Atomized Data Segmentation for Dynamic Query Contexts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional database management systems face limitations in dynamically changing query contexts and sub-contexts, leading to constrained data interrogation and investigation capabilities due to embedded data relationships, data structure, and storage mechanisms, which hinder interactive, iterative, and immediate data analysis.
Innovation Solution
The Highly Atomized Segmented and Interrogatable Data Systems (HASIDS) approach provides granular data and relationship handling, allowing for real-time context changes through semantic metadata, innate indexing, and massive parallel processing, enabling flexible querying and efficient storage without the need for explicit indices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional database management systems use embedded data relationships and structured query languages, then data storage and retrieval are efficient, but the ability to dynamically change query contexts and perform flexible data interrogation is constrained
Solution Approach 1:
The patent segments data into highly atomized units called data elements, where each element is independently addressable and can be individually queried. This segmentation allows dynamic assembly of different data elements based on runtime query requirements, enabling flexible data interrogation without being constrained by pre-defined relationships. The system divides complex data structures into fundamental atomic units that can be recombined arbitrarily to support adaptive query contexts.
Solution Approach 2:
The system implements dynamic data relationships where associations between data elements are not fixed in the data structure but are created and modified at query execution time. The query context can be dynamically changed by selectively associating and dissociating data elements based on the specific interrogation needs, allowing the system to adapt to varying analytical requirements without structural reconfiguration.
2Speed
If conventional systems pre-compute data across all relevant hierarchies to enable rapid analysis, then slice-dice and roll-up operations are fast, but the system cannot freely define run-time relationships and dynamic contexts
Solution Approach 1:
The system performs preliminary atomization of all data into fundamental data elements during data ingestion, preparing them in a standardized format with unique identifiers. This preliminary action enables rapid querying later without requiring pre-computation of specific analytical views, as the atomized data can be quickly assembled into any required configuration on-demand.
Solution Approach 2:
The system changes the parameter of data organization from fixed hierarchical structures to flexible parameter-based associations. Data elements can be associated with multiple contexts through parameter tags and metadata, allowing the same data to be rapidly reconfigured for different analytical perspectives without pre-computation, achieving both speed and adaptability.
3Speed
If conventional database systems use explicit indices to speed up data retrieval, then query performance is improved, but storage overhead and system complexity increase
Solution Approach 1:
Each data element contains its own metadata and identification information embedded within it, making the data self-descriptive and self-locatable. This self-service approach eliminates the need for separate index structures, as the data elements can be directly identified and retrieved using their intrinsic identifiers without requiring external indexing mechanisms.
Solution Approach 2:
The patent merges the data value, metadata, and identification information into a single unified data element structure. By combining what were previously separate components (data, indexes, and metadata) into an integrated atomized unit, the system achieves fast retrieval without the storage overhead of duplicate index structures.
4Reliability
If conventional systems maintain centralized and controlled data structures for data integrity, then data security and consistency are ensured, but interactive and iterative data interrogation is hindered
Solution Approach 1:
The system segments data into atomic units that maintain their integrity independently while allowing flexible combinations. Each data element remains a reliable, immutable unit with guaranteed consistency, yet multiple elements can be freely associated and dissociated to support interactive and iterative querying without compromising the integrity of individual elements.
Data Source
AI summary
Systems and methods for data interrogation and investigation using the exemplary Highly Atomized Segmented and Interrogatable Data System (HASIDS) include receiving a source set of data elements and associating a common key with each of at least a subset of the source set of data elements. One or more unary keysets are generated, with each unary keyset corresponding to one of the data elements of the subset, and each unary keyset being single-dimensional and comprising the common key. The HASIDS allows flexible querying and efficient storage and processing of the unary keysets.