Process mining is a new direction in the field of modeling and analysis of processes, where using information from event logs, describing the history of the system behavior, plays an important role. Methods and approaches used in the process mining are often based on various heuristics, and experiments with large event logs are crucial for the study and comparison of the developed methods and algorithms. Such experiments are very time consuming, so automation of experiments is an important task in the field of process mining. This paper presents the language DPMine developed specifically to describe and carry out experiments on the discovery and analysis of process models. The basic concepts of the DPMine language, as well as principles and mechanisms of its extension are described. Ways of integration of the DPMine language as dynamically loaded components into the VTMine modeling tool are considered. An illustrating example of an experiment to build a fuzzy model of the process discovered from the log data stored in a normalized database is given.
This volume presents new results in the study and optimization of information transmission models in telecommunication networks using different approaches, mainly based on theiries of queueing systems and queueing networks .
The paper provides a number of proposed draft operational guidelines for technology measurement and includes a number of tentative technology definitions to be used for statistical purposes, principles for identification and classification of potentially growing technology areas, suggestions on the survey strategies and indicators. These are the key components of an internationally harmonized framework for collecting and interpreting technology data that would need to be further developed through a broader consultation process. A summary of definitions of technology already available in OECD manuals and the stocktaking results are provided in the Annex section.