Sparse Matrix User Behavior Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for analyzing user behavior data from web logs are cumbersome and expensive due to the unwieldy and disorganized nature of raw data, making it difficult to construct and analyze efficiently.
Innovation Solution
A software facility constructs and maintains a sparse matrix representation of user behavior information, using a fact table indexed in two dimensions with arrays of pointers to efficiently store and analyze user actions, allowing for efficient data retrieval and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional intermediate data structures are constructed from raw web log data, then user behavior analysis can be performed, but the construction process is laborious and the analysis is expensive
Solution Approach 1:
The patent applies preliminary action by constructing the fact table and intermediate data structures in advance, during the data collection phase, rather than building them when analysis is needed. The system pre-processes raw web log data into a structured fact table format with pre-computed aggregates, so that when analysis queries are executed, the heavy lifting of data transformation has already been completed. This eliminates the laborious construction process at analysis time and reduces computational costs.
Solution Approach 2:
The patent introduces an intermediary fact table structure that sits between the raw web log data and the analysis queries. This fact table serves as a mediator that pre-organizes data into meaningful aggregates and relationships, allowing analysis queries to operate on pre-processed data rather than raw logs. The intermediary structure includes pre-computed user sessions, page views, and behavioral metrics that bridge the gap between raw data and analytical needs.
2Ease of manufacture
If raw web log data is used directly for analysis, then no data transformation is needed, but the data is unwieldy and disorganized making analysis difficult and expensive
Solution Approach 1:
The patent applies segmentation by dividing the monolithic raw web log data into structured segments within the fact table. Data is segmented by user identity, session, page view, and behavioral event types. Each segment is organized into discrete rows and columns with specific meanings, transforming the unwieldy continuous log stream into manageable, queryable units. This segmentation allows analysts to work with specific data slices without dealing with the entire raw dataset.
Solution Approach 2:
The patent introduces additional dimensions to organize the data by creating a multi-dimensional fact table structure. Beyond the basic time dimension, data is organized along dimensions such as user identity, session, page, and event type. This dimensional organization transforms the one-dimensional sequential log data into a multi-dimensional cube structure that enables efficient analysis from multiple perspectives simultaneously, making the data more manageable and analytically powerful.
Data Source
AI summary
A facility for analyzing user behavior information is described. The facility receives a stream of user behavior information. The facility augments a representation of user behavior in accordance with the received stream of user behavior information. The representation augmented by the facility is a sparse matrix of user behavior facts. The facility analyzes the sparse matrix to obtain an analysis result for the received stream of user behavior.