search
HomeTechnology peripheralsAIVolcano engine tool technology sharing: use AI to complete data mining and complete SQL writing with zero threshold

火山引擎工具技术分享:用 AI 完成数据挖掘,零门槛完成 SQL 撰写

When using BI tools, the question often encountered is: "How can I produce and process data if I don't know SQL? Can I do mining analysis if I don't know the algorithm?"

When professional algorithm teams do data mining, data analysis and visualization will also appear relatively fragmented. Completing algorithm modeling and data analysis work in a streamlined manner is also a good way to improve efficiency.

At the same time, for professional data warehouse teams, data content with the same theme faces the problem of "repeated construction, relatively scattered use and management" - is there any way to produce it at the same time in one task, with the same theme? Datasets with different content? Can the produced data set be used as input to re-participate in data construction?

1. DataWind’s visual modeling capability is here

The BI platform DataWind intelligent data insight launched by the Volcano Engine has launched a new advanced feature-visual modeling.

Users can simplify the complex data processing and modeling process into a clear and easy-to-understand canvas process through visual dragging, pulling, and connecting operations. All types of users can complete data production and processing according to the idea of ​​​​what they think is what they get. Thereby lowering the threshold for data production and acquisition.

Canvas supports the simultaneous construction of multiple groups of canvas processes, and one picture can realize the construction of multiple data modeling tasks, improving the efficiency of data construction and reducing task management costs; in addition, Canvas integrates and encapsulates more than 40 types of data cleaning , feature engineering operators, covering primary to high-level data production capabilities, without the need for coding to complete complex data capabilities.

2. Zero-threshold SQL tools

Data production and processing is the first step to obtain and analyze data.

For non-technical users, there is a certain threshold for using SQL syntax. At the same time, local files cannot be updated regularly, resulting in the dashboard needing to be redone manually every time. The technical manpower required to obtain data often needs to be scheduled, and the timeliness and satisfaction of data acquisition are greatly reduced. Therefore, it is particularly important to use zero-code data construction tools.

Listed below are two typical scenarios of how zero-threshold data processing is applied in work.

2.1 [Scenario 1] What you think is what you get, and the data processing process is completed visually

When product operation iterations are in urgent need of timely input feedback of different data, the data processing process can be abstracted and constructed through visualization The modular drag operator constructs the data processing process.

If you want to obtain the number of orders and order amounts based on date and city granularity, and obtain the city data of the top 10 daily consumption amount data, the operation is as follows:

General data processing process

Visual modeling process

  1. Please technical students pull the detailed data of the order, including order id/order amount/user id/order date city, etc.
  2. Set the data through the perspective operation, order date, city, the indicator is the sum of the order amount, the sum of the order id
  3. Sort the pivot results by amount, and then write the serial number
  4. Use the filter to filter the Top10 data
  1. Select the data source, select the database table or upload a CSV file or connect to LarkSheet
  2. Filter the field information you need to use , configure your own defined field names and formats
  3. Select the aggregation operator to aggregate and calculate order volume and order amount based on date and city
  4. Select the Top value operator and take the Top10 amount
  5. Output the data set, which can be applied to Fengshen Draw a chart

火山引擎工具技术分享:用 AI 完成数据挖掘,零门槛完成 SQL 撰写

2.2 [Scenario 2] Quickly combine multiple tables to easily solve multi-data association calculations

In the data processing process, there are multiple data sources that need to be combined and used. Conventionally, it is difficult and time-consuming to master advanced Vlookup and other algorithms through Excel. At the same time, when the amount of data is large, the computer performance may not be able to complete the combined calculation of the data.

If there are two orders with relatively large amounts of data and a customer attribute information table, the profit amount needs to be calculated based on the bill amount and cost amount, and then the top 100 user order information is taken based on the profit contribution

火山引擎工具技术分享:用 AI 完成数据挖掘,零门槛完成 SQL 撰写

3. AI data mining is no longer out of reach

When basic data cleaning is no longer able to satisfy data construction and data analysis, AI algorithm support is needed for mining. When data has more hidden value. Algorithm team students may suffer from the inability to work well with visual charts, and cannot produce good data that can be applied quickly; while ordinary users may be directly suppressed by the high threshold of AI code to suppress the emergence of this algorithm - raising demand but fearing demand It is too shallow and the value cannot be well evaluated. At this time, algorithm mining becomes a luxury.

DataWind’s visual modeling encapsulates more than 30 common AI operator capabilities. Users only need to understand the function of the algorithm and configure the input and training goals of the algorithm operator through configuration to complete model training. , quickly obtain prediction results based on other configured data content.

火山引擎工具技术分享:用 AI 完成数据挖掘,零门槛完成 SQL 撰写

火山引擎工具技术分享:用 AI 完成数据挖掘,零门槛完成 SQL 撰写

火山引擎工具技术分享:用 AI 完成数据挖掘,零门槛完成 SQL 撰写

#The following will take two typical scenarios as examples, How to complete data mining without knowing how to write Python.

