KStore Node Counting for Probable Path Determination
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Solution Overview
Problem
Existing database storage technologies, specifically interlocking trees datastores, face challenges in efficiently processing and traversing particle streams to determine the most probable node, particularly when learning is off and no match is found, leading to inefficiencies in updating node counts and determining next nodes.
Innovation Solution
A method is introduced for processing particle streams in a KStore system by receiving particles, determining matches, and updating node counts based on traversal events, using a count field to increment or decrement values depending on learning or query functions, and determining the most probable node based on these counts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system traverses the K structure to process particles, then node matches are determined, but when learning is off and no match is found, the system cannot efficiently determine the most probable next node
Solution Approach 1:
The system performs preliminary actions by maintaining count fields that record the number of times each node has been traversed during learning operations. These counts are updated in advance during learning phases, so that when query operations occur and no exact match is found, the system can immediately determine the most probable node based on pre-computed traversal statistics, eliminating the need for exhaustive traversal.
Solution Approach 2:
The system implements feedback mechanisms where the count fields are continuously updated based on actual traversal patterns during learning operations. This feedback loop allows the system to learn from actual usage patterns and adjust node probabilities accordingly, enabling efficient prediction during query operations without re-traversing the entire K structure.
2Measurement precision
If count fields are updated for every node traversal, then node probability determination is improved, but the complexity of tracking and updating counts increases
Solution Approach 1:
The K structure performs self-service by automatically updating its own count fields during normal operation. The system leverages the existing node traversal mechanisms to simultaneously perform both the primary function (particle processing) and the secondary function (count updating), eliminating the need for separate complex tracking systems.
Solution Approach 2:
The system merges the count tracking function with the existing node traversal and processing functions. By combining these operations, the system updates counts as a byproduct of normal particle processing rather than requiring separate tracking mechanisms, thereby reducing overall system complexity while maintaining measurement precision.
3Reliability
If the system processes every particle through complete K structure traversal, then all matches are found, but processing time increases significantly
Solution Approach 1:
The system applies partial action by using count field information to determine the most probable node without necessarily traversing the entire K structure. When learning is off and no exact match is found, the system can stop traversal early and use pre-computed counts to identify the most likely node, reducing processing time while maintaining acceptable reliability.
Solution Approach 2:
The system implements skipping by using count field data to rapidly identify the most probable node without exhaustive traversal. The count fields serve as a shortcut mechanism that allows the system to skip through portions of the K structure that are unlikely to contain matches, significantly reducing processing time while maintaining search completeness.
Data Source
AI summary
The process of traversing a K may involve determining a match between a root node and a Result node of a node on the asCase list of a current K node. When learning is off and a match is not found, the procedure may ignore the particle being processed. An alternative solution determines which node on the asCase list is the most likely to be the next node. While the K Engine is traversing and events are being recorded into a K structure, a count field may be added to each K node to contain a record of how many times each K path has been traversed. The count field may be updated according to the processes traversing the K. Typically, the count is incremented only for learning functions. This count field may be used in determining which node may be the most (or least) probable.