Analytics Brokerage System for Query Confidence Management
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Solution Overview
Problem
Selecting appropriate analytics engines for Big Data analysis is challenging due to the unwieldy nature of large data sets, with varying costs, accuracy, and responsiveness, making it inefficient to examine all data and engines, and often only a subset is used for decision-making, while certain algorithms or data sources are specific to certain engines.
Innovation Solution
A computer-implemented method that receives a query, establishes a target confidence level, assigns individual confidence levels to multiple analytics engines, selects a group based on similar query responses, and summarizes their responses to achieve the desired accuracy and cost-effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all analytics engines are examined to ensure comprehensive data analysis, then measurement precision and reliability are improved, but device complexity and loss of time increase significantly
Solution Approach 1:
The patent introduces a broker as an intermediary component that sits between the client and multiple analytics engines. The broker receives queries from clients, selects appropriate analytics engines based on predefined criteria and confidence levels, coordinates the analysis, and returns results. This intermediary abstraction layer eliminates the need for clients to directly manage and examine all analytics engines, reducing complexity while maintaining reliable results through systematic engine selection and validation.
2Measurement precision
If all data sources are examined to ensure comprehensive analysis, then measurement precision is improved, but loss of time and productivity decrease
Solution Approach 1:
The patent implements preliminary action by pre-establishing confidence levels for different analytics engines and pre-defining selection criteria before queries are received. When a query arrives, the broker can immediately evaluate which engines meet the required confidence thresholds and initiate analysis without needing to examine all data sources from scratch. This pre-preparation enables rapid, accurate responses by filtering and selecting appropriate engines based on predetermined standards rather than exhaustive examination.
3Reliability
If multiple analytics engines are used to improve accuracy, then measurement precision is improved, but loss of energy and cost increase
Solution Approach 1:
The patent applies local quality by assigning different confidence levels to different analytics engines based on their specific strengths and characteristics. Rather than treating all engines uniformly, the system evaluates each engine's suitability for particular query types and data sources, selecting only the engines that meet the required confidence threshold for each specific analysis task. This localized evaluation ensures that resources are allocated efficiently to engines that provide the most value for each particular query, avoiding unnecessary energy consumption and costs associated with employing all available engines for every analysis.
Data Source
AI summary
In one embodiment, a computer-implemented method includes receiving a query. A target confidence level is established for the query, the target confidence level representing a requested level of accuracy for a result of the query. At least one individual confidence level is assigned to each of a plurality of analytics engines. One or more analytics engines are queried based on the query. A group of the analytics engines are selected, by a computer processor, where the analytics engines in the selected group have query responses to the query that are deemed to be similar to one another, and where the selection of the selected group is at least partially based on the target confidence level. The query responses from the selected group of analytics engines are summarized into a final result, where the final result is an answer to the query.