Query Burst Classification via Wavelet Transform and Clustering
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Solution Overview
Problem
Existing systems lack effective methods to detect, classify, and rank query bursts in real-time, which are essential for understanding user behavior, trends, and demand patterns in online environments, impacting merchandising, traffic management, and fraud detection.
Innovation Solution
A system comprising a query detection subsystem, classification subsystem, and ranking subsystem that uses modules like logging, cost association, frequency conversion, clustering, and wavelet transforms to identify and rank query bursts based on their frequency, cost analysis, and external data correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time query burst detection and classification is implemented, then user behavior insights and trend analysis are improved, but system complexity and computational resources increase
Solution Approach 1:
The system segments query bursts into different classes (e.g., expected vs. unexpected, normal vs. anomalous) using classification algorithms. This segmentation allows precise detection and analysis of different query patterns without requiring a single complex monolithic system, thereby improving measurement precision while managing system complexity through modular classification components.
Solution Approach 2:
The patent introduces intermediary components such as query log analysis modules and classification subsystems that act as mediators between raw query data and final insights. These intermediaries process and filter query data, enabling accurate burst detection without directly exposing the full complexity of the underlying system architecture.
2Loss of information
If comprehensive query analysis and classification is performed, then user behavior understanding is improved, but processing time and computational cost increase
Solution Approach 1:
The system performs preliminary actions by continuously logging and pre-processing query data in real-time, maintaining ready-to-analyze query patterns in structured formats. This preliminary organization of data allows for rapid classification and analysis when query bursts occur, reducing processing time while preserving comprehensive user behavior information.
Solution Approach 2:
The patent applies parameter changes by adjusting classification thresholds and analysis depth based on query characteristics. For example, the system can dynamically change the level of detailed analysis applied to different query types, processing simple queries faster while applying more comprehensive analysis only when necessary, thus balancing information retention with processing efficiency.
3Reliability
If query bursts are ranked and prioritized, then fraud detection and load management are improved, but computational resources and system complexity increase
Solution Approach 1:
The system applies local quality by implementing differentiated ranking and analysis strategies for different query categories. High-priority queries suspected of fraud receive more intensive computational resources and detailed ranking analysis, while normal queries use lighter processing. This localized application of computational effort improves fraud detection accuracy while optimizing resource utilization.
Solution Approach 2:
The patent uses parameter changes by dynamically adjusting ranking criteria and computational intensity based on query characteristics and system conditions. The system can change ranking parameters such as time sensitivity, query frequency thresholds, and analysis depth to match the specific context, enabling effective fraud detection with adaptive resource consumption rather than constant high computational overhead.
Data Source
AI summary
Query utilization in which a rate of a plurality of queries to a data source may be determined for a plurality of time periods. A cost may be associated with a query state transition. A query state may be assigned to a particular query on a particular time period of the plurality of time periods based on the rate of queries for the particular time period and the cost of the query state transition. A query burst may be identified during the plurality of time periods based on assignment of a query state to the plurality of queries. The query may exhibit a normal query state, a normal-to-deviated query state transition, and a deviated query state. A query burst identification module may identify a query burst during the plurality of time periods, a burst conversion module may convert the query burst to a wavelet using a wavelet transform, a clustering module may apply a clustering technique to the wavelet, and a query classification module may classify the query burst based on applying the clustering technique.