Hierarchical Simulation Data Summarization for Query Efficiency
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Solution Overview
Problem
Software applications for simulating complex systems generate large amounts of data, leading to computational constraints and making it difficult to extract meaningful information in a timely manner, resulting in impractical query response times.
Innovation Solution
A method for summarizing and querying data-intensive simulation results by generating first, second, and third level summaries during the simulation process, which include frequency distributions, causality relationships, and probability distributions, allowing for efficient storage and rapid query response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If thorough simulations of complex systems are performed considering multiple parameters and scenarios, then the completeness and accuracy of simulation results are improved, but the memory and storage requirements increase significantly
Solution Approach 1:
The patent extracts only the essential and meaningful information from massive simulation data through multiple levels of summarization. First-level summaries capture key events and attributes, second-level summaries aggregate scenarios with similar outcomes, and third-level summaries provide executive overviews. This extraction process retains critical simulation insights while dramatically reducing storage requirements.
Solution Approach 2:
The patent segments simulation data into hierarchical levels of summarization. Each level processes and organizes data differently: level 1 captures detailed events, level 2 groups by scenario similarities, and level 3 provides high-level aggregates. This segmentation allows the system to maintain comprehensive simulation results while managing storage through structured organization.
2Loss of information
If large amounts of simulation data are generated and stored for multiple scenarios, then the depth of analysis and query capability are improved, but the time required to extract meaningful information increases
Solution Approach 1:
The patent performs summarization operations in advance during simulation execution. First-level summaries are generated as simulations run, second-level summaries are pre-computed by grouping similar scenarios, and third-level summaries are prepared beforehand. When queries are executed, the system retrieves pre-processed summaries rather than analyzing raw data, dramatically reducing query response time while maintaining information quality.
3Adaptability or versatility
If simulations are executed multiple times for different scenarios and parameter combinations, then the comprehensiveness of system analysis is improved, but the computational resources and processing time increase
Solution Approach 1:
The patent merges scenarios with similar outcomes at the second level of summarization by grouping them based on key event sequences and attribute patterns. This consolidation allows the system to handle multiple scenario variations efficiently, reducing redundant processing while maintaining comprehensive scenario coverage. The merging process identifies and combines equivalent simulation results, improving computational efficiency.
4Measurement precision
If detailed simulation data is maintained for all scenarios to enable comprehensive querying, then the accuracy of query results is improved, but the storage requirements and data management complexity increase
Solution Approach 1:
The patent transforms flat simulation data into a hierarchical multi-dimensional structure. Data is organized across three summarization levels, with each level adding a dimension of abstraction. This dimensional transformation maintains query accuracy by preserving essential information at appropriate levels while simplifying data management through the hierarchical organization, reducing overall system complexity.
Data Source
AI summary
Simulation data is summarized and queried. A user provides an indication of simulation data that will be subsequently queried. The queried simulation data comprises (i) a set of key attributes, (ii) a set of events, and/or (iii) a set of causality relationships between a plurality of the events. First level summaries summarize simulation executions of scenarios of a combinatorial process and comprise (i) a summary of the frequency distribution of key attribute values, (ii) a timestamp for each event, and (iii) an indication of causality between events observed during the simulation. Second level summaries summarize executions of the given scenario and comprise (i) a consolidated distribution probability for the key attributes, (ii) a frequency distribution of occurrences of the events in a single execution, and (iii) a frequency of observations of the causality between pairs of events. In response to a query, second level summaries of selected scenarios are accessed to retrieve information related to the elements expressed in the query and to produce a third level summary that aggregates information accessed from the second level summaries of the selected scenarios.