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Добірка наукової літератури з теми "Informatica (Data processing, Computer science)"

Автор: Grafiati

Опубліковано: 14 березня 2023

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Статті в журналах з теми "Informatica (Data processing, Computer science)"

1

Kryshchuk, Mikola, Juris Lavendels, and Vjaceslavs Sitikovs. "Models of data and their processing for introductory courses of computer science." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 3 (June 16, 2015): 134. http://dx.doi.org/10.17770/etr2015vol3.178.

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<p class="R-AbstractKeywords">Evolution of secondary school course on Informatics during 1990s is discussed. This evolution gave for secondary school graduates rather high level knowledge of applications usage. On the other hand Informatics became purely pragmatic matter within generally academic nature of secondary school curricula. As the result graduates of secondary schools are not prepared enough for mastering of university’s course in Computer Science. Authors are not aware of serious studies and methods of prevention of such negative habits caused by evolution of Informatics course. In this article one method applied in university’s introduction course of Computer Science is considered. The method provides use of algorithmic system closed to the human mind, and to some extent compensates topics removed from the Informatics course in secondary school.</p>

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2

Stacewicz, Paweł. "From Computer Science to the Informational Worldview. Philosophical Interpretations of Some Computer Science Concepts." Foundations of Computing and Decision Sciences 44, no. 1 (March 1, 2019): 27–43. http://dx.doi.org/10.2478/fcds-2019-0003.

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AbstractIn this article I defend the thesis that modern computer science has a significant philosophical potential, which is expressed in a form of worldview, called here informational worldview (IVW). It includes such theses like: a) each being contains a certain informational content (which may be revealed by computer science concepts, such as code or algorithm), b) the mind is an information processing system (which should be modeled by means of data processing systems), c) cognition is a type of computation. These (pre)philosophical theses are accepted in many sciences (e.g. in cognitive science), and this is both an expression and strengthening of the IWV. After a general discussion of the relations between philosophy, particular sciences and the worldview, and then the presentation of the basic assumptions and theses of the IWV, I analyze a certain specification of thesis b) expressed in the statement that “the mind is the Turing machine”. I distinguish three concepts of mind (static, variable and minimal) and explain how each of them is connected with the concept of the Turing machine.

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3

Setiawan, Aditya, Reza Avrizal, and Risma Nurul Auliya. "Implementation of Motorcycle Rental Information System at the Trans Jaya Tangerang." SYSTEMATICS 2, no. 2 (August 1, 2020): 72–78. http://dx.doi.org/10.35706/sys.v2i2.3847.

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The growth in science and technology is accelerating, resulting in a competition that is becoming more intense. Advanced science and technology are obviously also in contact with computers. Computer use can minimize potential errors in data processing compared with manual data processing. The development of these sciences and technologies also encourages companies to improve corporate performance, and one of their uses is the creation of the information systems that companies need. The purpose of this study is to design and build a desktop java-based motorbike rental information system for Koperasi Trans Jaya Tangerang. The methodology used by researchers was grounded research. The methods of data collection used in this study are interviews, observations, and library studies.

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4

Gough, T. G. "Data Processing Methods." Data Processing 27, no. 5 (June 1985): 51. http://dx.doi.org/10.1016/0011-684x(85)90145-5.

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5

Richards, B. "Data processing mathematics." Data Processing 28, no. 3 (April 1986): 162. http://dx.doi.org/10.1016/0011-684x(86)90015-8.

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6

Lepper, AM. "Data Processing Budgets." Data Processing 28, no. 2 (March 1986): 103. http://dx.doi.org/10.1016/0011-684x(86)90114-0.

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7

Voropaieva, A., G. Stupak, and O. Zhabko. "INFORMATION TRANSMISSION ALGORITHMS FOR INFRASTRUCTURE COMPUTER-INTEGRATED DATA PROCESSING SYSTEMS." Naukovyi visnyk Donetskoho natsionalnoho tekhnichnoho universytetu 1(6), no. 2(7) (2021): 14–23. http://dx.doi.org/10.31474/2415-7902-2021-1(6)-2(7)-14-23.

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8

ORLOV, GRIGORY A., ANDREY V. KRASOV, and ARTEM M. GELFAND. "THE USE OF BIG DATA IN THE ANALYSIS OF BIG DATA IN COMPUTER NETWORKS." H&ES Research 12, no. 4 (2020): 76–84. http://dx.doi.org/10.36724/2409-5419-2020-12-4-76-84.

