Timestamped Data Partitioning for Scalable Cloud Analysis
Find Innovative SolutionsGenerate Solutions
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, leading to missed opportunities in decision-making due to the high computational and communicative expenses associated with sorting and analyzing large datasets.
Innovation Solution
A system that partitions and distributes timestamped data across multiple computing devices, using a modulo function based on hash values to sort and accumulate data in parallel, allowing for efficient processing and analysis with reduced bandwidth usage and scalable computing capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If timestamped data is stored and processed using conventional techniques, then data storage and processing can be performed, but computational and communicative expenses become infeasible for large datasets
Solution Approach 1:
The patent divides the large timestamped dataset into multiple partitions distributed across different computing devices. Each partition is processed independently in parallel, reducing the computational burden on any single device and enabling efficient processing of large volumes of data without overwhelming computational resources.
Solution Approach 2:
The patent introduces a distributed computing dimension by spreading data processing across multiple computing devices rather than relying on a single conventional system. This dimensional expansion allows the system to handle large datasets by distributing both storage and computational tasks across the network.
2Ease of operation
If timestamped data is sorted prior to analysis, then time series analysis can be performed, but bandwidth usage and communicative expenses increase significantly
Solution Approach 1:
The patent performs preliminary sorting of timestamped data during the data ingestion phase, organizing data into time-ordered partitions before distribution. This preliminary action ensures that when data is distributed to computing devices, it is already sorted, eliminating the need for additional sorting operations during analysis and reducing bandwidth consumption.
Solution Approach 2:
Each computing device maintains its own sorted partitions of timestamped data locally, allowing it to perform time series analysis independently without requiring frequent data exchanges or centralized sorting operations. This self-service approach reduces network bandwidth usage while maintaining analytical capabilities.
3Measurement precision
If more timestamped data is leveraged for analysis, then decision-making accuracy improves, but hardware resources become overwhelmed
Solution Approach 1:
The patent segments both the data and the computing resources, assigning different partitions of timestamped data to different computing devices. This segmentation allows the system to leverage large volumes of data for improved analytical accuracy while distributing hardware resource requirements across multiple devices, preventing any single system from becoming overwhelmed.
Solution Approach 2:
The distributed computing architecture allows multiple computing devices to perform the same time series analysis functions simultaneously on different data partitions. This multi-functionality enables the system to process larger datasets for better decision-making accuracy without requiring a single powerful hardware system, as each device performs universal analysis tasks on its assigned data.
Data Source
AI summary
In some examples, computing devices can partition timestamped data into groups. The computing devices can then distribute the timestamped data based on the groups. The computing devices can also obtain copies of a script configured to process the timestamped data, such that each computing device receives a copy of the script. The computing devices can determine one or more code segments associated with the groups based on content of the script. The one or more code segments can be in one or more programming languages that are different than a programming language of the script. The computing devices can then run the copies of the script to process the timestamped data within the groups. This may involve interacting with one or more job servers configured to run the one or more code segments associated with the groups.