Live Event Search Indexing for Relevant Real-Time Results
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Solution Overview
Problem
Existing search systems fail to efficiently surface live events in search results, requiring users to submit multiple queries to find relevant information, which increases system resource demand.
Innovation Solution
A system that identifies and selectively provides graphical representations of live events in search results by using a live event index, determining quality scores, and including events in search results based on entity matching and time proximity to the query.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users submit multiple queries to find live events, then they can locate relevant information, but system resource demand increases
Solution Approach 1:
The system pre-processes and indexes live event data from multiple sources before user queries are submitted. This preliminary action creates a structured live event index that enables direct matching with user queries, eliminating the need for users to submit multiple iterative queries and reducing system resource demand during query processing
Solution Approach 2:
A live event index structure acts as an intermediary between raw live event data and user queries. This intermediary layer pre-organizes event information by entities, topics, and timeframes, enabling efficient retrieval without requiring multiple user queries and reducing the computational burden on the search system
2Quantity of substance
If the system includes all live events in search results, then completeness is improved, but relevance to user query decreases
Solution Approach 1:
The system applies different quality filters to different portions of the live event data based on their relevance to the user query. Events are selectively included or excluded based on entity matching, topic alignment, and temporal proximity, ensuring that only locally relevant events (those matching the specific query context) are included in the search results
Solution Approach 2:
The system dynamically adjusts inclusion parameters (entity matching criteria, time window thresholds, topic relevance scores) based on the specific user query. This parameter adaptation allows the system to optimize the balance between quantity and relevance for each query, including enough events to be comprehensive while filtering out irrelevant ones
3Quantity of substance
If the system processes all live events from multiple sources, then data completeness improves, but processing complexity increases
Solution Approach 1:
The system segments live event data from multiple sources into distinct, organized categories within the index structure, separating events by source, entity, topic, and timeframe. This segmentation allows the system to maintain complete data from all sources while processing and querying specific segments efficiently, reducing overall processing complexity
Solution Approach 2:
The system performs preliminary processing and normalization of live event data from multiple sources during the indexing phase, standardizing formats, resolving duplicates, and organizing by entities and topics before queries are submitted. This pre-processing reduces the complexity of subsequent query processing while maintaining data completeness
Data Source
AI summary
Implementations include actions of obtaining a set of entities based on one or more terms of a query, obtaining one or more entities associated with each live event of a plurality of live events, identifying a live event that is responsive to the query based on comparing at least one entity in the set of entities to one or more entities associated with each live event of a plurality of live events, determining that an event search result corresponding to the live event is to be displayed in search results, and in response: providing the event search result for display, the event search result including information associated with the live event, the information including an indicator of an occurrence of the live event.