Semantic Reasoner Epoch-Based Rule Processing
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Solution Overview
Problem
Current data center networks face challenges in operational efficiency and resource optimization due to the complexity of managing diverse applications and services, with existing technologies requiring manual rule entry and inefficient data acquisition processes, leading to increased processing costs and human intervention.
Innovation Solution
A semantic reasoner system that constructs a dependency chain for rules in a knowledge base, assigns them to epochs, and performs machine reasoning sequentially, enabling just-in-time data acquisition and incremental processing of changes, reducing the need for full re-processing of large rule sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual rule entry and traditional data acquisition processes are used, then system implementation is straightforward, but operational efficiency is reduced and human intervention is increased
Solution Approach 1:
The semantic reasoner system automatically acquires data from data sources and processes rules without human intervention. The system self-services by constructing dependency chains, determining data acquisition orders, and performing machine reasoning autonomously, eliminating the need for manual rule entry and data collection
Solution Approach 2:
The patent replaces manual mechanical processes (human entry of rules and acquisition of data) with automated machine-based systems. The semantic reasoner uses machine learning and automated reasoning algorithms to perform tasks that previously required human operators, thereby improving operational efficiency and reducing human intervention
2Reliability
If full re-processing of large rule sets is performed, then completeness of analysis is ensured, but processing time and resource usage increase
Solution Approach 1:
The patent segments the large rule set into smaller epochs based on a constructed dependency chain. Rules are divided into multiple processing stages (epochs) where each epoch contains a subset of rules that can be processed independently. This segmentation allows the system to process only necessary portions of the rule set at each step, reducing overall processing time while maintaining analysis completeness through sequential epoch processing
Solution Approach 2:
The system performs preliminary actions by constructing the dependency chain before processing rules. This dependency chain pre-analyzes rule relationships and determines the optimal acquisition order of data objects. By preparing this structure in advance, the system avoids redundant processing during the actual rule evaluation phase, ensuring both completeness and efficiency
3Loss of information
If comprehensive data is acquired upfront, then all possible reasoning outcomes are available, but data acquisition costs and processing overhead increase
Solution Approach 1:
The patent applies partial action by acquiring only the necessary subset of data objects required for each specific reasoning task, rather than acquiring all possible data upfront. The dependency chain identifies which data objects are actually needed for rule processing, allowing the system to fetch only those specific data items, thereby reducing data acquisition costs while maintaining sufficient information completeness for accurate reasoning
Solution Approach 2:
The system dynamically determines data acquisition based on the specific reasoning requirements and the constructed dependency chain. Rather than a static upfront acquisition of all data, the system adaptively selects and acquires data objects in the order determined by the dependency chain, optimizing resource usage based on actual processing needs while ensuring all necessary information is obtained
Data Source
AI summary
An aspect of the present disclosure aims to reduce problems associated with data acquisition of a rule set. Systems and methods enabling a semantic reasoner to stage acquisition of data objects necessary to bring each of the rules stored in the knowledge base to a conclusion are disclosed. To that end, a dependency chain is constructed, identifying whether and how each rule depends on other rules. Based on the dependency chain, the rules are assigned to difference epochs and reasoning engine is configured to perform machine reasoning over rules of each epoch sequentially. Moreover, when processing rules of each epoch, data objects referenced by the rules assigned to a currently processed epoch are acquired according to a certain order established based on criteria such as e.g. cost of acquisition of data objects. Such an approach provides automatic determination and just-in-time acquisition of data objects required for semantic reasoning.