textquotesingle}19} } ``` ## Demos - 1M flights in the browser: https://uwdata.github.io/falcon/flights/ - 7M flights in [OmniSci Core](https://www.omnisci.com/platform/core/): https://uwdata.github.io/falcon/flights-mapd/ - 500k weather records: https://uwdata.github.io/falcon/weather/ ![Falcon demo](images/cross.gif "Falcon demo") ## Usage Install with `yarn add falcon-vis`. You can use two query engines. First `ArrowDB` reading data from [Apache Arrow](https://arrow.apache.org/). This engine works completely in the browser and scales up to ten million rows. Second, `MapDDB`, which connects to OmniSci Core. The indexes are created as [ndarrays](https://github.com/scijs/ndarray). Check out the examples to see how to set up an app with your own data. More documentation will follow. ## Features ### Zoom You can zoom histograms. Falcon automatically re-bins the data. ### Show and hide unfiltered data The original counts without filters, can be displayed behind the filtered counts to provide context. Hiding the unfiltered data shows the relative distribution of the data. With unfiltered data. Without unfiltered data. ### Circles or Color Heatmap Heatmap with circles (default). Can show the data without filters. Heatmap with colored cells. ### Vertical bar, horizontal bar, or text for counts Horizontal bar. Vertical bar. Text only. ### Timeline visualization You can visualize the timeline of brush interactions in Falcon. ## Falcon with 1.7 Billion Stars from the GAIA Dataset The [GAIA spacecraft](<https://en.wikipedia.org/wiki/Gaia_(spacecraft)>) measured the positions and distances of stars with unprecedented precision. It collected about 1.7 billion objects, mainly stars, but also planets, comets, asteroids and quasars among others. Below, we show the dataset loaded in Falcon (with OmniSci Core). There is also a [video of me interacting with the dataset through Falcon](images/gaia.mp4). ## Developers Install the dependencies with `yarn`. Then run `yarn start` to start the flight demo with in memory data. Have a look at the other `script` commands in [`package.json`](https://github.com/uwdata/falcon/blob/master/package.json). ## Experiments First version that turned out to be too complicated is at https://github.com/uwdata/falcon/tree/complex and the client-server version is at https://github.com/uwdata/falcon/tree/client-server.
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