Data Source Visualizations with Late-Binding Schema Adaptation
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Solution Overview
Problem
Internet-connected devices often lack access to complex communication infrastructure and processing capabilities, making it difficult to process, secure, and query large volumes of data effectively, especially with fast-changing technologies and varied data formats.
Innovation Solution
A data intake and query system that utilizes a late-binding schema and token-based authentication to process and analyze machine-generated data from diverse sources, enabling flexible schema development and real-time data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional data logging functionality is added to internet-connected devices, then data collection capability is improved, but device performance is compromised and development time increases
Solution Approach 1:
The patent extracts data collection and processing functionality from the internet-connected device itself and relocates it to a centralized data storage system. The device only needs to send data packets via HTTP requests, while the complex logging, storage, and analysis functions are performed remotely by the data storage system, thus preserving device performance while maintaining data collection capability.
Solution Approach 2:
The data storage system is designed to handle multiple types of data from diverse internet-connected devices through a unified HTTP-based interface. The system can process data from various sources (sensors, applications, system logs) and store them in a standardized format, eliminating the need for device-specific logging implementations and reducing development complexity.
2Quantity of substance
If data logging functionality is added to internet-connected devices, then data collection capability is improved, but development time significantly increases
Solution Approach 1:
The patent removes the burden of implementing data logging functionality from the device development process. Instead of requiring developers to build custom logging systems, the device simply sends data packets to the centralized system using standard HTTP requests, which can be implemented with minimal code and no specialized knowledge of data storage infrastructure.
Solution Approach 2:
The unified data storage system provides a universal interface for data collection from all types of internet-connected devices. Developers can implement data sending functionality using the same HTTP-based approach regardless of device type or data format, significantly reducing development time and simplifying the manufacturing process.
3Quantity of substance
If large volumes of data are stored in a data storage system, then data capacity is improved, but processing, securing, and querying becomes extremely difficult
Solution Approach 1:
The patent segments the data storage system into specialized components: data reception modules that handle incoming HTTP requests, data storage modules that organize and store data packets, and data processing modules that perform querying and analysis. This segmentation allows each component to be optimized independently, making it easier to manage and process large volumes of data efficiently.
Solution Approach 2:
The patent introduces an intermediary layer (the centralized data storage system) between the internet-connected devices and the data processing functions. This intermediary handles the complexity of data storage, security, and querying, allowing devices to simply send data without needing to implement complex processing logic. The intermediary translates various data formats into a standardized internal representation, simplifying subsequent processing.
4Adaptability or versatility
If data storage systems attempt to scale across new technologies and devices, then adaptability is improved, but the system becomes unable to keep up with fast-changing technologies and configurations
Solution Approach 1:
The patent implements a dynamic data storage system that can adapt to new device types and data formats through flexible schema definitions. The system allows schema evolution without requiring changes to the core infrastructure, enabling it to accommodate fast-changing technologies while maintaining stable processing operations. New device configurations can be registered and integrated through standardized HTTP interfaces, ensuring both adaptability and reliability.
Data Source
AI summary
A data intake and query system processes and stores events, which are associated with token identifiers for tokens corresponding to data sources for the messages that the events are generated from. Thus, the data intake and query system can receive a request to provide analyses and visualizations regarding stored events associated with a particular component associated with a plurality of events, such as a data source for the messages from which the plurality of events are generated from. These requests and the resulting visualizations can be customized based on selected tokens and selected components.