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The concept of Big Data includes the totality of all data sets, the total size of which is several times larger than the capabilities of conventional databases.it is also necessary to note the use of non-classical data processing methods. For example, in the management, analysis of information received, or simply storage. Big Data algorithms have emerged in parallel with the introduction of the first high-performance servers of their kind, such as the mainframe, which have sufficient resources required for operational information processing, as well as corresponding to computer calculations with subsequent analysis. The algorithms are based on performing series-parallel calculations, which significantly increases the speed of performing various tasks. Entrepreneurs and scientists are interested in Big Data, who are concerned with issues related to not only high-quality, but also up-to-date interpretation of data, as well as creating innovative tools for working with them. A huge amount of data is processed in order for the end user to get the results they need for their further effective use. Big Data enables companies to expand the number of their customers, attract new target audiences, and also helps them implement projects that will be in demand not only among current customers, but also attract new ones. Active implementation and subsequent use of Big Data correspond to the solution of these problems. In this paper, we compare the main types of databases and analyze intrusion detection using the example of distributed information system technologies for processing Big Data. Timely detection of intrusions into data processing systems is necessary to take measures to preserve the confidentiality and integrity of data, as well as to correctly correct errors and improve the protection of the data processing system.

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9

Scherr, A. L. "Distributed data processing." IBM Systems Journal 38, no. 2.3 (1999): 354–74. http://dx.doi.org/10.1147/sj.382.0354.

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10

Wang, Xi, Taizheng Chen, Dongwei Li, and Shiqi Yu. "Processing Methods for Digital Image Data Based on the Geographic Information System." Complexity 2021 (June 22, 2021): 1–12. http://dx.doi.org/10.1155/2021/2319314.

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Digital image data processing is mainly to input digital image data into a computer to complete the conversion of a continuous spatially distributed image model into a discrete digital model so that the computer can identify, process, and store the processing process of digital image information. Geographic information system (GIS) is a computer system that integrates multiple forms of information expression, and it integrates functions such as collection, processing, transmission, storage, management, analysis, expression, and query retrieval, which can quickly discover the spatial distribution of things and their attributes and can express the results accurately and vividly in various intuitive forms. Therefore, on the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of processing methods for digital image data, elaborated the development background, current status, and future challenges of the GIS technology, introduced the methods and principles of permutation matrix algorithm and subimage averaging method, constructed the processing model for digital image data based on GIS, analyzed the data structure and its database establishment for digital image, proposed the processing methods for digital image data based on GIS, performed the enhancement processing and calculation classification of digital image data, and finally conducted a case analysis and its result discussion. The study results show that the proposed processing methods for digital image data based on GIS can perform analogue-to-digital conversion of continuous images, complete the steps of sampling, layering, and quantization, and then encode the obtained discrete digital signal into the computer to form an in-plane collection of pixels; this processing method can also organically combine spatial information and image data and identify, process, and store digital image data from both spatial and attribute aspects. The study results of this paper provide a reference for further research on the processing methods for digital image data based on GIS.

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Дисертації з теми "Informatica (Data processing, Computer science)"

1

Lian, Xiang. "Efficient query processing over uncertain data /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20LIAN.

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2

Sungoor, Ala M. H. "Genomic signal processing for enhanced microarray data clustering." Thesis, Kingston University, 2009. http://eprints.kingston.ac.uk/20310/.

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Genomic signal processing is a new area of research that combines genomics with digital signal processing methodologies for enhanced genetic data analysis. Microarray is a well known technology for the evaluation of thousands of gene expression profiles. By considering these profiles as digital signals, the power of DSP methods can be applied to produce robust and unsupervised clustering of microarray samples. This can be achieved by transferring expression profiles into spectral components which are interpreted as a measure of profile similarity. This thesis introduces enhanced signal processing algorithms for robust clustering of micro array gene expression samples. The main aim of the research is to design and validate novel genomic signal processing methodologies for micro array data analysis based on different DSP methods. More specifically, clustering algorithms based on Linear prediction coding, Wavelet decomposition and Fractal dimension methods combined with Vector quantisation algorithm are applied and compared on a set of test microarray datasets. These techniques take as an input microarray gene expression samples and produce predictive coefficients arrays associated to the microarray data that are quantised in discrete levels, and consequently used for sample clustering. A variety of standard micro array datasets are used in this work to validate the robustness of these methods compared to conventional methods. Two well known validation approaches, i.e. Silhouette and Davies Bouldin index methods, are applied to evaluate internally and externally the genomic signal processing clustering results. In conclusion, the results demonstrate that genomic signal processing based methods outperform traditional methods by providing more clustering accuracy. Moreover, the study shows that the local features of the gene expression signals are better clustered using wavelets compared to the other DSP methods.

