18471097. METHOD AND APPARATUS FOR INFORMATION PROCESSING simplified abstract (Fujitsu Limited)
Contents
METHOD AND APPARATUS FOR INFORMATION PROCESSING
Organization Name
Inventor(s)
Yuji Mizobuchi of Kawasaki (JP)
METHOD AND APPARATUS FOR INFORMATION PROCESSING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18471097 titled 'METHOD AND APPARATUS FOR INFORMATION PROCESSING
Simplified Explanation:
The patent application describes a computer system that analyzes cluster data and performance data to classify sample programs into clusters and evaluate their reusability based on execution performance.
Key Features and Innovation:
- Classification of sample programs into clusters based on performance data.
- Calculation of evaluation values for reusability of sample programs within clusters.
- Selection of optimal classification results based on evaluation values at different levels.
Potential Applications: This technology can be applied in various fields such as data analysis, machine learning, and software development to optimize program classification and reusability.
Problems Solved: This technology addresses the challenge of efficiently organizing and evaluating sample programs based on their performance data for better reusability.
Benefits:
- Improved efficiency in classifying and evaluating sample programs.
- Enhanced reusability of programs based on performance data.
- Optimal selection of classification results for better decision-making.
Commercial Applications: Optimized program classification and evaluation can benefit industries such as software development, data analysis, and machine learning by improving efficiency and reusability.
Prior Art: Readers can explore prior research on program classification, cluster analysis, and performance evaluation in the fields of data science and software engineering.
Frequently Updated Research: Stay updated on the latest advancements in program classification, cluster analysis, and performance evaluation to enhance the efficiency and effectiveness of this technology.
Questions about the Technology: 1. How does this technology improve the reusability of sample programs in cluster analysis? 2. What are the potential implications of this technology in the field of machine learning and data analysis?
Original Abstract Submitted
A computer acquires cluster data and performance data. The cluster data represents classification results obtained by classifying multiple sample programs into two or more clusters and arranging the clusters in multiple levels in such a manner that each level contains a different number of clusters. The performance data represents an execution performance of each sample program. The computer calculates, for each of two or more clusters in each level, a first evaluation value based on an index value for reusability of two or more sample programs belonging to the cluster and the execution performances of those sample programs. The computer also calculates, for each level, a second evaluation value based on the first evaluation values corresponding to the two or more clusters of the level. The computer selects, based on the second evaluation values corresponding to the multiple levels, the classification results of a level amongst the multiple levels.
