Context-Aware Query Recommender for Log Search
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Solution Overview
Problem
On-call engineers face challenges in troubleshooting production issues due to the lack of expertise in formulating effective search queries for log data, which is essential for identifying root causes of problems.
Innovation Solution
A system and method that provide search query recommendations based on user context, leveraging techniques such as suggested searches, autocomplete functionality, and natural language processing to assist users in formulating queries and finding relevant information in log data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users without expertise search for log data manually, then they can access the data, but they struggle to formulate effective queries and take longer to find solutions
Solution Approach 1:
The system performs preliminary action by proactively generating and presenting relevant search queries before the user needs to search. When a user opens the log search interface, the system analyzes their user profile, recent alerts, and historical data to pre-generate and display targeted search queries, eliminating the need for the user to manually formulate complex queries from scratch.
Solution Approach 2:
The system implements feedback by continuously learning from user interactions and adjusting query recommendations accordingly. It monitors which queries users select, how long they spend on each query, and what results they view, then uses this feedback to refine future query recommendations, making the system increasingly effective over time.
2Productivity
If the system provides personalized query recommendations, then it improves user productivity, but it requires analyzing user context and alert data
Solution Approach 1:
The system performs self-service by automatically analyzing user profiles, alert data, and search history to generate personalized recommendations without requiring manual configuration. It self-adjusts based on user behavior patterns and automatically learns from interactions, eliminating the need for complex manual setup while delivering increasingly accurate recommendations.
Solution Approach 2:
The system applies parameter changes by dynamically adjusting recommendation parameters based on real-time conditions. It modifies query recommendations based on current user profiles, recent alert patterns, time of day, and historical performance data, allowing the system to adapt to changing conditions without requiring complex reconfiguration.
3Measurement precision
If the system analyzes user context and alert history to generate recommendations, then query relevance improves, but data processing requirements increase
Solution Approach 1:
The system extracts only the essential information needed for generating recommendations from the vast amount of available data. It selectively extracts key parameters from user profiles, recent alerts, and search history rather than processing all data comprehensively, achieving sufficient relevance with minimal computational resources.
Solution Approach 2:
The system applies partial action by processing only the necessary portion of data required for effective recommendations. It focuses on recent alerts and user-specific patterns rather than analyzing entire historical datasets, achieving adequate query relevance while significantly reducing processing requirements compared to comprehensive analysis.
Data Source
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AI summary
Techniques are presented for recommending queries to search log information. The system provides useful insights and recommendations based on user needs and queries by utilizing the user context, with information about the user activities (e.g., recent alerts) and the user configuration in the system (e.g., applications configured by the user), to provide recommendations. There may not be enough context for a new user to provide good recommendations, so the system determines the context based on the activities of other users, such as more experienced users or users investigating the same type of problem. Based on the context, the user recommends natural language queries (NLQ) or system queries to accelerate the search process and assist the user during an investigation. Further, NLQs may be converted to complex search queries that use the search query language, and the NLQs may also be used as part of the context for the subsequent recommendations.