Universal Primitives for Mixed Data Storage
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data management systems face inefficiencies in storing and querying mixed numeric and non-numeric data, as traditional compression techniques like Run-Length Encoding may increase storage requirements when data lacks 'runs' of duplicates, and existing methods struggle with resource optimization and query performance.
Innovation Solution
The use of global dictionaries with universal primitives (UNIPs) that leverage the IEEE-754 standard for floating-point arithmetic to efficiently represent and manipulate non-numeric data, allowing for reduced storage and improved query performance by replacing non-numeric data with 64-bit universal primitives, which can be used as keys for data manipulation and queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If Run-Length Encoding compression technique is used, then storage requirements are reduced when data contains duplicate runs, but storage size increases when data lacks duplicate runs
Solution Approach 1:
The patent creates a universal compression framework that adapts to different data types (numeric and non-numeric) through a single system. The global dictionary and UNIP mechanism provide multi-functional compression capabilities that work effectively across diverse data patterns, eliminating the need for separate compression strategies for different data characteristics.
Solution Approach 2:
The system dynamically changes compression parameters based on data characteristics. By using global dictionaries and UNIPs, the compression approach adapts its methodology according to the specific data type and pattern being compressed, optimizing storage efficiency for both duplicate-heavy and unique data scenarios.
2Quantity of substance
If non-numeric data is stored directly, then data integrity is maintained, but storage efficiency and query performance deteriorate
Solution Approach 1:
The patent creates compact representations (UNIPs) that copy essential information from non-numeric data while maintaining referential integrity through global dictionaries. The UNIP serves as a compressed copy that preserves data meaning while reducing storage requirements, with the full data accessible through dictionary lookups when needed.
Solution Approach 2:
The global dictionary acts as an intermediary between the compressed UNIP representation and the original non-numeric data. This mediator layer enables efficient storage through UNIPs while maintaining data integrity by providing on-demand access to the complete original data through the dictionary structure.
3Use of energy by moving object
If traditional compression techniques are used, then resource requirements are reduced, but query performance and manipulation efficiency worsen
Solution Approach 1:
The system performs preliminary organization of data into global dictionaries during the data loading phase. This advance preparation creates optimized data structures that enable faster query performance later, trading initial processing time for improved operational efficiency during data retrieval and manipulation.
Solution Approach 2:
The patent segments data into distinct numeric and non-numeric components with dedicated handling mechanisms. By separating data types and applying specialized compression and storage strategies to each segment, the system optimizes both resource usage and query performance for different data categories simultaneously.
4Adaptability or versatility
If mixed numeric and non-numeric data is stored together, then data versatility is improved, but storage management and query efficiency worsen
Solution Approach 1:
The patent implements a universal storage management system that handles both numeric and non-numeric data through common mechanisms (global dictionaries, UNIPs, and standardized compression approaches). This multi-functional framework maintains data type flexibility while simplifying storage management by eliminating the need for separate handling procedures for different data types.
Data Source
AI summary
Embodiments are directed towards managing data storage and queries within a database system using global dictionaries with universal primitives (UNIPs) to represent non-numeric data within a mixed numeric/non-numeric environment. Common data types are managed within a same global dictionary through dictionaries that are globally used within the database system. At least non-numeric data within mixed data fields may be stored using a UNIP to identify the stored non-numeric data. The UNIP may take advantage of the IEEE-754 standard for floating point data representation by setting a first field within the UNIP to 0x7ff (HEX) to indicate that the data is non-numeric (NaN) and using remaining bits to store typed data, such as a date or unique indirect reference (e.g. a sequence number or file offset to larger piece of data). The UNIP may then replace the data within the database and be used during operations performed on the data.