Pixel-Aware Query Rewriting for Time-Series Visualization
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Solution Overview
Problem
Conventional visual data-analysis tools face high latency and bandwidth issues when handling large volumes of time-series data due to the transfer of unnecessary data from database management systems (DBMS) to visualization clients, leading to unresponsive interfaces and impaired system performance.
Innovation Solution
A query rewriter associated with the DBMS or visualization client rewrites initial database queries based on visualization parameters to reduce data transfer by selecting only the data points necessary for rendering, using aggregation operators to focus the query results on values that affect visualization, thereby reducing data volume and improving interaction speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a non-aggregating SQL query is used to retrieve time-series data, then complete data accuracy is maintained, but data transfer volume becomes excessively large (24 GB total)
Solution Approach 1:
The patent extracts only the essential data points needed for visualization rendering from the complete time-series dataset. By identifying and retrieving only the data points that directly affect visualization output (such as points at pixel boundaries and extreme values), the system transfers minimal data while preserving visualization accuracy, reducing transfer volume from 24 GB to a manageable size.
Solution Approach 2:
The patent applies partial action by selecting a subset of data points that are sufficient for accurate visualization rather than transferring all available data. The selective sampling approach retrieves only the necessary portion of data (points that affect rendering), avoiding the excessive data transfer of complete datasets while maintaining visualization fidelity.
2Loss of information
If all time-series data is transferred from DBMS to visualization client, then complete data is available for analysis, but latency increases significantly
Solution Approach 1:
The system extracts only the critical data points required for visualization from the complete time-series dataset. By identifying and transferring only these essential points (such as boundary points and extreme values), the patent minimizes data transfer time and latency while ensuring that all information necessary for accurate visualization is included.
3Loss of information
If high-volume time-series data is processed by visualization client, then detailed analysis is possible, but system memory is overwhelmed
Solution Approach 1:
The patent extracts only the essential data points needed for visualization rendering, significantly reducing the data volume that must be processed and stored in memory. By retrieving only points that affect visualization output rather than all time-series data, the system maintains analysis capability while preventing memory overload.
4Loss of energy
If data reduction is applied at DBMS side, then bandwidth consumption is reduced, but visualization accuracy may be degraded
Solution Approach 1:
The patent extracts specifically those data points that are essential for accurate visualization rendering, such as points at pixel boundaries and extreme values. This selective extraction ensures that visualization accuracy is maintained while achieving significant bandwidth reduction, as only the critical subset of data is transferred rather than the complete dataset.
Solution Approach 2:
The patent applies local quality by differentiating between data points based on their importance for visualization. Critical points (boundary points, extreme values) are retained and transferred, while non-critical points are excluded. This selective approach ensures high visualization accuracy in critical areas while reducing overall data transfer volume.
Data Source
AI summary
A query rewriter associated with a database management system or visualization client rewrites a database query based on properties, characteristics, etc. of the visualization to be rendered by the visualization client. For example, the query rewriter receives an initial query and one or more visualization parameters (such as width, height and/or type of visualization) for the visualization client. The query rewriter rewrites the initial query based on the visualization parameter(s), so as to produce a data-reducing query, and then outputs the data-reducing query for execution. The query rewriter can selectively rewrite the initial query depending on size of query results of the initial query. In some example implementations, the query rewriting models a process of rasterization of geometric primitives by the visualization client, so as to facilitate error-free visualization. In many cases, the query rewriter significantly reduces the volume of query results while facilitating fast interaction with the visualization.