3.1 [Elementary] You can do data mining even if you don’t know Python

Users’ daily work basically does not involve writing Python, but there are demand scenarios for data mining. He needs to do customer intention mining based on existing high-intention customer samples. At this point, the data mining process can be built through visual modeling:

    Drag in the sample data and all data as data input.
  1. Drag into the classification algorithm, such as XGB algorithm for model training.
  2. Drag in the prediction operator and build the relationship between the model and all the data for prediction.
  3. The actual data and predicted results are combined with the output data set to analyze the intention distribution of all user data.


火山引擎工具技术分享:用 AI 完成数据挖掘,零门槛完成 SQL 撰写

3.2 [Advanced] You can build complex algorithm models without writing Python

Users need to build a model based on existing data User repurchase model. During model building, it is necessary to use gradient boosting tree to build a prediction model after data cleaning and format conversion. At this time, the repurchase model process can be built based on visual modeling:

火山引擎工具技术分享:用 AI 完成数据挖掘,零门槛完成 SQL 撰写

    Merge rows: Merge the output data tables of n operators (rectangles in the figure) into one overall data table based on consistent headers. If the user's sales data does not add or delete new attributes, no changes are needed here.
  1. Missing value replacement: When there is a null value (null) in the attribute column, it will affect subsequent model calculations. Use the replace missing value operator to replace the null value with the specified default value. When the user's sales data does not add or delete new attributes. No changes are needed here.
  2. one-hot encoding: Text type attributes cannot be directly used in model training and need to be encoded into a numeric vector by one_hot. For example:

火山引擎工具技术分享:用 AI 完成数据挖掘,零门槛完成 SQL 撰写

    Gradient Boosting Tree: Responsible for fitting training data and outputting a model that can be used for prediction (parameters not marked in the figure do not need to be modified by maintainers):


火山引擎工具技术分享:用 AI 完成数据挖掘,零门槛完成 SQL 撰写

    Aggregation_1: Remove duplicates in the prediction data and take the maximum probability.
  1. Extract fields: Extract the necessary label and probability value output.


火山引擎工具技术分享:用 AI 完成数据挖掘,零门槛完成 SQL 撰写

4. Multi-scenario and multi-task construction, management is no longer decentralized

As a data analyst, you will also have a lot of daily work to build data sets and build data dashboards. But usually the bottom table obtained from the data warehouse will be a wide table. On this basis, different data set tasks are built according to different scenario requirements.

In subsequent use, we often encounter more and more similar data sets, but the specific logic cannot be well compared and confirmed. At this time, it would be great if all the data set logic is configured and generated in one data set, and each data set can be judged and defined through the task process.

For this scenario, DataWind’s visual modeling capabilities can also be completed very well. The visual modeling function supports a single data set to be processed by multiple logic processes at the same time to generate multiple data sets. Take the processing of order data and user data as an example:

  1. If a user wants to see the order statistics, then the data processing process of the order statistics data set can be built.
  2. There are users who want to see detailed data, but the detail fields need to be processed and cleaned. In this case, the processing flow of the order detail table data set can be constructed.
  3. Some users want to combine user attributes to count the user's order distribution, then build a multi-table association combined with indicator aggregation to generate a complete user order statistical data set.
  4. The same logic can generate user order details data set under multi-table association.

Thus, four data sets are generated through one task and two data inputs. The four data sets can construct a data subject domain, and subsequent related data can be used from this point on. The data set output by the task is used.

火山引擎工具技术分享:用 AI 完成数据挖掘,零门槛完成 SQL 撰写

5. About us

Volcano Engine Intelligent Data Insight DataWind is an enhanced platform that supports self-service analysis at the detailed level of big data. ABI platform. From data access, data integration, to query and analysis, it is finally presented to business users in the form of visual data portals, digital large screens, and management cockpits, allowing data to exert value.

General data processing process

Visual modeling process

  1. Requires two After the order data is opened, the Copy data is merged into a file
  2. Use VloopUp to find the user data in the order and the user data in the customer, and then combine the two data to generate new data
  3. Use a pivot table to calculate the user bill amount and cost amount, and then calculate the profit amount
  4. ## Get TopN customer information sorted by profit amount
    ## You can upload CSV files/LaskSheet to build data input
  1. Then you can merge the March/April order data into one data
  2. Connect the customer information attribute table and bind the customer Attribute information
  3. Select aggregation to calculate the bill amount and cost amount according to the customer specific number
  4. ## Select the calculation column to calculate the profit amount based on the bill amount and cost amount
  5. Get the TopN customer information sorted according to the profit amount

The above is the detailed content of Volcano engine tool technology sharing: use AI to complete data mining and complete SQL writing with zero threshold. For more information, please follow other related articles on the PHP Chinese website!