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3

Engbring, Dieter, and Harald Selke. "Informatik und Gesellschaft als Gebiet der Informatik." Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6497/.

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In diesem Beitrag berichten wir über die Erfahrungen einer umgestalteten Lehre im Bereich Informatik und Gesellschft (IuG). Die Gründe für die Umge staltung und die Konzeption werden skizziert. Die Erfahrungen haben wir zu Thesen verdichtet: 1. Informatik und Gesellschaft sollte eine Pflichtveranstaltung im Bachelor-Studium sein, in der Studierende einen Überblick erhalten, welche gesellschaftlichen Rahmenbedingungen für sie relevant sind und wie man diese in die Praxis mit einbeziehen kann. 2. Historische Inhalte der Informatik sollen hier aufgearbeitet werden, indem man aktuelle Entwicklungen im Kontext ihrer Genese betrachtet.

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4

Müller, Dorothee, Andreas Frommer, and Ludger Humbert. "Informatik im Alltag : Durchblicken statt Rumklicken." Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6495/.

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Die Fachwissenschaft Informatik stellt Mittel bereit, deren Nutzung für Studierende heutzutage selbstverständlich ist. Diese Tatsache darf uns allerdings nicht dar- über hinwegtäuschen, dass Studierende in der Regel keine Grundlage im Sinne einer informatischen Allgemeinbildung gemäÿ der Bildungsstandards der Gesellschaft für Informatik besitzen. Das Schulfach Informatik hat immer noch keinen durchgängigen Platz in den Stundentafeln der allgemein bildenden Schule gefunden. Zukünftigen Lehrkräften ist im Rahmen der bildungswissenschaftlichen Anteile im Studium eine hinreichende Medienkompetenz zu vermitteln. Mit der überragenden Bedeutung der digitalen Medien kann dies nur auf der Grundlage einer ausreichenden informatischen Grundbildung erfolgen. Damit ist es angezeigt, ein Studienangebot bereitzustellen, das allen Studierenden ein Eintauchen in Elemente (Fachgebiete) der Fachwissenschaft Informatik aus der Sicht des Alltags bietet. An diesen Elementen werden exemplarisch verschiedene Aspekte der Fachwissenschaft beleuchtet, um einen Einblick in die Vielgestaltigkeit der Fragen und Lösungsstrategien der Informatik zu erlauben und so die informatische Grundbildung zu befördern.

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5

Holz, Jan, Nadine Berger, and Ulrike Schroeder. "Anwendungsorientierte Gestaltung eines Informatik-Vorkurses als Studienmotivator." Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6487/.

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Zur Unterstützung von Studierenden in der Studieneingangsphase wurde an der RWTH Aachen ein neuartiger und motivierender Einstieg in den Vorkurs Informatik entwickelt und zum Wintersemester 2011/12 erprobt. Dabei wurde die grafische Programmierung mittels App Inventor eingeführt, die zur Umsetzung anwendungsbezogener Projekte genutzt wurde. In diesem Beitrag werden die Motivation für die Neugestaltung, das Konzept und die Evaluation des Testlaufs beschrieben. Diese dienen als Grundlage für eine vollständige Neukonzeption des Vorkurses für das Wintersemester 2012/2013.

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6

Göttel, Timo. "Schnupperveranstaltungen Informatik in der Hochschullandschaft : Angebot vs. Nachfrage?" Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6486/.

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Die vorliegende Arbeit erörtert die Frage, wie Nachwuchs für das Informatikstudium nachhaltig gesichert werden kann. Dazu werden Befragungen unter Schülerinnen und Schülern (1316 Jahre), sowie aktuelle Informatik-Schnupperangebote für Schülerinnen und Schüler an deutschsprachigen Hochschulen vorgestellt und untersucht. Diese Gegenüberstellung zeigt deutlich, dass die Angebote nur bedingt eine breite Zielgruppe ansprechen und dass weitere Formate und Inhalte notwendig sind, um Schülerinnen und Schüler frühzeitig und in voller Breite zu erreichen und für das Informatikstudium zu begeistern. Daraus wird abgeleitet, dass Missverständnisse und Probleme mit der Informatik im Schulkontext aufgegriffen werden müssen. Das vorgestellte Programm Schulbotschafter Informatik stellt einen möglichen Weg dar, um dies zu erreichen und übliche Schnupperangebote zu ergänzen.

