Real-time analysis of parameters of multiple object detection systems

Protsenko, V. I.; Kazanskiy, Nikolay L.; Serafimovich, P. G.

doi:10.18287/0134-2452-2015-39-4-582-591

Cited by 22 publications

(13 citation statements)

References 24 publications

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“…Changes have also taken place in the journal contentnew publication categories have appeared: Earth's Remote Sensing Technology, including development of hyperspectral equipment [6][7][8][9]; photonic-crystal sensors [10][11]; LED technology [12][13]; video signal stream processing and some other new methods of image subject processing [14][15][16][17][18]; new types of laser beams [19][20]; and academic reviews of up-to-date trends have been published [21][22]. In 2016, for the first time in a long period, a full-text English version of the journal of Computer Optics was issued with the articles originally written in English (Volume 40, Issue 5), which had not previously been published elsewhere.…”

Section: Results Of Two Years Developmentmentioning

confidence: 99%

Current problems of development of the journal of Computer Optics

Kudryashov¹

2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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This paper presents overall results of two years experience in executing tasks defined in article "Quo Vadis" written by the Editor-in-Chief of the journal of Computer Optics, Corresponding Member of RAS V.A. Soifer (Computer Optics 2014; Volume 38, Issue 4). The main bibliometric indicators of the journal in SCOPUS citation database are compared with similar metrics of some most relevant periodicals. The paper declares some important events to be held in 2017. Based on the current progress of the journal, new objectives of its further development are defined, and major events targeted for 2017 are discussed.

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Section: Results Of Two Years Developmentmentioning

confidence: 99%

Current problems of development of the journal of Computer Optics

Kudryashov¹

2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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“…Short-term and long-term predictions play important role in finance [22][23] and automatic control [24][25][26]. Elaborate design of IBM InfoSphere Streams system also allows to transform multimedia data [27,28] which result can be further piped into time series analysis operators.…”

Section: Resultsmentioning

confidence: 99%

Software and Hardware Infrastructure for Data Stream Processing

Protsenko

Seraphimovich²,

Popov

et al. 2016

Collection of Selected Papers of the II International Conference on Information Technology and Nanotechnology

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Abstract. In this paper state-of-the-art hardware and software technologies for stream data processing are reviewed. IBM InfoSphere Streams and Apache Spark are among of the most popular software products that alleviates burden of distributed program development for data analysis tasks. Capabilities of these systems are considered in application to the time series analysis. IBM InfoSphere Streams turns to be more suitable for online processing, whereas Apache Spark time series library focuses on a bulk processing of big collections of the time series.Keywords: data stream processing, high performance computing, time series analysis.Citation: Protsenko VI, Seraphimovich PG, Popov SB, Kazanskiy NL. Firmware and hardware infrastructure for data stream processing. CEUR Workshop Proceedings, 2016; 1638 : 782-787. DOI: 10.18287/1613 -0073-2016 -1638 1 IntroductionIn the recent years business and scientific organizations faced the problem of development of analytic pipelines that could process large amounts of data in real-time and be able to seamlessly incorporate new data sources and new queries. Previous approach, that suggest to use conventional data bases, was not suited for real-time data analysis because of the need to store data before the processing. Priority in ACID principle in databases also constrains its ability to scale well on clusters of tenths to thousands of nodes. As a consequence, it is hard or impossible to process massive amounts of data in a fixed time. New approaches were proposed: Hadoop framework for bulk processing and dataflow graph based stream processing systems. Still a lot of research results in the data base field is now used in data stream processing systems: random sampling [1], aggregations [2,3], join techniques [4,5], query plan optimizers [6] and schedulers [7,8,9].

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“…Unlike the classification for clustering, the list of groups is not clearly defined and is determined during the operation of the algorithm. The main goal of clustering is the search for existing structures [2][3][4][5][6].…”

Section: Text Data Clusteringmentioning

confidence: 99%

Development and research of algorithms for clustering data of super-large volume

Rytsarev¹,

Blagov²,

Khotilin³

2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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The work is devoted to the research of text data clustering algorithms. As the object of research, the social network Twitter was selected. At the same time, text data was collected, processed and analyzed. To solve the problem of obtaining the necessary information, studies in the field of optimizing the data collection of the social network Twitter were carried out. A software tool that provides the collection of necessary data from specified geolocation has been developed. The existing algorithms for clustering data, mainly of large volume were explored.

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Real-time analysis of parameters of multiple object detection systems

Cited by 22 publications

References 24 publications

Current problems of development of the journal of Computer Optics

Current problems of development of the journal of Computer Optics

Software and Hardware Infrastructure for Data Stream Processing

Development and research of algorithms for clustering data of super-large volume

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