Database Indexing via Structure-Preserving Dimensionality Reduction
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Solution Overview
Problem
Traditional database systems face inefficiencies in querying large and complex datasets due to the need for metadata storage, which becomes cumbersome with hyperdimensionality, hypercardinality, sparsity, and schemalessness, leading to slow searches and increased processing power consumption.
Innovation Solution
Implementing a system that uses pluggable structure-preserving dimensionality reducers and set intersection primitives to eliminate the need for a metadata store, storing data in a unified key-value set representation that includes both metadata and data, allowing for efficient querying without accessing a separate metadata table.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional row-oriented data stores are used, then data can be stored with all field values, but query processing becomes slow because the system fetches additional extraneous field values from storage
Solution Approach 1:
The patent extracts only the relevant field values needed for query processing from the data store, rather than fetching entire rows. This is achieved through a columnar storage format where data is organized by fields, allowing the query processor to retrieve only the specific columns required by the query, thereby eliminating the waste of fetching extraneous field values.
Solution Approach 2:
The patent segments the data storage into columnar structures, separating different fields into distinct storage units. This segmentation allows the system to access only the specific column data needed for a given query, rather than retrieving entire rows, thus improving query processing efficiency and reducing unnecessary I/O operations.
2Productivity
If column-oriented data stores are used to improve query efficiency, then relevant data fetching is improved, but the system scales poorly when the dataset becomes complex
Solution Approach 1:
The patent merges the columnar storage format with a unified metadata representation that can handle complex datasets. By integrating metadata management directly into the columnar storage architecture and using a flexible key-value store for metadata, the system maintains query efficiency while scaling to handle complex, high-dimensional, and schemaless data without requiring separate complex metadata management systems.
Solution Approach 2:
The patent creates a universal data storage system that combines columnar storage with a flexible metadata layer capable of handling various data types and structures. This unified approach allows the same storage mechanism to efficiently handle both simple and complex datasets, including high-dimensional, sparse, and schemaless data, without requiring different storage strategies.
3Adaptability or versatility
If metadata stores are used to manage complex datasets, then data organization is improved, but storage requirements increase and search speed decreases
Solution Approach 1:
The patent merges metadata with the data itself by using a unified columnar storage format where metadata is stored alongside the actual data in the same storage structure. This eliminates the need for separate metadata stores, reducing overall storage requirements while maintaining the ability to organize and query complex datasets efficiently.
Solution Approach 2:
The patent implements local quality by storing metadata in a distributed manner within the columnar storage structure, where each column has its associated metadata stored locally with it. This allows the system to maintain adaptability for complex datasets while minimizing the additional storage overhead, as metadata is only stored where needed rather than in a centralized location.
4Adaptability or versatility
If metadata stores are used to manage complex datasets, then data organization is improved, but query search speed becomes slow
Solution Approach 1:
The patent merges metadata management with the columnar storage operations, allowing metadata to be accessed and processed in the same I/O operations as the actual data. This integration eliminates the need for separate metadata lookup steps, thereby maintaining query search speed while providing the adaptability needed to handle complex, high-dimensional, and schemaless datasets.
Data Source
AI summary
Embodiments of the present disclosure are directed to systems and methods for managing a database. In one or more examples, the system obtains input data comprising one or more data entries, where each data entry comprises one or more data items, and each data item comprises a field name and a field value. The system can generate a key-value set for each data item to obtain a plurality of key-value sets. Each key-value set includes at least a first key element comprising the field name of the respective data item and a second key element comprising the field value of the respective data item. The system can sort and store the plurality of key-value sets in the database. The system can further receive a query indicative of a field name or a field value, and generate, for display, an output based on retrieved key elements sets based on the query.