Incremental Visualization for Enterprise Data Query Execution
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Solution Overview
Problem
Current enterprise-level data processing tasks, such as database queries, are hindered by high computing resource costs, leading to inefficiencies and delays, which impede productivity and hinder meaningful insights from statistical analysis.
Innovation Solution
A simulation mechanism that executes queries over incremental samples, monitors user interactions, and provides partial query results, allowing for real-time decision-making and visualization, thereby reducing costs and enhancing user satisfaction by emulating an enterprise-level data store with a back-end data store.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If enterprise-level data processing tasks are executed over complete enterprise-level data stores, then measurement precision and reliability are improved, but loss of time and productivity deteriorate due to considerable processing time and computing resource consumption
Solution Approach 1:
The system executes queries over incremental samples (partial data) rather than complete enterprise-level data stores. By processing progressively larger portions of data and allowing early termination when sufficient accuracy is achieved, the system reduces query execution time while maintaining acceptable measurement precision for decision-making purposes.
Solution Approach 2:
The complete data store is divided into incremental samples that are processed in sequential iterations. Each iteration processes a portion of the data and returns partial results, allowing the system to balance between processing completeness and execution time based on user needs and resource constraints.
2Productivity
If conventional large-scale data stores use incremental query execution techniques, then productivity is improved by avoiding overnight waiting, but the accuracy and reliability of statistical analysis deteriorate due to incomplete query responses at initial iterations
Solution Approach 1:
The system monitors user interactions with partial query results and uses this feedback to determine when to terminate incremental execution. By analyzing user behavior patterns and engagement with the data, the system can identify when sufficient accuracy has been achieved for meaningful insights, allowing early termination without sacrificing productivity or analytical value.
Solution Approach 2:
The system dynamically adjusts the termination point of query execution based on real-time user interactions and engagement metrics. Rather than fixed iteration counts or time limits, the execution continues or terminates based on dynamic assessment of whether partial results are sufficient for user decision-making needs.
3Productivity
If systems present hypothetical results instead of actual results from enterprise-level data stores, then productivity is improved by enabling faster decision-making, but reliability deteriorates because hypothetical results do not accurately reflect actual results
Solution Approach 1:
The system executes queries over incremental samples in advance and presents partial results that represent actual (not hypothetical) data from the enterprise-level store. By preparing and presenting real partial results upfront, the system enables faster decision-making with credible, actual data rather than hypothetical projections.
Data Source
AI summary
The subject disclosure is directed towards simulating query execution to provide incremental visualization for a global data set. A data store may be configured for searching at least a portion of a global data set being stored at an enterprise-level data store. In response to a user-issued query, partial query results are provided to a front-end interface for display to the user. The front-end interface also provides statistical information corresponding to the partial query results in relation to the global data set, which may be used to determine when a current set of query results becomes acceptable as a true/accurate estimate.