Information Retrieval Scheduling From Historical Issuance Patterns
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Scheduling the collection of financial documents from third-party sources is challenging as it can result in either wasting computational resources by retrieving documents that are not yet available or keeping accounting systems out-of-date by retrieving them too late.
Innovation Solution
A method and system that determine a predicted time of information issuance based on historical data to schedule retrieval, using a scheduling application that analyzes successful retrieval times and cadences to optimize fetching runs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If information retrieval is performed frequently to ensure up-to-date accounting systems, then data freshness is improved, but computational resources are wasted when documents are not yet available
Solution Approach 1:
The system dynamically adjusts retrieval schedules based on learned patterns from historical data. Instead of using a fixed retrieval interval, the scheduling application modifies retrieval timing according to observed issuance patterns, allowing the system to adapt to varying document availability cycles and avoid both premature and delayed retrievals.
Solution Approach 2:
The system uses feedback from historical retrieval outcomes to improve future scheduling decisions. By analyzing past retrieval success rates and document issuance patterns, the scheduling application learns optimal retrieval timings and continuously refines the schedule, creating a closed-loop system that reduces resource waste while maintaining data freshness.
2Loss of energy
If information retrieval is performed less frequently to save computational resources, then resource efficiency is improved, but the accounting system becomes out-of-date
Solution Approach 1:
The system performs preliminary analysis of historical issuance patterns to predict future document availability before scheduling retrieval operations. By pre-processing historical data to identify cadences and patterns, the system can proactively schedule retrievals at optimal times, ensuring documents are retrieved as soon as they become available without requiring continuous monitoring.
Solution Approach 2:
The system changes the temporal parameters of retrieval operations based on learned patterns. Instead of using uniform time intervals, the scheduling application adjusts retrieval timing parameters according to observed issuance cadences, transforming the retrieval strategy from a static schedule to a pattern-adaptive schedule that optimizes both resource efficiency and data currency.
3Device complexity
If fixed-interval retrieval scheduling is used to simplify implementation, then system complexity is reduced, but retrieval efficiency deteriorates due to mismatched timing with actual document issuance
Solution Approach 1:
The scheduling application performs self-learning by automatically analyzing historical retrieval data and issuing patterns without requiring manual configuration. The system serves itself by generating optimized schedules based on its own operational history, eliminating the need for complex manual scheduling rules while improving retrieval efficiency through data-driven timing decisions.
Data Source
AI summary
Described embodiments relate to methods, systems and computer program product for scheduling retrieval of candidate information from a first entity system at the predicted time. The method comprises determining a dataset associated with historical information issued by a first entity, wherein the dataset comprises a plurality of entries, each entry comprising an information date; determining a period of successful retrieval of information issued by the first entity, or a period of issuance of information by the first entity, based on the information dates of the plurality of entries in the dataset; determining a predicted time of issuance of future information by the first entity based on the determined period; and scheduling retrieval of candidate information from a first entity system at the predicted time.