Vector Intelligence Network for Object Network Modeling
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Solution Overview
Problem
Conventional information systems are not designed to leverage the enhanced processing power and expanded memory capabilities of modern and future computer hardware, such as 64-bit CPUs and GPGPUs, limiting their ability to efficiently model object networks and perform similarity matching.
Innovation Solution
A Simarray Vector Intelligence Network with real-time associative object clustering and memory-based operations, utilizing vector fingerprinting, dimension indexing, and vector space model filtration to form and manage object networks, allowing for explicit and implicit association modeling without offline indexing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional database systems are used, then data storage and basic querying are supported, but they cannot leverage enhanced processing power and expanded memory space of modern hardware
Solution Approach 1:
The patent replaces conventional database management systems with a vector space model-based information system that leverages modern hardware capabilities. The system substitutes traditional disk-based database operations with memory-based vector operations, utilizing enhanced processing power and expanded addressable memory space to achieve superior performance in information retrieval and association discovery.
2Power
If conventional information systems are used, then basic information retrieval is possible, but they are ill equipped to leverage parallel processing capabilities of advanced CPUs and GPGPUs
Solution Approach 1:
The patent implements a dynamic system that adapts to parallel processing architectures. The vector space model enables operations to be distributed across multiple processing units, with the system dynamically utilizing available parallel processing resources in CPUs and GPGPUs to accelerate similarity matching and association discovery operations.
3Speed
If offline indexing is used in conventional systems, then query processing is simplified, but it increases time consumption and cannot achieve real-time performance
Solution Approach 1:
The patent performs preliminary actions by pre-computing and storing vector representations of objects in memory during data ingestion. This preliminary vectorization enables real-time similarity matching operations without requiring offline indexing, as the vector space model allows for efficient on-the-fly comparison of newly ingested objects against existing vectors.
4Productivity
If memory-based operations are implemented, then real-time processing is achieved, but system complexity increases
Solution Approach 1:
The patent changes the fundamental parameter of data representation from traditional database records to vector space representations. This parameter change enables memory-based operations and real-time processing, as vectors can be efficiently stored in expanded memory space and subjected to rapid mathematical operations that leverage modern hardware capabilities.
Data Source
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AI summary
According to one embodiment, a system is provided. The system includes a memory, at least one processor coupled to the memory and an object network modeler component executable by the at least one processor. The memory stores an object network including a plurality of objects, the plurality of objects including a first object, a second object, a third object, and a fourth object. The object network modeler component is configured to implicitly associate, within the object network, the first object with the second object and explicitly associate, within the object network, the third object with the fourth object.