Statement
This article is reproduced at:51CTO.COM. If there is any infringement, please contact admin@php.cn delete
ai合并图层的快捷键是什么ai合并图层的快捷键是什么Jan 07, 2021 am 10:59 AM

ai合并图层的快捷键是“Ctrl+Shift+E”,它的作用是把目前所有处在显示状态的图层合并,在隐藏状态的图层则不作变动。也可以选中要合并的图层,在菜单栏中依次点击“窗口”-“路径查找器”,点击“合并”按钮。

ai橡皮擦擦不掉东西怎么办ai橡皮擦擦不掉东西怎么办Jan 13, 2021 am 10:23 AM

ai橡皮擦擦不掉东西是因为AI是矢量图软件,用橡皮擦不能擦位图的,其解决办法就是用蒙板工具以及钢笔勾好路径再建立蒙板即可实现擦掉东西。

谷歌超强AI超算碾压英伟达A100!TPU v4性能提升10倍,细节首次公开谷歌超强AI超算碾压英伟达A100!TPU v4性能提升10倍,细节首次公开Apr 07, 2023 pm 02:54 PM

虽然谷歌早在2020年,就在自家的数据中心上部署了当时最强的AI芯片——TPU v4。但直到今年的4月4日,谷歌才首次公布了这台AI超算的技术细节。论文地址:https://arxiv.org/abs/2304.01433相比于TPU v3,TPU v4的性能要高出2.1倍,而在整合4096个芯片之后,超算的性能更是提升了10倍。另外,谷歌还声称,自家芯片要比英伟达A100更快、更节能。与A100对打,速度快1.7倍论文中,谷歌表示,对于规模相当的系统,TPU v4可以提供比英伟达A100强1.

ai可以转成psd格式吗ai可以转成psd格式吗Feb 22, 2023 pm 05:56 PM

ai可以转成psd格式。转换方法:1、打开Adobe Illustrator软件,依次点击顶部菜单栏的“文件”-“打开”,选择所需的ai文件;2、点击右侧功能面板中的“图层”,点击三杠图标,在弹出的选项中选择“释放到图层(顺序)”;3、依次点击顶部菜单栏的“文件”-“导出”-“导出为”;4、在弹出的“导出”对话框中,将“保存类型”设置为“PSD格式”,点击“导出”即可;

GPT-4的研究路径没有前途?Yann LeCun给自回归判了死刑GPT-4的研究路径没有前途?Yann LeCun给自回归判了死刑Apr 04, 2023 am 11:55 AM

Yann LeCun 这个观点的确有些大胆。 「从现在起 5 年内,没有哪个头脑正常的人会使用自回归模型。」最近,图灵奖得主 Yann LeCun 给一场辩论做了个特别的开场。而他口中的自回归,正是当前爆红的 GPT 家族模型所依赖的学习范式。当然,被 Yann LeCun 指出问题的不只是自回归模型。在他看来,当前整个的机器学习领域都面临巨大挑战。这场辩论的主题为「Do large language models need sensory grounding for meaning and u

ai顶部属性栏不见了怎么办ai顶部属性栏不见了怎么办Feb 22, 2023 pm 05:27 PM

ai顶部属性栏不见了的解决办法:1、开启Ai新建画布,进入绘图页面;2、在Ai顶部菜单栏中点击“窗口”;3、在系统弹出的窗口菜单页面中点击“控制”,然后开启“控制”窗口即可显示出属性栏。

强化学习再登Nature封面,自动驾驶安全验证新范式大幅减少测试里程强化学习再登Nature封面,自动驾驶安全验证新范式大幅减少测试里程Mar 31, 2023 pm 10:38 PM

引入密集强化学习,用 AI 验证 AI。 自动驾驶汽车 (AV) 技术的快速发展,使得我们正处于交通革命的风口浪尖,其规模是自一个世纪前汽车问世以来从未见过的。自动驾驶技术具有显着提高交通安全性、机动性和可持续性的潜力,因此引起了工业界、政府机构、专业组织和学术机构的共同关注。过去 20 年里,自动驾驶汽车的发展取得了长足的进步,尤其是随着深度学习的出现更是如此。到 2015 年,开始有公司宣布他们将在 2020 之前量产 AV。不过到目前为止,并且没有 level 4 级别的 AV 可以在市场

ai移动不了东西了怎么办ai移动不了东西了怎么办Mar 07, 2023 am 10:03 AM

ai移动不了东西的解决办法:1、打开ai软件,打开空白文档;2、选择矩形工具,在文档中绘制矩形;3、点击选择工具,移动文档中的矩形;4、点击图层按钮,弹出图层面板对话框,解锁图层;5、点击选择工具,移动矩形即可。

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Tools

mPDF

mPDF

mPDF is a PHP library that can generate PDF files from UTF-8 encoded HTML. The original author, Ian Back, wrote mPDF to output PDF files "on the fly" from his website and handle different languages. It is slower than original scripts like HTML2FPDF and produces larger files when using Unicode fonts, but supports CSS styles etc. and has a lot of enhancements. Supports almost all languages, including RTL (Arabic and Hebrew) and CJK (Chinese, Japanese and Korean). Supports nested block-level elements (such as P, DIV),

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

SublimeText3 Linux new version

SublimeText3 Linux new version

SublimeText3 Linux latest version

SublimeText3 English version

SublimeText3 English version

Recommended: Win version, supports code prompts!