Graph Database Search Using Log Node Pairs
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Solution Overview
Problem
Conventional database systems face inefficiencies in returning relevant search results, leading to user frustration due to the volume and irrelevance of search results, especially in cloud computing environments where users struggle to find specific information quickly.
Innovation Solution
Creating a weighted graph from user logs to rank search results based on relevance and importance, using log node pairs and weighted scores to prioritize data objects accessed frequently by users, thereby improving the accuracy of search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional database search systems are used, then search functionality is provided, but search results are voluminous and irrelevant, leading to user frustration
Solution Approach 1:
The system performs preliminary actions by creating and maintaining a graph data structure that pre-processes and organizes data objects based on user interactions. Before actual search occurs, the graph captures relationships and access patterns, enabling precise result ranking when queries are submitted.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user interactions with data objects and using this information to update the graph structure. User access patterns, preferences, and behaviors provide feedback that refines the graph's representation of data relationships, improving search relevance over time.
2Loss of time
If users search for specific information in cloud computing environments, then information can be found, but it takes significant time and effort
Solution Approach 1:
The graph data structure performs preliminary organization of data objects based on user interactions and access patterns. This pre-processing creates optimized search paths and relationships, allowing the system to quickly retrieve relevant information without requiring users to manually navigate through large datasets.
Solution Approach 2:
The graph structure acts as an intermediary between users and the vast amounts of stored data. It translates user queries into efficient search operations by leveraging pre-established relationships and access patterns, significantly reducing the time and effort required to retrieve specific information.
3Measurement precision
If a graph of nodes and edges is created from user logs, then search result accuracy is improved, but system complexity increases
Solution Approach 1:
The graph data structure serves multiple functions: it stores data object relationships, tracks user access patterns, enables search ranking, and provides a foundation for personalized recommendations. This multi-functionality consolidates what would otherwise require multiple separate systems into a single unified structure.
Solution Approach 2:
The system creates a simplified representation (copy) of user interactions and data relationships in the graph structure. This abstract representation captures essential patterns without storing raw log data, reducing complexity while maintaining the information needed for accurate search results.
Data Source
AI summary
Disclosed are examples of systems, apparatus, methods and computer program products for generating or updating a graph of nodes and edges using logs, where the graph can be used for database searching. In some implementations, a server can identify logs. Based on the identified logs, log nodes can be generated or updated. Log nodes can be sorted in a first ordered list according to each log node's key data. Log node pairs can be determined from the log nodes in the first list. The log node pairs can be aggregated and sorted into a second ordered list. A graph of nodes and edges can be generated or updated according to the relative importances of the log node pairs in the second ordered list.