String processing model for knowledge-driven systems - V. P. Ivashenko (Doklady Belorusskogo gosudarstvennogo universiteta informatiki i radioèlektroniki, 2020)
The purpose of the work is to confirm experimentally theoretical estimates for time complexity of operations of the string processing model linked with the metric space for solving data processing problems in knowledge-driven systems including the research and comparison of the operation characteristics of these operations with the characteristics of similar operations for the most relevant data structures. Integral and unit testing were used to obtain the results of the performed computational experiments and verify their correctness. The C \ C++ implementation of operations of the string processing model was tested. The paper gives definitions of concepts necessary for the calculation of metric features calculated over strings. As a result of the experiments, theoretical estimates of the computational complexity of the implemented operations and the validity of the choice of parameters of the used data structures were confirmed, which ensures near-optimal throughput and operation time indicators of operations. According to the obtained results, the advantage is the ability to guarantee the time complexity of the string processing operations no higher than O  at all stages of a life cycle of data structures used to represent strings, from their creation to destruction, which allows for high throughput in data processing and responsiveness of systems built on the basis of the implemented operations. In case of solving particular string processing problems and using more suitable for these cases data structures such as vector or map the implemented operations have disadvantages meaning they are inferior in terms of the amount of data processed per time unit. The string processing model is focused on the application in knowledge-driven systems at the data management level.