Статья описывает новый метод конструирования семантических расширений запросов о достижениях и неудачах активных систем (организаций, людей). Этот метод базируется на теории К-представлений (концептуальных представлений), предложенной В.А. Фомичевым – новой теории проектирования семантико-синтаксических анализаторов естественно-языковых текстов с широким применением формальных средств представления входных, промежуточных и выходных данных. Метод использует оригинальную формальную модель базы целей – базы знаний, содержащей информацию о целях активных систем. Изложенный подход реализован с помощью языка Веб-программирования Java: разработана и протестирована экспериментальная поисковая система AOS (Аспектно-Ориентированный Поиск).
В статье анализируется памятник, хранящийся в Государственном Эритаже и, возможно, отразивший ситуацию неприятия македонского завоевания Египта его элитой.
The main goal of the present paper is the development of general approach to network analysis of statistical data sets. First a general method of market network construction is proposed on the base of idea of measures of association. It is noted that many existing network models can be obtained as a particular case of this method. Next it is shown that statistical multiple decision theory is an appropriate theoretical basis for market network analysis of statistical data sets. Finally conditional risk for multiple decision statistical procedures is introduced as a natural measure of quality in market network analysis. Some illustrative examples are given.
Operational processes leave trails in the information systems supporting them. Such event data are the starting point for process mining – an emerging scientific discipline relating modeled and observed behavior. The relevance of process mining is increasing as more and more event data become available. The increasing volume of such data (“Big Data”) provides both opportunities and challenges for process mining. In this paper we focus on two particular types of process mining: process discovery (learning a process model from example behavior recorded in an event log) and conformance checking (diagnosing and quantifying discrepancies between observed behavior and modeled behavior). These tasks become challenging when there are hundreds or even thousands of different activities and millions of cases. Typically, process mining algorithms are linear in the number of cases and exponential in the number of different activities. This paper proposes a very general divide-and-conquer approach that decomposes the event log based on a partitioning of activities. Unlike existing approaches, this paper does not assume a particular process representation (e.g., Petri nets or BPMN) and allows for various decomposition strategies (e.g., SESE- or passage-based decomposition). Moreover, the generic divide-and-conquer approach reveals the core requirements for decomposing process discovery and conformance checking problems.
With the process of globalization the number of borrowings from English has rapidly increased in languages all over the world. In systems of automatic speech recognition, spell-checking, tagging and other tasks in the field of natural language processing the loan words frequently cause problems and should be treat separately. In this paper we present a corpora-based approach for the automatic detection of anglicisms in Russian social network texts. Proposed method is based on the idea of simultaneous scripting, phonetics and semantics similarity of the original Latin word and its Cyrillic analogue. We used a set of transliteration, phonetic transcription and morphological analysis methods to find possible hypotheses and distributional semantic models to filter them. Resulting list of borrowings, gathered from approximately 20 million LiveJournal texts shows good intersection with manually collected dictionary. Proposed method is fully automated and can be applied to any domain-specific area.
The research concerns issues on simulation modeling of Russian Federation higher education system considering its interaction with macroeconomic and demographic factors. The proposed range of simulation models of higher education system is developed using agent-based modeling and system dynamics methods. The range of models allows strategic planning of development of Russian Federation higher education system.