Grid Computing Timestamped Data Analysis
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Solution Overview
Problem
The increasing volume of timestamped data poses computational and architectural challenges, making it impractical for organizations to store and process using conventional techniques, particularly due to high memory and communication resource demands, which can lead to missed opportunities in decision-making processes.
Innovation Solution
A system utilizing grid-computing devices to receive, compile, and execute scripts that deterministically distribute and analyze timestamped data in parallel, sorting and accumulating it into time series for efficient processing and output generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional techniques are used to store and process timestamped data, then data storage and processing can be performed using standard infrastructure, but computational expense and memory requirements become infeasible for large volumes of data
Solution Approach 1:
The patent segments timestamped data into multiple time series based on time series criteria (e.g., grouping by device, location, or category). Each time series is independently processed by different grid-computing devices, enabling parallel processing and reducing the computational burden on individual devices while handling large volumes of data efficiently
Solution Approach 2:
The patent introduces a new dimension of processing by distributing data across a grid computing architecture rather than using conventional single-system processing. By organizing computation across multiple nodes in a distributed network, the system achieves scalable processing capacity that linearly increases with the number of grid devices, making large-scale timestamped data analysis feasible
2Productivity
If timestamped data is moved between devices during analysis, then data can be processed across multiple computing devices, but communication and computational expenses increase significantly
Solution Approach 1:
The patent performs preliminary distribution of timestamped data to appropriate grid-computing devices based on time series criteria before processing begins. Each device receives and retains the specific time series assigned to it, eliminating the need for repeated data movement during analysis. This preliminary organization ensures that each device processes only its assigned data locally, minimizing communication expenses while maintaining high productivity
Solution Approach 2:
The patent assigns specific time series to specific grid-computing devices based on their characteristics and capabilities. Each device is optimized to process its assigned time series locally without requiring access to other devices' data, reducing inter-device communication while maintaining overall system productivity through specialized local processing
3Measurement precision
If more timestamped data is leveraged for analysis, then prediction accuracy and decision-making quality improve, but memory and hardware resources are overwhelmed
Solution Approach 1:
The patent segments the overall analysis task into multiple independent time series processing tasks, each handled by a different grid-computing device. This segmentation allows the system to leverage large volumes of timestamped data for improved prediction accuracy while distributing memory and computational resources across multiple devices, preventing any single device from being overwhelmed
Solution Approach 2:
The patent creates a universal grid computing framework where multiple devices work together to process diverse time series data. Each device in the grid can handle different types of timestamped data (e.g., sensor readings, transaction logs, event streams), allowing the organization to leverage various data sources for more accurate predictions without overloading individual hardware resources
Data Source
AI summary
Timestamped data can be read in parallel by multiple grid-computing devices. The timestamped data, which can be partitioned into groups based on time series criteria, can be deterministically distributed across the multiple grid-computing devices based on the time series criteria. Each grid-computing device can sort and accumulate the timestamped data into a time series for each group it receives and then process the resultant time series based on a previously distributed script, which can be compiled at each grid-computing device, to generate output data. The grid-computing devices can write their output data in parallel. As a result, vast amounts of timestamped data can be easily analyzed across an easily expandable number of grid-computing devices with reduced computational expense.