登录查看更多内容

How can OLAP support machine learning workflows?

由人工智能和领英社区提供技术支持

Machine learning (ML) is a branch of data science that uses algorithms and models to learn from data and make predictions or decisions. ML workflows involve various steps such as data collection, preprocessing, feature engineering, model training, evaluation, and deployment. To perform these steps efficiently and effectively, ML practitioners need to access, manipulate, and analyze large and complex datasets. Online analytical processing (OLAP) is a technology that can support ML workflows by enabling fast and flexible data analysis across multiple dimensions and perspectives.

此文章中的业界达人

由社区从 4 条内容中精选。了解更多

Sabikah Mukhi

Data Analytics | Data Visualisation

1 What is OLAP?

OLAP is a technique that organizes data into multidimensional structures called cubes, which consist of dimensions and measures. Dimensions are categories or attributes that define the data, such as time, location, product, or customer. Measures are numerical values that can be aggregated or calculated, such as sales, revenue, or profit. OLAP cubes allow users to perform complex queries and calculations on data by slicing, dicing, drilling, rolling, and pivoting the dimensions and measures. OLAP also supports various analytical functions, such as ranking, sorting, filtering, grouping, and forecasting.

添加您的观点

Juan M.

Business Analytics, CFO, Data Analyst, BI
举报内容
Data Preparation and Structuring: OLAP systems provide a structured, multidimensional view of data, simplifying data preparation for machine learning. Feature Engineering Facilitation: OLAP tools assist in creating and organizing features, streamlining the feature engineering process. Data Exploration and Insights: OLAP systems enable interactive data exploration, helping data scientists identify patterns and gain insights for feature selection and model development. Historical Data for Validation: OLAP systems offer historical data for model validation, cross-validation, and testing, improving model accuracy and reliability. Real-Time Data Integration. Performance Monitoring and Reporting

已翻译

赞

2 How can OLAP support ML workflows?

OLAP can support ML workflows in several ways, such as data exploration, preprocessing, feature engineering, training, and evaluation. It can provide interactive and visual tools to slice and dice the data across different dimensions and measures, as well as to apply various transformations and functions to the data. OLAP can also help engineer features for the ML models by creating new measures or dimensions from the existing data. Furthermore, it can provide fast and flexible access to the data for model training, and various metrics and indicators to measure the accuracy of the ML models. Finally, it can integrate the ML models with OLAP cubes to deliver real-time or near-real-time data analysis and prediction capabilities.

添加您的观点

加载更多内容

3 What are the challenges and limitations of OLAP for ML workflows?

OLAP is not the ideal solution for ML workflows, and it has several challenges and limitations to consider. Firstly, OLAP relies on the quality and reliability of the data sources and the data integration processes. If the data is incomplete, inconsistent, outdated, or inaccurate, the OLAP cubes will reflect these issues and affect the ML workflows. Additionally, OLAP cubes are designed to provide aggregated and summarized views of the data, which may not capture the details and nuances of the data. Furthermore, OLAP cubes can handle large and complex datasets, but they also require significant storage and processing resources. If the data volume, variety, or velocity grows beyond the capacity of the OLAP cubes, their performance and functionality may suffer. Lastly, OLAP cubes expose the data to various users and applications, which may pose risks of data leakage, breach, or misuse. Therefore, proper data security and governance policies need to be implemented to protect the data and comply with regulations and standards.

添加您的观点

Sabikah Mukhi

Data Analytics | Data Visualisation
举报内容
ML requires a totally different dataset, it needs the data to be converted to numbers to be able to apply statistical models, whereas OLAP tends to bring pre-calculated/aggregated sdata in a multidimensional model that brings more of a business value making the business users capable of dragging and dropping the data and viewign the information from a multi dimensional view/cube

已翻译

赞

4 What are the best practices and standards for OLAP for ML workflows?

To leverage OLAP for ML workflows effectively and efficiently, some best practices and standards should be followed. Data modeling should be structured according to business requirements and objectives of the ML workflows. The dimensions and measures should be relevant, meaningful, and consistent, and the relationships and hierarchies among them should be clear and logical. Furthermore, the OLAP cubes should be processed and refreshed regularly to ensure data accuracy. It's also important to optimize and tune the cubes for better performance. Moreover, OLAP results should be validated with other sources and methods, as well as with domain knowledge. Finally, OLAP insights should be communicated and presented with clarity and simplicity, using appropriate visualizations and dashboards. It's essential to explain the findings with evidence, context, actionable recommendations, and meaningful suggestions.

添加您的观点

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

加载更多内容

Data Science

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

How can OLAP support machine learning workflows?

1

2

3

4

5

1 What is OLAP?

2 How can OLAP support ML workflows?

3 What are the challenges and limitations of OLAP for ML workflows?

4 What are the best practices and standards for OLAP for ML workflows?

5 Here’s what else to consider

Data Science

给文章评分

感谢您的反馈

更多Data Science相关文章

更多相关阅读内容

How can OLAP support machine learning workflows?

1

2

3

4

5

1 What is OLAP?

2 How can OLAP support ML workflows?

3 What are the challenges and limitations of OLAP for ML workflows?

4 What are the best practices and standards for OLAP for ML workflows?

5 Here’s what else to consider

Data Science

给文章评分

感谢您的反馈

查看其他技能