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7

Berges, Marc, Andreas Mühling, Peter Hubwieser, and Horst Steuer. "Informatik für Nichtinformatiker : ein kontext- und praxisorientiertes Konzept." Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6496/.

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Анотація:

Wir stellen die Konzeption und erste Ergebnisse einer neuartigen Informatik- Lehrveranstaltung für Studierende der Geodäsie vor. Das Konzept verbindet drei didaktische Ideen: Kontextorientierung, Peer-Tutoring und Praxisbezug (Course). Die Studierenden sollen dabei in zwei Semestern wichtige Grundlagen der Informatik verstehen und anzuwenden lernen. Durch enge Verzahnung der Aufgaben mit einem für Nichtinformatiker relevanten Kontext, sowie einem sehr hohen Anteil von Selbsttätigkeit der Studierenden soll die Motivation für fachfremde Themen gesteigert werden. Die Ergebnisse zeigen, dass die Veranstaltung sehr erfolgreich war.

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8

Elbekai, Ali Sayeh. "Generic model for application driven XML data processing." Thesis, Northumbria University, 2006. http://nrl.northumbria.ac.uk/55/.

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XML technology has emerged during recent years as a popular choice for representing and exchanging semi-structured data on the Web. It integrates seamlessly with web-based applications. If data is stored and represented as XML documents, then it should be possible to query the contents of these documents in order to extract, synthesize and analyze their contents. This thesis for experimental study of Web architecture for data processing is based on semantic mapping of XML Schema. The thesis involves complex methods and tools for specification, algorithmic transformation and online processing of semi-structured data over the Web in XML format with persistent storage into relational databases. The main focus of the research is preserving the structure of original data for data reconciliation during database updates and also to combine different technologies for XML data processing such as storing (SQL), transformation (XSL Processors), presenting (HTML), querying (XQUERY) and transporting (Web services) using a common framework, which is both theoretically and technologically well grounded. The experimental implementation of the discussed architecture requires a Web server (Apache), Java container (Tomcat) and object-relational DBMS (Oracle 9) equipped with Java engine and corresponding libraries for parsing and transformation of XML data (Xerces and Xalan). Furthermore the central idea behind the research is to use a single theoretical model of the data to be processed by the system (XML algebra) controlled by one standard metalanguage specification (XML Schema) for solving a class of problems (generic architecture). The proposed work combines theoretical novelty and technological advancement in the field of Internet computing. This thesis will introduce a generic approach since both our model (XML algebra) and our problem solver (the architecture of the integrated system) are XML Schema- driven. Starting with the XML Schema of the data, we first develop domain-specific XML algebra suitable for data processing of the specific data and then use it for implementing the main offline components of the system for data processing.

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9

Knobelsdorf, Maria, and Christoph Kreitz. "Ein konstruktivistischer Lehransatz für die Einführungsveranstaltung der Theoretischen Informatik." Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6484/.

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Ausgehend von einem sozial-konstruktivistischen Verständnis von Lernprozessen und unter der besonderen Berücksichtigung der durch die Bologna-Studienreform angeregten Kompetenzorientierung, haben wir in den letzten Jahren einen hochschuldidaktischen Ansatz für die Einführungsveranstaltung im Bereich der Theoretischen Informatik an der Universität Potsdam entwickelt und praktisch erprobt. Nach zahlreichen Experimenten und mit einer Durchfallquote von zuletzt 6% im Wintersemester 2011/2012 haben wir den Eindruck, dass der Ansatz den Studierenden jene Lernumgebung und -anregung bietet, die ihnen hilft, die entsprechenden Fachkompetenzen in der Veranstaltung zu entwickeln. In diesem Artikel stellen wir unseren Ansatz vor und skizzieren abschlieÿend, wie wir diesen im nächsten Wintersemester empirisch evaluieren werden.

