Combinatorial Process Evaluation via Parallel Simulation and Statistical Analysis
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Solution Overview
Problem
Traditional approaches to evaluating combinatorial processes, such as logistics, face challenges due to the combinatorial explosion of states, leading to excessively long execution times and the need to discard data, making it difficult to provide timely and relevant answers for decision-makers.
Innovation Solution
A combinatorial process evaluation framework that integrates simulation techniques with multiple parallel statistical analyses, allowing for query-oriented execution within a massively parallel processing environment, generating simulation models, key feature prediction models, and global prediction models to efficiently answer user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional statistical analysis and simulation techniques are used to evaluate combinatorial processes, then comprehensive analysis of process quality and prediction of critical situations is achieved, but execution time becomes excessively long and data volume becomes unmanageable
Solution Approach 1:
The patent divides the evaluation process into two distinct segments: (1) a simulation model that generates synthetic process data, and (2) multiple parallel statistical analyses performed on both real and simulated data. This segmentation allows the system to avoid processing all possible combinatorial states through traditional simulation alone, thereby reducing execution time while maintaining evaluation accuracy through the complementary use of statistical methods on segmented data sets.
Solution Approach 2:
The system performs preliminary action by generating a simulation model beforehand that captures the essential behavior of the combinatorial process. This pre-generated model provides a foundation for subsequent rapid statistical analyses, eliminating the need to perform exhaustive simulations each time evaluation is needed. The simulation model serves as a pre-computed reference that accelerates future evaluations.
2Reliability
If traditional simulation techniques are used to evaluate combinatorial processes, then prediction of critical situations is achieved, but data volume increases dramatically making it difficult to provide timely answers
Solution Approach 1:
The patent applies partial action by performing statistical analyses on a carefully selected subset of data rather than processing all possible combinatorial states. Multiple parallel statistical analyses are conducted on representative samples from both real and simulated data, providing sufficient prediction capability without generating or processing the full volume of data that would result from exhaustive simulation of all possible process states.
Solution Approach 2:
The system creates a simulation model that serves as a synthetic copy of the real combinatorial process. This copied model generates simulated data that mirrors the behavior and characteristics of the actual process, allowing statistical analyses to be performed on the copy rather than requiring exhaustive analysis of the original system's complete state space, thereby reducing data volume while maintaining prediction reliability.
3Loss of information
If comprehensive data collection is performed for combinatorial process evaluation, then complete process understanding is achieved, but traditional approaches fail to provide relevant answers in reasonable time frame
Solution Approach 1:
The evaluation framework segments information processing into two parallel streams: statistical analysis of real collected data and statistical analysis of simulated data generated from a process model. This segmentation allows the system to maintain information completeness by utilizing both data sources simultaneously, while reducing response time by performing multiple parallel statistical analyses rather than sequential processing of all information.
Solution Approach 2:
The system performs preliminary action by pre-generating a simulation model that encapsulates process behavior before evaluation is needed. This pre-computed model allows rapid generation of additional data and performs statistical analyses in advance, enabling the system to provide relevant answers quickly without having to process all collected information from scratch each time evaluation is required.
Data Source
AI summary
Methods and apparatus are provided for evaluating combinatorial processes using simulation techniques and multiple parallel statistical analyses of real-world data. A simulation model is generated that simulates one or more steps of a combinatorial process. The simulation model comprises key features of the combinatorial process. A plurality of first data mining tasks are performed in parallel over real data of the combinatorial process to obtain key feature prediction models that estimate the key features. The key feature prediction models are bound to the simulation model. Query types to be supported are identified and a plurality of simulation runs are generated in parallel, comprising simulated data for the supported query types. A plurality of second data mining tasks are performed in parallel over the plurality of simulation runs to build global prediction models to answer queries of each supported query type. An answer to a user query is determined using the global prediction models.