M2M Data Aggregator Schema Conversion
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Solution Overview
Problem
The integration of data from diverse sources in different formats poses challenges due to inaccuracy and delayed analysis, leading to unreliable decision-making in systems that rely on this data.
Innovation Solution
A machine-to-machine (M2M) data aggregator system that converts data into a common format using a schema repository, coupled with a metadata system for reputation management and provenance tracking, ensuring data reliability and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from multiple diverse sources in different formats is collected, then the quantity and variety of available information increases, but the accuracy and reliability of data-driven decisions deteriorate
Solution Approach 1:
The patent introduces a data aggregation system as an intermediary component that sits between multiple diverse data sources and the decision-making systems. This aggregator standardizes data from different sources into a common format, validates data quality, and manages data provenance, thereby maintaining reliability while handling large quantities of diverse data.
Solution Approach 2:
The system performs preliminary data validation, formatting, and quality assessment before data is used for decision-making. By pre-processing and standardizing data upfront, the system ensures that only reliable, properly formatted data enters the decision-making pipeline, preventing accuracy deterioration.
2Quantity of substance
If data is collected over an extended period from multiple sources, then the comprehensiveness of information increases, but the timeliness and speed of analysis deteriorate
Solution Approach 1:
The data aggregation system performs preliminary processing, validation, and standardization of data as it arrives, rather than batching all processing later. This continuous pre-processing approach reduces the time required for subsequent analysis while maintaining comprehensive data collection over extended periods.
3Adaptability or versatility
If data from different formats and sources is integrated, then the versatility of information available increases, but the complexity of data processing and management increases
Solution Approach 1:
The patent implements a universal data aggregation system that can handle multiple data formats and sources through a single standardized interface. The system uses common data models and standardized protocols to process diverse inputs, reducing the need for separate processing pipelines for each data source and thereby managing complexity while maintaining versatility.
4Ease of operation
If diverse data formats are converted to a common format, then the ease of data analysis improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs format conversion and standardization as a preliminary step when data first enters the aggregation system, rather than converting formats repeatedly during subsequent analysis operations. This one-time preliminary conversion reduces overall computational overhead while improving ease of analysis for downstream systems.
Data Source
AI summary
A computer system may include data aggregator logic configured to ingest a data item from a data source via an aggregation socket, wherein the aggregation socket is configured to ingest data items of a particular data type; identify a schema associated with the aggregation socket; convert the data item into a common data format using the identified schema; store the converted data item in a common data format storage associated with the computer device; and provide the stored data item to a data utilization system. The computer system may further maintain and record provenance and reputation models associated with data items stored in the common data format storage.