Query Manager Service for Remote Analytics Data Extraction
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Solution Overview
Problem
Data maintenance and analysis across multiple data centers incur technological inefficiencies due to significant resource requirements for data transmission and storage of redundant data, as well as challenges in preparing analytical artifacts tailored to specific user needs.
Innovation Solution
Implementing computer-implemented methods for data analytics through a remote services plug-in, where a query manager service in one data center receives and validates query definitions from an analytics platform in another data center, generating analytical artifacts that can be executed within the first data center and transmitted for analysis, thereby reducing the need for data replication and enhancing analytical efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is transmitted from the first data center to the second data center for analysis, then data analysis can be performed in a separate analytics environment, but significant technical resources (processors, memory, bandwidth) are consumed
Solution Approach 1:
The patent extracts only the necessary query definitions and analytical metadata from the analytics platform in the second data center, rather than transmitting entire datasets. The calculation scenarios are defined remotely and only the results are transmitted back, eliminating the need to move large volumes of data and reducing bandwidth and processing resource consumption.
Solution Approach 2:
The patent introduces an intermediary mechanism where calculation scenarios are defined in the analytics platform but executed against data in the transactional system through a coordinated interface. This intermediary approach allows the analytics platform to access transactional data without physically copying it, reducing resource requirements while maintaining analytical capabilities.
2Adaptability or versatility
If redundant data is stored in multiple data centers (production data in first data center, copy in second data center), then data analysis can be performed independently, but technical resources (memory) are burdened
Solution Approach 1:
Instead of storing redundant copies of production data in the analytics platform, the patent extracts only the query definitions and calculation scenario descriptions. The actual data remains stored once in the transactional system, eliminating duplicate data storage while enabling independent analytical operations through remote query execution.
Solution Approach 2:
The patent creates lightweight copies of query definitions and calculation scenarios in the analytics platform rather than copying the actual data. These copied metadata elements are sufficient to define analytical operations without requiring duplicate data storage, reducing memory burden while maintaining analytical independence.
3Manufacturing precision
If query definitions are validated and analytical artifacts are generated in the first data center, then analytical precision is improved, but processing time increases
Solution Approach 1:
The patent performs query validation and analytical artifact generation in advance, before actual data analysis is needed. Calculation scenarios are defined, validated against analytical metadata, and prepared for execution ahead of time. This preliminary preparation ensures analytical precision is established upfront, while the actual data processing can then proceed efficiently without re-validation delays.
Data Source
AI summary
Methods, systems, and computer-readable storage media for receiving, by a QMS executed within a first data center, a query definition provided from a browser of an analytics platform executed within the second data center, the query definition being stored in a query repository, being validated by the QMS based on analytical metadata, and being stored in a mirror query repository of the first data center, providing, after validation of the query definition, a calculation scenario as an object model based on the query definition, executing, by a calculation engine within the first data center, the calculation scenario to provide an analytical artifact including a query-level calculation view on transactional data stored in the first data center, and transmitting, by the first data center, the analytical artifact to the second data center to perform analytics on at least a portion of the transactional data stored in the first data center.