Methods of Situation Analysis and Graphical Visualisation of Big Data Streams

Authors: Proletarsky A.V., Berezkin D.V., Gapanyuk Yu.E., Kozlov I.A., Popov A.Yu., Samarev R.S., Terekhov V.I. Published: 16.04.2018
Published in issue: #2(119)/2018  
DOI: 10.18698/0236-3933-2018-2-98-103

Category: Informatics, Computer Engineering and Control | Chapter: System Analysis, Control, and Information Processing  
Keywords: semiotic system, complex graph models, metagraph, event detection, scenario analysis, cognitive computer graphics, dynamic anamorphosis technique

The article presents an approach to developing a data processing system dealing with manipulation and deep analytics of heterogeneous data streams. We analysed existing instruments for stream data processing. In order to support decision making, we extract the necessary information from message streams to monitor and predict evolution of situations. We base our approach on sequential event detection in a text stream, forming situations and constructing scenarios of their subsequent evolution. We suggest using complex graph models, the metagraph model in particular, in order to represent cause-and-effect and hierarchical connections between events and situations. We consider the issues of developing hardware for acceleration of operations dealing with these models. We employ techniques from so-called "cognitive graphics", in particular dynamic anamorphosis, in order to ensure that the decision maker uses the results of this data stream analysis in a highly efficient manner. We propose a semiotic system structure for processing big data streams


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