Hypothesis Orchestration System for Big Data Analysis
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Solution Overview
Problem
Current systems fail to effectively address the challenge of mining relevant information from large 'big data' repositories and data streams, lacking the ability to generate timely and accurate answers to complex questions, handle uncertainty, and provide intuitive user experiences for human insight and evidence analysis.
Innovation Solution
The Hypothesis Orchestration System combines probabilistic complex event processing, activity recognition, and predictive analysis to emulate human problem-solving by generating, updating, and scoring hypotheses, using a Value-Of-Information metric to identify missing evidence and reduce uncertainty, while providing an interactive user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated reasoning technology is implemented to mine relevant information from big data repositories, then information analysis efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments the complex reasoning task into distinct functional modules: hypothesis generation module, evidence collection module, hypothesis evaluation module, and value of information calculation module. Each module handles a specific aspect of the reasoning process, making the overall system more manageable and maintainable while achieving automated reasoning capabilities
Solution Approach 2:
The hypothesis orchestration system is designed as a universal platform that can handle multiple types of reasoning tasks and data sources through standardized interfaces. The system accommodates various inquiry types, data repositories, and analysis methods without requiring separate specialized systems, thereby improving productivity while controlling complexity through reusability
2Measurement precision
If multiple competing hypotheses are automatically formed and evaluated, then accuracy of answers to hard questions is improved, but computational resources required increase
Solution Approach 1:
The system evaluates multiple hypotheses but applies partial action by focusing computational resources on the most promising hypotheses based on preliminary scoring. Rather than exhaustively evaluating all possible hypotheses with equal depth, the system prioritizes evaluation based on initial plausibility assessments, achieving acceptable accuracy while reducing computational overhead
Solution Approach 2:
The system dynamically adjusts evaluation parameters such as confidence thresholds, evidence requirements, and hypothesis priority weights based on the specific inquiry and available data. This adaptive parameter adjustment allows the system to balance accuracy requirements with computational resource constraints by tightening or loosening evaluation criteria as appropriate
3Reliability
If the system handles uncertainty and maintains hypotheses over extended periods, then reliability of forensic analysis is improved, but time required for analysis increases
Solution Approach 1:
The system maintains hypotheses continuously over time, automatically updating them as new evidence becomes available from data streams and repositories. This continuous operation eliminates the need for manual re-evaluation and allows the system to accumulate evidence over extended periods, improving reliability without proportionally increasing analysis time through automation
Solution Approach 2:
The system implements feedback mechanisms where hypothesis evaluation results and evidence quality assessments automatically adjust future evaluation priorities and thresholds. This feedback loop allows the system to learn from previous analyses and optimize its time management, improving reliability through iterative refinement while reducing redundant analysis time
4Measurement precision
If the system provides interactive user experience for capturing human knowledge, then quality of predictive analysis is improved, but ease of operation decreases
Solution Approach 1:
The system introduces an intermediary layer that translates complex reasoning processes and hypothesis evaluations into user-friendly presentations. This intermediary handles the complexity of knowledge capture and hypothesis management internally while presenting simplified interactions to users, thereby maintaining high analysis quality without sacrificing ease of operation at the user interface level
Data Source
AI summary
Enterprise Hypothesis Orchestration provides users an intuitive system for building an inquiry model that thereafter creates and evaluates each of a plurality of hypotheses as it continuously searches for evidence to formulate, score, and resolve each hypothesis. The Enterprise Hypothesis Orchestration system moreover continuously deals with the uncertainty caused by noisy, missing, inaccurate, and/or contradictory data. The present invention uses abductive reasoning to infer the best explanation or hypothesis for a set of observations. Given an inquiry the Hypothesis Orchestration System identifies relevant data from which to form a plurality of hypotheses. It thereafter collects evidence in support of each hypothesis and crafts a degree of confidence that the hypothesis is true. If a hypothesis is found to lack support an analysis of any missing evidence is conducted to identify and seek which evidence would offer the highest benefit to resolving one or more of the plurality of hypotheses.