Event-Based Data Intake System for Retail Location Tracking
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Solution Overview
Problem
Analyzing and searching massive quantities of machine-generated data from diverse sources, such as system logs, network packets, and sensor data, is challenging due to the vast volume, variety, and complexity of the data, requiring efficient data intake and query systems to extract insights.
Innovation Solution
The implementation of an event-based data intake and query system, like the SPLUNK® ENTERPRISE system, which collects, indexes, and searches machine-generated data using flexible schemas and late-binding extraction rules, allowing for real-time operational intelligence and flexible data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional structured databases and direct customer data gathering methods are used, then data can be organized and analyzed, but the system cannot efficiently handle massive quantities of machine-generated data from diverse sources
Solution Approach 1:
The patent implements a universal data intake system that can ingest multiple types of machine-generated data (system logs, network packets, sensor data) from diverse sources through a single platform. The system uses flexible schemas and late-binding extraction rules to adapt to different data formats and sources, enabling one system to handle various data types efficiently.
Solution Approach 2:
The system employs dynamic schema validation and late-binding extraction rules that can be configured and modified without restructuring the entire database. This allows the system to adapt to new data sources and formats over time, improving versatility while maintaining processing efficiency through optimized data paths.
2Quantity of substance
If massive quantities of machine-generated data are collected and stored, then comprehensive analysis is possible, but the volume and complexity of data make searching and analysis challenging
Solution Approach 1:
The patent segments the data processing system into distinct functional components: data intake, schema validation, extraction rule application, and analysis. This modular architecture handles large volumes of data by processing them through specialized stages, reducing overall system complexity while maintaining comprehensive data collection capabilities.
Solution Approach 2:
The system introduces intermediary layers including flexible schemas and extraction rules that sit between raw data collection and final analysis. These intermediaries transform and organize massive quantities of heterogeneous data into structured formats, making the data manageable and searchable without requiring direct complex handling of raw inputs.
3Ease of operation
If flexible schemas and late-binding extraction rules are implemented, then real-time operational intelligence is enabled, but data processing and validation become more complex
Solution Approach 1:
The system performs preliminary data validation and schema assignment during the data intake phase, before full analysis occurs. Late-binding extraction rules are pre-configured but only applied when relevant, reducing real-time processing complexity while maintaining flexibility for comprehensive data analysis.
Data Source
AI summary
Embodiments are disclosed for a method that may include accessing events in a field-searchable data store. The events may include raw machine data associated with a timestamp. The raw machine data may represent interactions between a mobile device and one or more network devices at a locale. The method may further include determining, based on the interactions, one or more geographic positions of the mobile device, and calculating a metric for the locale using the geographic positions.