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Estimation of time parameters of electronic document management

Authors: Michael N. Vasilenko; Vladimir Belozerov; Yury Yefimenko; Michael A. Gordon; Gennady M. Groshev; Peter Bulavsky;

Estimation of time parameters of electronic document management

Abstract

Processes of technical documentation processing, opportunities and advantages of their implementation with the help of automated technologies of electronic document management depend to a large extent on the quality of the technical documentation involved in them and the time for performing elementary operations of document management. In the paper, it is proposed to use dynamic probabilistic loaded graphs to estimate the time parameters of electronic document management of technical documentation. The use of the apparatus of dynamic probabilistic loaded graphs makes it possible to model processes taking into account the probabilistic-temporal characteristics of operations and the impact on them of the quality of technical documentation. To evaluate the quality of technical documents, characteristics are proposed that determine their ability to meet established or perceived needs. The presentation of electronic document management of technical documentation by a dynamic probabilistic technological process allows to more accurately determine the duration of the implementation of elementary operations and transitions at a given hierarchical level of representation taking into account their probabilistic characteristics. The total duration of the process of electronic document management of technical documentation includes a set of elementary operations for this level of representation performed at different stages and loads of arcs of a dynamic probabilistic loaded graph in accordance with the structural scheme of the interaction of processes and the probability of performing conditional transitions at a given time. This approach allows one to more accurately determine the duration of electronic document management of technical documentation.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Top 10%
Average
Average
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