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10

Apel, Rebecca, Tobias Berg, Nadine Bergner, Mhamed Amine Chatti, Jan Holz, Carmen Leicht-Scholten, and Ulrike Schroeder. "Ein vierstufiges Förderkonzept für die Studieneingangsphase in der Informatik." Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6502/.

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Es wird ein vierstufiges Förderkonzept für die Studieneingangsphase im Fach Informatik beschrieben, das derzeit im Rahmen des Projekts IGaDtools4MINT an der RWTH Aachen auf der Basis einer Literaturanalyse und eines daraus abgeleiteten Indikatorenkatalogs entwickelt wird.

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Книги з теми "Informatica (Data processing, Computer science)"

1

F, McAllister David, ed. Discrete mathematics in computer science. London: Prentice-Hall International, 1986.

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2

Dewdney, A. K. The Turing omnibus: 61 Excursions in Computer Science. Rockville, Maryland, USA: Computer Science Press, 1989.

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3

Informatica e critica dei testi. Roma: Bulzoni, 2001.

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4

Montgomery, A. Y. Introduction to computer science. 2nd ed. Sydney: Prentice Hall, 1986.

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5

Deitel, Harvey M. Computers and data processing. London: Academic Press, 1985.

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6

B, Tucker Allen, and Association for Computing Machinery, eds. Computer science handbook. 2nd ed. Boca Raton: Chapman & Hall/CRC, 2004.

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7

1945-, Lister A., ed. Computer science: A modern introduction. 2nd ed. Englewood Cliffs, N.J: Prentice Hall, 1988.

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8

Marco, Pozzi. Pal@eomatica: Informatica applicata alle scienze paleontologiche : fondamenti e modelli concettuali. Trento: UNI service, 2008.

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9

Marco, Pozzi. Pal@eomatica: Informatica applicata alle scienze paleontologiche : fondamenti e modelli concettuali. Trento: UNI service, 2008.

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10

Bin, Roberto. Informatica per le scienze giuridiche. Padova: CEDAM, 2002.

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Частини книг з теми "Informatica (Data processing, Computer science)"

1

Chen, Yi, Zhi Qiao, Spencer Davis, Hai Jiang, and Kuan-Ching Li. "Pipelined Multi-GPU MapReduce for Big-Data Processing." In Computer and Information Science, 231–46. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00804-2_17.

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2

Luong, Johannes, Dirk Habich, Thomas Kissinger, and Wolfgang Lehner. "Towards Efficient Multi-domain Data Processing." In Communications in Computer and Information Science, 47–64. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62911-7_3.

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3

Simon, Marion, and Hartmut Asche. "Automated Spatial Data Processing and Refining." In Communications in Computer and Information Science, 38–49. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-51641-7_3.

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4

Mann, Zoltán Ádám, Eliot Salant, Mike Surridge, Dhouha Ayed, John Boyle, Maritta Heisel, Andreas Metzger, and Paul Mundt. "Secure Data Processing in the Cloud." In Communications in Computer and Information Science, 149–53. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-79090-9_10.

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5

Barabanov, Igor, Elizaveta Barabanova, Natalia Maltseva, and Irina Kvyatkovskaya. "Data Processing Algorithm for Parallel Computing." In Communications in Computer and Information Science, 61–69. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11854-3_6.

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6

Matsuda, Yoshitatsu, Takayuki Sekiya, and Kazunori Yamaguchi. "Discovery of Interconnection Among Knowledge Areas of Standard Computer Science Curricula by a Data Science Approach." In Neural Information Processing, 186–95. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70139-4_19.

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7

Pan, Zhang, Lai Fenggang, Du Jing, Ying Zhangchi, Kong Rui, Zhou Yi, and Yu Xiao. "An Elastic Data Processing Method Based on Data-Center-Platform." In Communications in Computer and Information Science, 62–71. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9031-3_6.

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8

Spyratos, Nicolas, and Tsuyoshi Sugibuchi. "Parallelism and Rewriting for Big Data Processing." In Communications in Computer and Information Science, 11–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40140-4_2.

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Hrkút, Patrik, Štefan Toth, Michal Ďuračík, Matej Meško, Emil Kršák, and Miroslava Mikušová. "Data Collection for Natural Language Processing Systems." In Communications in Computer and Information Science, 60–70. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3380-8_6.

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Shornikova, Tatiana A., and Yuliya S. Gusynina. "Tone Image Processing with Discrete Data Structure." In Communications in Computer and Information Science, 117–27. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21340-3_11.

