Automated Data Science Framework for Algorithm Pairing
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Solution Overview
Problem
Current data science techniques require significant computing power, are inefficient, and inflexible, necessitating trained data scientists and complex processes for handling large data sets and performing data science operations.
Innovation Solution
A dynamic data science system that analyzes and organizes data sources and algorithms, providing a user-friendly framework through a graphical user interface, allowing users to easily select data sources and algorithms, and automatically pairs them for data science operations, using reusable algorithmic building blocks to simplify and customize data science processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If trained data scientists use sophisticated computing processes and frameworks to perform data science operations on large data sets, then the quality and accuracy of data science operations are improved, but the device complexity and time required are significantly increased
Solution Approach 1:
The patent introduces an automated data science system that acts as an intermediary between raw data and analysis results. This system includes automated data cleaning components, algorithm selection components, and execution components that work together to perform data science operations without requiring trained data scientists to manually navigate complex processes. The intermediary system handles the sophistication required for quality operations while presenting a simplified interface to users.
Solution Approach 2:
The system enables self-service data science operations through automated frameworks that can independently clean data, select appropriate algorithms, and execute analyses. The automated data cleaning component can process data without manual intervention, and the algorithm selection component can automatically determine which algorithms are suitable for given data types and research questions, allowing the system to serve itself rather than requiring expert operators.
2Measurement precision
If trained data scientists manually clean and prepare large data sets before running algorithms, then the accuracy of data analysis is improved, but the loss of time and productivity are significantly increased
Solution Approach 1:
The system performs preliminary data cleaning and preparation actions automatically before algorithms are executed. The automated data cleaning component processes data in advance, handling formatting, normalization, and validation tasks that would otherwise require manual intervention. This preliminary automated action ensures data accuracy is maintained while eliminating the time-consuming manual preparation phase.
Solution Approach 2:
The patent replaces the mechanical manual process of data cleaning with an automated computational system. Instead of data scientists manually examining and correcting data points, the automated data cleaning component uses computational algorithms to detect and correct errors, standardize formats, and validate data quality, thereby maintaining measurement precision while dramatically reducing the time required.
3Adaptability or versatility
If data scientists manually program algorithms and customize data science operations, then the adaptability and versatility of the system are improved, but the ease of operation and accessibility are significantly reduced
Solution Approach 1:
The system provides a universal platform that can handle multiple types of data science operations through a single automated framework. The algorithm selection component can identify and execute various algorithms appropriate for different data types and research questions without requiring users to manually program each operation. This multi-functionality maintains adaptability while simplifying operation, as the system can universally apply appropriate methods across diverse scenarios.
Solution Approach 2:
The automated system acts as an intermediary that translates user-friendly inputs into sophisticated algorithmic operations. Users can specify their analysis needs in simple terms, and the intermediary system handles the complex algorithm selection, parameter configuration, and execution details, thereby maintaining versatility while dramatically improving ease of operation and accessibility to non-experts.
4Ease of operation
If general-purpose frameworks are used to standardize data science processes, then the ease of operation is improved, but the device complexity and computing power required are still too high for many users
Solution Approach 1:
The system provides self-service capabilities that allow users to access standardized data science processes without needing to understand or configure the underlying complex frameworks. The automated components handle framework management, algorithm selection, and execution automatically, enabling users to benefit from standardized processes while the system manages the framework complexity in the background.
Solution Approach 2:
The patent introduces an intermediary layer between users and complex data science frameworks. This intermediary system includes automated components that translate user requests into framework operations, managing the complexity of general-purpose frameworks while presenting a simplified interface to users. The intermediary handles algorithm selection, parameter configuration, and execution details, allowing users to access standardized processes without directly engaging with framework complexity.
Data Source
AI summary
The present disclosure is directed to performing data science operations. In particular, the present disclosure relates to a data science system that improves data science operations as well as enhances a user's experience in performing data science operations. For example, the data science system provides an improved framework that enables a data source to be paired with one or more algorithms to create a data science operation. In many instances, the data science operation outputs visual results, such as charts and graphs, that are easy for the user to understand. Further, using the framework, the data science system provides tools that enable the user to further customize data science operations.