Analytics SDK Precision via Segmented Data Processing
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Solution Overview
Problem
Existing analytics systems fail to effectively process uncertain data and provide ambiguous outputs, limiting their relevance for decision-making, as they typically mask uncertain data or execute queries with customized margins of error without considering different business scenarios.
Innovation Solution
An analytics system that processes API calls by parsing them into API call names and parameters, generates prediction values for interpreted data parameters, and performs analytics operations based on these values and rules to provide a visual representation of results, allowing for the inclusion of both certain and uncertain data in queries and iterative refinement of predictions to meet user criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If analytics systems mask uncertain data or execute queries with customized margins of error, then security is improved, but measurement precision deteriorates
Solution Approach 1:
The patent segments data into certain data and uncertain data, allowing different processing approaches for each type. Certain data is processed with standard precision while uncertain data is handled through multiple interpretations and prediction values, resolving the contradiction between security (masking) and precision (accurate representation).
Solution Approach 2:
The system changes the parameter of data representation by generating multiple prediction values (e.g., optimistic, pessimistic, most likely scenarios) for uncertain data parameters. This allows the system to maintain security by not exposing raw uncertain data while improving precision through structured prediction ranges and confidence intervals.
2Measurement precision
If analytics systems process only certain data, then measurement precision is improved, but adaptability deteriorates
Solution Approach 1:
The analytics system is designed to handle both certain data and uncertain data through a unified framework. It can process deterministic queries when data is certain and switch to probabilistic processing with multiple interpretations when data is uncertain, making the system universally applicable to diverse business scenarios while maintaining precision through appropriate methods for each data type.
Solution Approach 2:
The system dynamically adapts its processing approach based on the nature of the input data. When certain data is provided, it uses standard precise analytics; when uncertain data is detected, it automatically generates multiple prediction values and scenarios. This dynamic behavior improves adaptability without sacrificing precision in either data type processing.
3Adaptability or versatility
If analytics systems provide ambiguous outputs, then adaptability is improved, but measurement precision deteriorates
Solution Approach 1:
The system provides structured feedback through multiple prediction values with associated confidence levels and scenarios. Rather than ambiguous outputs, it delivers organized information including optimistic, pessimistic, and most likely predictions, allowing users to understand the range and reliability of results. This feedback mechanism maintains adaptability while improving precision through systematic presentation of uncertainty.
Data Source
AI summary
This disclosure generally relates to computer-implemented analytics, and more particularly to systems and methods for improved security and precision in executing analytics using SDKs. In one embodiment, an analytics system is disclosed, comprising: a processor; and a memory device operatively connected to the processor and storing processor-executable instructions for: receiving an application programming interface (API) call for a service; parsing the API call to extract an API call name and one or more API call parameters; generating prediction values for one or more interpreted-data parameters; obtaining one or more analytics rules; performing an analytics operation to generate an analytics result according to the one or more analytics rules based on the generated prediction values for the one or more interpreted-data parameters and the extracted one or more API call parameters; and generating a visual representation of the analytics result.