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Тези доповідей конференцій з теми "Informatica (Data processing, Computer science)"

1

Wang, Liang. "Research on Data Visualization Information Processing Based on Computer Big Data." In 2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS). IEEE, 2021. http://dx.doi.org/10.1109/tocs53301.2021.9688643.

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Zheng, Zhiming. "Research on Computer Information Processing Technology Based on Big Data." In 2021 International Conference on Big Data Analysis and Computer Science (BDACS). IEEE, 2021. http://dx.doi.org/10.1109/bdacs53596.2021.00037.

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3

Kowalska, Aleksandra, Piotr Łuczak, Dawid Sielski, Tomasz Kowalski, Andrzej Romanowski, and Dominik Sankowski. "Towards Big Data Solutions for Industrial Tomography Data Processing." In 2019 Federated Conference on Computer Science and Information Systems. IEEE, 2019. http://dx.doi.org/10.15439/2019f310.

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4

"Research on Computer Information Processing Technology in Mass Data Era." In 2018 5th International Conference on Electrical & Electronics Engineering and Computer Science. Francis Academic Press, 2018. http://dx.doi.org/10.25236/iceeecs.2018.090.

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5

Du, Quanye, Hui Cao, Haitao Ma, and Han Hu. "Fast Processing of Airborne LiDAR Data and Imagery." In 2009 International Conference on Information Engineering and Computer Science. IEEE, 2009. http://dx.doi.org/10.1109/iciecs.2009.5363706.

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Jiang, Yixiang, Qingsong Zhang, Chengting Zhang, and Xuefeng Gao. "Analysis of Computer Information Processing Technology Under the Background of Big Data." In 2018 International Symposium on Communication Engineering & Computer Science (CECS 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/cecs-18.2018.15.

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Zhang, Shaohong, Jiqiao Li, Hai Huang, Hongqiu Wu, Haodong Lin, and Wenxiao Qiu. "Ensemble-based factor analysis of survey data on computer science courses." In 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2016. http://dx.doi.org/10.1109/cisp-bmei.2016.7853032.

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Jiong Cong Chen, Nan Hua Yu, Hui Chen, and Wen Jie Zheng. "HAPS: Hierarchical abnormal event processing over data streams." In 2012 International Conference on Computer Science and Information Processing (CSIP). IEEE, 2012. http://dx.doi.org/10.1109/csip.2012.6308932.

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Cheng, Yu, and Tao Zhang. "A Probability Based Approach for Processing Dimension Missing Data." In 2009 International Conference on Information Engineering and Computer Science. IEEE, 2009. http://dx.doi.org/10.1109/iciecs.2009.5364328.

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Lee, Chagmin, Changhyun Byun, and Hyunjeong Shin. "A Resource-Efficient Algorithm for Processing Various Sensor Data in Internet of Things Applications." In Information Technology and Computer Science 2016. Science & Engineering Research Support soCiety, 2016. http://dx.doi.org/10.14257/astl.2016.136.24.

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Звіти організацій з теми "Informatica (Data processing, Computer science)"

1

Modlo, Yevhenii O., Serhiy O. Semerikov, Pavlo P. Nechypurenko, Stanislav L. Bondarevskyi, Olena M. Bondarevska, and Stanislav T. Tolmachev. The use of mobile Internet devices in the formation of ICT component of bachelors in electromechanics competency in modeling of technical objects. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3264.

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Анотація:

Computer simulation of technical objects and processes is one of the components of the system of professional training of a modern electromechanics engineer. It has been established that despite the fact that mobile Internet devices (MID) are actively used by electrical engineers, the methods of using them in the process of bachelor in electromechanics training is considered only in some domestic scientific studies. The article highlights the components of the methods of using MID in the formation of the ICT component of the competence of the bachelor in electromechanics in modeling of technical objects, providing for students to acquire basic knowledge in the field of Computer Science and modern ICT and skills to use programming systems, math packages, subroutine libraries, and the like. For processing tabular data, it is proposed to use various freely distributed tools that do not significantly differ in functionality, such as Google Sheets, Microsoft Excel, for processing text data – QuickEdit Text Editor, Google Docs, Microsoft Word. For 3D-modeling and viewing the design and technological documentation, the proposed comprehensive use of Autodesk tools in the training process.

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