Distributed Technical Object Model Synthesis Based on Monitoring Data

Distributed Technical Object Model Synthesis Based on Monitoring Data

Man Tianxing (ITMO University, St. Petersburg, Russian Federation), Vasily Osipov (St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg, Russian Federation), Alexander I. Vodyaho (Saint-Petersburg Electrotechnical University “LETI”, St. Petersburg, Russian Federation), Sergey Lebedev (Saint-Petersburg Electrotechnical University “LETI”, St. Petersburg, Russian Federation) and Nataly Zhukova (St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg, Russian Federation)
Copyright: © 2019 |Pages: 17
DOI: 10.4018/IJKSS.2019070103

Abstract

Practically all human activities depend on technical systems, which consists of a multitude of dynamically distributed objects. In order to control these systems, it is necessary to build and periodically rebuild models of objects, which consist of elements and connections between them and describes the object's state in time and space. Due to a large amount of monitoring data, the problem of automation of object model synthesis arises. By now the most work is done by experts. Analysis of the works from the related areas has shown that methods for the automated synthesis of object models based on link discovering do not exist. An approach for the automated synthesis of object models based on content extracted from messages received from monitoring systems is proposed. A context describing synthesis process conditions is supposed to be considered. The approach is illustrated with an example.
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The term “linked data” was proposed in the area of the Semantic Web (W3C, 2018; Wikipedia, 2018). Linking is a process of extraction, setting and shared usage of links between elements of data, information, and knowledge (linkeddata.org, 2018). In government reports data linking is considered as a process of setting links between elements from different sources. Links are a set based on some common properties of these sources (Data.gov.au, 2018).

There are a lot of definitions of linked data. The main idea of linking is to set relations (or links) between different information elements. These relations define interdependencies between elements and enrich the original data.

Contemporary methods for data linking are mostly based on models and methods of intelligent data analyses and machine learning. They enable investigation of indirect links, restoration of some kind of unobvious interdependencies including those that can be found in large datasets or in a high dimensional data.

The following groups of intelligent data analyses methods can be used for data linking: clustering; classification and forecasting; template matching, etc. (Graesser, 2018; Hastie et al., 2009; Nikolenko & Tulup’ev, 2009; Pospelov, 1990; Russell & Norvig, 2009; Witten & Frank, 2005; Zaki & Meira, 2014).

There are two types of analyses used for dependency discovering: associative and sequential. Associative analysis methods enable discovering logical dependencies between information elements. Sequential analysis methods are used to process sequences of events.

The majority of classical dependency discovering methods is based on brute force search. The complexity of algorithms implementing these methods is decreased with the help of optimization. Classification and evaluation of associative algorithms can be found in (Tushkanova & Gorodeckij, 2015a, 2015b). Some of these algorithms are described in (Zaki & Meira, 2014). In the last few years, a number of new effective associative models have been created, e.g. an associative Bayesian network (Gorodeckij & Samojlov, 2009).

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