Information relationships and measures: an analysis apparatus for efficient information system synthesis
L. Jóźwiak
Abstract
L. Jóźwiak
Abstract
The analysis of information relationships is of primary importance for the analysis and synthesis of digital information systems. The paper aims to introduce and discuss the fundamental apparatus for the analysis and evaluation of information relationships. It defines and explains various relationships between information, measures for the amount and importance of information, and measures for the strength and importance of the information relationships. It demonstrates importance of the introduced relationships and measures for efficient synthesis, and shows how to apply them in the synthesis process. The analysis apparatus makes operational the famous theory of partitions and set systems of Hartmanis (1966). While partitions and set systems enable one to model information, the relationships and measures enable one to analyze and measure information and information relationships. Both together form a complete information modeling and analysis apparatus that can be applied in logic design, decision system design, pattern recognition, knowledge discovery, machine learning and other areas.
OpenAlex reports 37 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
The analysis of information relationships is of primary importance for the analysis and synthesis of digital information systems. The paper aims to introduce and discuss the fundamental apparatus for the analysis and evaluation of information relationships. It defines and explains various relationships between information, measures for the amount and importance of information, and measures for the strength and importance of the information relationships. It demonstrates importance of the introduced relationships and measures for efficient synthesis, and shows how to apply them in the synthesis process. The analysis apparatus makes operational the famous theory of partitions and set systems of Hartmanis (1966). While partitions and set systems enable one to model information, the relationships and measures enable one to analyze and measure information and information relationships. Both together form a complete information modeling and analysis apparatus that can be applied in logic design, decision system design, pattern recognition, knowledge discovery, machine learning and other areas.
Key concepts: Computer science, Set (abstract data type), Information system, Measure (data warehouse), Data mining, Process (computing), Information retrieval, Data science