Provenance Analysis System Confidence Propagation
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Solution Overview
Problem
Existing frameworks for tracking and visualizing provenance metadata in information analysis fail to effectively assess the risk, confidence, and trustworthiness of insights generated by diverse teams of humans and machines, as they cannot propagate confidence attributions and update visualizations dynamically in response to changes or refutations.
Innovation Solution
A system and method for distributed provenance tracking that allows human and software agents to propagate confidence attributions and update visualizations dynamically, enabling users to assess the impact of refutations on insights and the sensitivity of analysis to individual or class elements, by using a provenance analysis system with a knowledge store, agent interface, provenance subgraph retriever, and visualizer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional provenance frameworks are used to track information lineage, then the basic provenance metadata can be recorded, but the system cannot effectively assess risk, confidence, and trustworthiness of insights generated by diverse agents
Solution Approach 1:
The provenance tracking system is segmented into distinct functional modules: a provenance graph structure for storing lineage data, a confidence propagation engine for calculating trustworthiness, and a risk assessment module for evaluating insights. This segmentation allows each module to specialize in specific tasks, improving reliability assessment capabilities while managing system complexity through modular design.
Solution Approach 2:
A confidence propagation mechanism serves as an intermediary layer between the provenance graph data structure and the risk assessment functions. This intermediary automatically calculates and propagates confidence scores through the provenance chain, mediating between raw provenance metadata and meaningful trustworthiness assessments, thereby enhancing reliability without requiring complex custom implementations.
2Productivity
If diverse software and human agents are used to process data, then productivity and insight generation are improved, but risk and complexity increase
Solution Approach 1:
The system implements feedback loops where confidence scores and risk assessments are continuously propagated back through the provenance graph. When new insights are generated by diverse agents, the system automatically feeds back confidence evaluations based on the agents' track records and the quality of their inputs, enabling continuous risk assessment that scales with productivity.
Solution Approach 2:
The system dynamically changes parameters such as confidence scores and risk weights based on agent performance and input quality. By adjusting these parameters in real-time as diverse agents contribute to insight generation, the system maintains reliable risk assessment even as productivity increases through the use of multiple agents.
3Measurement precision
If confidence attributions are propagated through the provenance chain, then trustworthiness assessment is improved, but computational complexity increases
Solution Approach 1:
The confidence propagation mechanism is designed to be dynamic and adaptive, automatically adjusting propagation depth and calculation intensity based on the complexity of the provenance chain. For simple linear provenance paths, basic confidence multiplication is used, while for complex branching structures, the system dynamically selects appropriate propagation strategies, improving measurement precision while managing computational complexity.
Solution Approach 2:
The system applies local quality principles by allowing different parts of the provenance graph to have different confidence propagation rules. Critical nodes in the provenance chain receive more intensive confidence analysis, while less critical paths use simplified calculations. This selective approach improves overall measurement precision without uniformly increasing computational complexity across the entire system.
4Ease of operation
If dynamic updates to provenance visualizations are implemented in response to refutations, then risk assessment capability is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing confidence scores and risk metrics in the provenance graph structure. When refutations occur, the system can quickly update visualizations by retrieving pre-computed data and making localized adjustments, rather than recalculating entire provenance chains. This improves risk assessment capability while minimizing the complexity of dynamic updates.
Data Source
AI summary
Provenance analysis systems and methods. Datums representing relationships between entities can be stored in a knowledge store. Datums can be received from agents as agents perform activities. Activity records are be stored in a provenance graph, the activity record and associate received datums with any input datums used in the activity. Provenance subgraphs can 5 be retrieved by traversing the provenance graph for selected datums and presented through a user interface. Provenance subgraphs can be augmented with trust modifiers determined based on attributions, confidences, and refutations provided by a user. Trust modifiers can be propagated downstream to enable the addressing of junctions in variable confidence.