Time-Series Date Projection for Faster Multi-Condition SQL Filtering
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Solution Overview
Problem
Existing methods for analyzing time-series data using relative date conditions in structured query environments are inefficient and resource-intensive, particularly when dealing with high data volumes, due to the need to generate and filter all possible date groupings, leading to prolonged query times and resource usage inefficiencies.
Innovation Solution
The technology filters time-series data by applying a sequence of projection operators directly on user-specified date ranges, avoiding row-expanding and hashing, and dynamically maps input dates to target dates, enabling efficient filtering and projection of data that meets multiple conditions in a single query.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all possible date groupings are generated and filtered using multiple JOINs in a SQL database, then complete data coverage is achieved, but query time exceeds 10 minutes and resource usage becomes inefficient
Solution Approach 1:
The patent segments the date range into discrete daily intervals and processes each day independently through a window function, avoiding the need to generate and filter all possible date groupings. This segmentation allows the system to evaluate conditions efficiently for each day without combinatorial explosion of date combinations.
Solution Approach 2:
The patent introduces a calendar mapping table as an intermediary structure that pre-defines valid date ranges and their relationships. This intermediary enables efficient joining and filtering operations by providing a standardized reference framework, reducing the complexity of direct multi-table joins on raw time series data.
2Measurement precision
If row-per-day projection is applied to 100 million data rows, then detailed daily analysis is enabled, but processing time exceeds 10 minutes
Solution Approach 1:
The patent performs preliminary filtering and date mapping operations before the final projection step. By pre-evaluating conditions and identifying valid date ranges using window functions, the system prepares the data in advance, so that the actual row-per-day projection only processes already-filtered results, dramatically reducing processing time.
Solution Approach 2:
The patent uses window functions to skip unnecessary intermediate processing steps. Instead of generating all possible date groupings and then filtering them, the window function directly computes the valid date ranges and projections in a single pass, rushing through the computation by eliminating redundant operations.
3Measurement precision
If multiple JOINs are used to filter customized date ranges, then accurate date-based filtering is achieved, but resource usage becomes inefficient
Solution Approach 1:
The patent merges multiple filtering conditions and date range validations into a single window function operation. Instead of performing separate JOINs for each filtering condition, the window function consolidates all date-based logic into one efficient computational step, reducing the number of database operations and associated resource consumption.
Solution Approach 2:
The patent changes the computational parameters by switching from a join-based filtering approach to a window function-based approach. This parameter change transforms the operation from multiple sequential joins to a single pass computation that evaluates all conditions simultaneously, improving resource efficiency while maintaining filtering accuracy.
Data Source
AI summary
Aspects of the subject technology relate to systems, methods, and computer-readable media for dynamically filtering time series data using multiple conditions in a structured query environment. An example method can include receiving, in a structured query environment, a data request, which includes a plurality of conditions associated with a time period. The example method further includes accessing a database, which is configured to store time-series data corresponding to the time period and mapping a set of dates based on the plurality of conditions associated with the time period. The example method also includes applying a sequence of filters corresponding to the plurality of conditions, onto the set of dates, within a single query to the database and obtaining, from the database, data corresponding to filtered dates.