Tenant and Commingling Processor Data Analysis Segmentation
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Solution Overview
Problem
Multitenant databases face inefficiencies in processing complex data analyses due to the computational load of commingled data, as existing systems struggle to handle the large-scale processing required for valuable data analyses across multiple tenants.
Innovation Solution
A system that determines and executes computational steps in subsets, utilizing both tenant-associated and commingling-associated processors, adhering to pipelining criteria to efficiently process and secure data analyses, with preprocessing, main processing, and postprocessing steps distributed across different processors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data analyses are performed by commingling tenant data on a single commingled data server, then data analysis capability is enabled, but the computational load becomes unmanageable and processing efficiency deteriorates
Solution Approach 1:
The patent segments the computational processing into two distinct parts: preprocessing steps executed by individual tenant data storage units and main processing steps executed by the commingling data server. This segmentation distributes the computational load, enabling complex data analyses while maintaining manageable processing efficiency by avoiding concentration of all computational tasks on a single server.
2Loss of information
If complex data analyses are performed on commingled data, then valuable insights are produced, but the computational load on the commingled data server increases significantly
Solution Approach 1:
The patent applies preliminary action by performing preprocessing steps at the tenant data storage units before data is commingled and sent to the main server. This includes filtering, aggregating, and preparing data locally, which reduces the volume and complexity of data that needs to be processed on the commingling server, thereby maintaining data analysis value while significantly reducing the computational load on the main server.
3Reliability
If tenant data is securely separated on different storage units, then data security is maintained, but efficient processing of commingled data becomes difficult
Solution Approach 1:
The patent segments the system into multiple tenant data storage units that maintain secure separation of data, while introducing a coordinated processing architecture where each unit performs preprocessing independently and results are aggregated for main processing. This segmentation approach preserves data security through physical and logical separation while enabling efficient processing through distributed computation, managing the complexity through standardized interfaces and protocols.
Data Source
AI summary
A system for performing a computation includes a tenant associated processor; a commingling associated processor; an interface; and a processor. The interface is configured to receive an indication of a computation. The processor is configured to determine a set of computational steps for performing the computation; determine a preprocessing subset, wherein the preprocessing subset comprises a subset of the set of computational steps for execution by the tenant associated processor; and determine a main processing subset, wherein the main processing subset comprises the set of computational steps for execution by the commingling associated processor.