Automotive Data Normalization via Modular Manipulation
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Solution Overview
Problem
The challenge in managing automotive data from connected vehicles is the diversity of data formats and sources, which complicates normalization in data marketplaces, essential for proper operation and consumer access.
Innovation Solution
A system comprising a data collector, a data manipulation platform, and a computer processor that selects and executes modules to normalize data entries by manipulating data type, name, format, and content, with the aid of a learning module applying machine learning algorithms to update normalization rules and ensure uniform data formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is collected from multiple sources in different formats, then data diversity and source coverage are improved, but data normalization difficulty and processing complexity increase
Solution Approach 1:
The normalization process is divided into multiple sequential manipulating modules, each handling specific aspects of data normalization (data type conversion, data name standardization, data format normalization, data content validation). This segmentation allows complex normalization tasks to be broken down into manageable, reusable components that can be applied systematically across diverse data sources.
Solution Approach 2:
The system employs universal manipulating modules that can process multiple types of data (sensor data, vehicle metadata, diagnostic information) through standardized operations. These modules are designed to handle various data formats and sources uniformly, applying the same normalization logic across different data types to ensure consistency throughout the marketplace.
2Manufacturing precision
If manual data normalization is performed, then data format consistency is improved, but processing time and operational effort increase
Solution Approach 1:
The system implements automated manipulating modules that autonomously perform data normalization without requiring manual intervention. The modules automatically detect data types, apply appropriate transformations, and validate normalized output, enabling the system to service itself in terms of data preparation and making the normalization process efficient and scalable.
Solution Approach 2:
The normalization process dynamically adjusts data parameters (data types, formats, names) based on the input characteristics and predefined normalization rules. The system changes data parameters automatically through manipulating modules that transform diverse input formats into a standardized output format, maintaining precision while reducing time loss.
3Measurement precision
If comprehensive data manipulation modules are implemented, then normalization accuracy is improved, but system complexity and computational resources increase
Solution Approach 1:
Comprehensive normalization functionality is achieved through segmented manipulating modules, each responsible for a specific aspect of data normalization. This modular architecture maintains high normalization accuracy while managing system complexity by organizing functions into discrete, manageable units that can be independently developed, tested, and maintained.
Solution Approach 2:
The manipulating modules serve as intermediaries between raw diverse data and the standardized data marketplace format. Each module acts as a mediator that transforms specific aspects of the data, ensuring accurate normalization while keeping the overall system structure manageable through clear interfaces and defined responsibilities between modules.
Data Source
AI summary
A method and a system for normalizing data and data format of automotive data associated with connected vehicles and obtained from a plurality of sources are provided herein. The system may include: a data collector configured to obtain a plurality of data entries relating to connected vehicles and presented in different data formats from a plurality of sources; a data manipulating platform configured to enable a user to select and order a plurality of manipulating modules configured to manipulate data or data format of the data entries; a computer processor configured to execute the manipulating modules, in the selected order on the data entries, to yield a plurality of respective data entries that are normalized in accordance with a predefined data and data format, wherein the manipulation includes in the selected order at least manipulation of the following: a data type, data name, data format, and data content.