Query Prediction and Data Partitioning for Search Speed
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Solution Overview
Problem
Current electronic search technologies for mapping applications are inefficient, requiring detailed queries and taking too long to provide relevant point-of-interest information, especially in mobile contexts where hardware limitations restrict user input and output.
Innovation Solution
Implementing a system that predicts full queries based on partial inputs and location data, distributing data across partitions based on perceived load and entropy weights to prioritize search results, allowing for real-time customization and efficient retrieval of relevant information without needing complete query submission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current electronic search technologies are used for mapping applications, then search functionality is provided, but the system requires detailed queries and takes too long to provide relevant information
Solution Approach 1:
The system performs preliminary actions by predicting the complete query intent before the user finishes typing. The query prediction module analyzes partial inputs and proactively generates predicted queries, executing searches in advance without waiting for complete user input. This resolves the contradiction by providing search results faster than traditional systems that must wait for full query submission.
Solution Approach 2:
The system applies partial action by processing and providing search results based on partial query inputs rather than requiring complete queries. The query prediction module works with incomplete user input (partial action) to generate relevant results, eliminating the need for users to type full detailed queries and significantly reducing query completion time.
2Measurement precision
If users must type detailed queries to get accurate results, then search precision is improved, but user effort and time increase
Solution Approach 1:
The system applies self-service by automatically completing the query formation process. The query prediction module autonomously analyzes partial inputs, predicts complete queries, and executes searches without requiring users to manually type detailed queries. This maintains search accuracy through intelligent prediction while dramatically improving ease of operation by eliminating the burden of detailed manual input.
Solution Approach 2:
The system performs preliminary query analysis and prediction based on partial inputs before the user completes their query. This preliminary action allows the system to proactively provide accurate search results without requiring users to invest time and effort in crafting detailed queries, thus resolving the contradiction between search precision and ease of operation.
3Productivity
If data is distributed across multiple partitions, then query processing efficiency is improved, but data distribution complexity increases
Solution Approach 1:
The system applies parameter changes by using dynamic parameters (user location, query type, historical data) to determine optimal data partition assignments. The load balancing module adjusts data distribution parameters based on real-time conditions, allowing efficient query processing across partitions while managing complexity through parameter-driven automation rather than static complex configurations.
Solution Approach 2:
The system implements feedback mechanisms where the load balancing module continuously monitors query performance and data access patterns across partitions. Based on this feedback, the system dynamically adjusts data distribution to optimize query processing efficiency. This feedback loop manages distribution complexity through automated adaptation rather than requiring manual complex configuration.
4Measurement precision
If location data analysis is incorporated into query processing, then result relevance is improved, but processing complexity increases
Solution Approach 1:
The system applies local quality by incorporating location data analysis specifically for mapping and location-based queries rather than all query types. The system enhances result relevance with location analysis where applicable (local quality) while maintaining simpler processing for non-location queries, thus improving relevance without uniformly increasing processing complexity across all operations.
Data Source
AI summary
Systems and methods are disclosed for partitioning data into n partitions in a database used for information retrieval. According to certain embodiments implemented with at least one processor, a perceived load is determined for each of a plurality of substrings, each of the substrings corresponding to one or more entries in the database. The plurality of substrings are ordered from highest to lowest perceived load. Data associated with each of the first n substrings is assigned to a separate partition, wherein n is greater than one. Moreover, data associated with each of the remaining substrings is assigned to the n partitions based on a score. The score is computed by calculating, for each partition, a load variance and an entropy weight for the candidate substring. The load variance and the entropy weight are combined, and the candidate substring is assigned to the partition that has the lowest combined score.