Dynamic Indexing Frequency Adjustment for Enterprise Search
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Solution Overview
Problem
Existing indexing and crawling engine solutions for cloud-based services rely on fixed time intervals for data retrieval, which do not account for dynamic user interactions and service usage patterns, leading to inefficient resource utilization and fragmented search capabilities across enterprises.
Innovation Solution
A method and system for dynamically adjusting the indexing frequency based on user interactions, service availability, and various restrictions such as API-request limits and cost considerations, using a user interaction monitor and service availability monitor to optimize indexing intervals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed time interval is used for indexing operations, then the indexing schedule is simple to implement, but resource utilization becomes inefficient and search accuracy deteriorates
Solution Approach 1:
The patent implements dynamic indexing frequency adjustment by monitoring data change rates and automatically adapting the indexing interval. The system transitions from a static fixed-time approach to a dynamic approach where the indexing frequency changes based on real-time monitoring of data modification rates, thereby optimizing resource utilization while maintaining implementation feasibility.
Solution Approach 2:
The patent changes the indexing frequency parameter based on monitored data change rates. When data changes frequently, the system increases indexing frequency to maintain search accuracy. When data changes slowly, the system decreases indexing frequency to conserve resources. This parameter adaptation resolves the contradiction between simple implementation and efficient resource utilization.
2Measurement precision
If indexing frequency is increased to improve search accuracy, then search performance improves, but resource consumption and costs increase
Solution Approach 1:
The patent dynamically adjusts the indexing frequency parameter based on monitored data change rates. By increasing indexing frequency only when data changes rapidly and decreasing it when changes are slow, the system maintains search accuracy while minimizing unnecessary resource consumption that would occur with continuously high-frequency indexing.
Solution Approach 2:
The system implements dynamic adaptation of indexing frequency to match actual data change patterns. This dynamic approach ensures search accuracy is maintained during periods of high data activity while reducing resource consumption during periods of low activity, resolving the contradiction between search precision and resource usage.
3Productivity
If indexing frequency is decreased to reduce resource consumption, then resource efficiency improves, but search accuracy and data freshness deteriorate
Solution Approach 1:
The patent adjusts indexing frequency parameter dynamically based on monitored data change rates. The system decreases indexing frequency to improve resource efficiency only when data changes slowly, while maintaining higher frequency when data changes rapidly to preserve search accuracy. This conditional parameter adjustment resolves the contradiction between resource efficiency and search accuracy.
4Ease of manufacture
If fixed time-based indexing is used, then implementation is straightforward, but adaptability to user interactions and service usage patterns is poor
Solution Approach 1:
The patent implements dynamic indexing frequency adjustment that adapts to monitored data change rates, user interactions, and service usage patterns. The system automatically modifies indexing frequency based on real-time conditions while maintaining a relatively simple monitoring and adjustment mechanism, thereby achieving both adaptability and implementation simplicity.
Solution Approach 2:
The indexing system monitors its own environment (data change rates, usage patterns) and automatically adjusts its indexing frequency without requiring complex external configuration or manual intervention. This self-adjusting capability provides adaptability to usage patterns while keeping the implementation relatively simple through automated decision-making.
Data Source
AI summary
A indexing engine and method are provided for operating an indexing engine that parses and indexes data created by a set of users associated with a business entity on a database while interacting with a service associated to the database, the method comprising: monitoring the users' interactions with the database related to a creation of new data by one or more of the users over a predefined period of time when using the service; monitoring service availability during these interactions over the same predefined period of time; and changing an indexing frequency based on both parameters associated with the monitoring of the users' interactions and the service usage conditions.