Database Indexing for Relative Attribute Queries
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Solution Overview
Problem
Relational databases face challenges in efficiently querying and indexing relative attributes across multiple data entities, leading to difficulties in analyzing temporal relationships and overlooking valuable information due to combinatorial complexity and scalability issues.
Innovation Solution
An indexing system that continuously indexes data entries using a key database and key space database, generating vectors for relative attributes and keys to enable efficient querying and discovery of relative attributes across large datasets, leveraging cloud computing resources for scalable performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If relational databases store data in a structure of rows and columns with keys for information lookup, then data retrieval for individual entities is efficient, but analysis of temporal relationships and combinations of attributes across multiple entities becomes combinatorially complex and prohibitive at scale
Solution Approach 1:
The patent segments the monolithic relational database structure into multiple specialized index structures (e.g., temporal indexes, attribute combination indexes). Each index handles specific types of queries independently, breaking down complex combinatorial problems into manageable segments that can be processed separately and efficiently combined.
Solution Approach 2:
The patent introduces additional indexing dimensions beyond traditional row-column storage by creating multi-dimensional indexes that simultaneously index multiple attributes and temporal relationships. This allows queries to traverse indexed paths in multiple dimensions rather than performing combinatorial searches across flat tables.
2Reliability
If relational databases maintain data on a per-entity basis, then data integrity for individual entities is preserved, but continuous indexing and querying of relative time relationships across entities becomes combinatorially prohibitive
Solution Approach 1:
The patent implements preliminary action by pre-computing and storing temporal relationships and attribute combinations in dedicated index structures during data insertion or batch processing. This preliminary indexing eliminates the need for combinatorial computations during query execution, significantly improving productivity while maintaining data integrity through structured index updates.
3Loss of time
If existing relational databases use traditional indexing methods, then simple lookups are efficient, but discovery of relative relationships and temporal patterns across large datasets is hindered by scalability limitations
Solution Approach 1:
The patent creates universal index structures that serve multiple functions simultaneously: they enable fast point lookups, support range queries for temporal patterns, facilitate aggregation operations, and enable pattern discovery across diverse query types. This multi-functionality eliminates the need for separate indexing strategies for different query patterns.
Solution Approach 2:
The patent introduces intermediate index structures that act as mediators between the base relational tables and complex analytical queries. These intermediate structures pre-process and organize data in ways that accelerate both simple lookups and complex pattern discovery, bridging the gap between transactional efficiency and analytical versatility.
Data Source
AI summary
An indexing system indexes data entries in a database and processes queries of the database. The indexing system generates a key database storing keys that each represent a vector of a relative attribute between two data entries. For instance, a temporal distance indicates a relative time between timestamps of events represented by the two data entries. In other embodiments, relative attributes may be based on physical proximity, altitude, semantics, etc. The indexing system may also generate a key space database to enable efficient lookup of events associated with a target entity for indexing new data entries. For example, the indexing system may perform one read operation using the key space database to lookup all events associated with the target entity. Furthermore, the indexing system may determine responses for queries to retrieve events that occurred within a target temporal distance before or after a target type of event.