Deriving Metrics from Queries via Pre-computed Caching
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Solution Overview
Problem
Computer systems face performance degradation due to slower data retrieval from disk storage compared to Random Access Memory (RAM), and existing technologies lack efficient methods to dynamically derive and utilize new data elements from user queries for enhanced user interfaces and system behavior.
Innovation Solution
A computer system that receives user queries, identifies operations satisfying specific criteria, and automatically derives new data elements, storing them for future use in various contexts, such as user interfaces, to enhance data processing and retrieval efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in disk storage, then storage capacity is increased, but data retrieval speed deteriorates
Solution Approach 1:
The system pre-computes and stores derived data elements (metrics, aggregations) in a cache or memory structure before they are requested. When a query arrives, the system checks if the derived element is already available in the pre-computed cache, avoiding the need to retrieve and process raw data from disk storage in real-time, thus resolving the contradiction between storage capacity and retrieval speed.
2Measurement precision
If manual data definition is required, then data accuracy is improved, but system complexity increases
Solution Approach 1:
The system automatically derives data elements by applying predefined operations (aggregations, calculations) to existing data fields based on query context. Instead of requiring manual definition of every metric, the system self-generates derived elements on-demand, reducing system complexity while maintaining data accuracy through consistent application of transformation rules.
Solution Approach 2:
The system creates a universal framework where a small set of base data fields can serve multiple purposes through different derived operations. The same base fields can generate various metrics (counts, sums, averages) depending on query context, reducing the need for extensive manual data definitions while maintaining accuracy through standardized derivation processes.
3Loss of time
If queries are processed in real-time, then responsiveness is improved, but computational load increases
Solution Approach 1:
The system pre-computes derived data elements and caches them before they are requested in queries. By having metrics and aggregations ready in advance in a cache structure, the system can respond to queries immediately without performing heavy computational operations in real-time, thus reducing both response time and instantaneous computational load.
Solution Approach 2:
The system extracts and separates the computationally intensive derivation operations from the query processing path. Derived elements are computed and cached independently, so that when queries arrive, only simple cache lookup and retrieval operations are needed, dramatically reducing the computational load during actual query execution while maintaining real-time responsiveness.
Data Source
AI summary
Methods, systems, apparatus, and computer-readable media for deriving data elements from queries. In some implementations, a query is received, and one or more computers determine that the query involves an operation that satisfies one or more criteria. In response data is saved that indicates a derived data element corresponding to the operation. The one or more computers provide data causing a representation of the derived data element to be presented, such as data causing an interactive control representing the derived data element to be presented on a user interface.