Journal articles: 'Mathematics – Data Processing'

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Jaworski, John, and Elizabeth Bliss. "Data Processing Mathematics." Mathematical Gazette 71, no. 458 (December 1987): 334. http://dx.doi.org/10.2307/3617092.

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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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Ivanek, J. "Mathematics for data processing and computing." European Journal of Operational Research 23, no. 3 (March 1986): 414–15. http://dx.doi.org/10.1016/0377-2217(86)90314-0.

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Ashcroft, W. A. "Mathematics for Seismic Data Processing and Interpretation." Physics of the Earth and Planetary Interiors 58, no. 2-3 (December 1989): 269–70. http://dx.doi.org/10.1016/0031-9201(89)90063-0.

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Kumkov, Sergey I., Vyacheslav S. Nikitin, Tatyana N. Ostanina, and Valentin M. Rudoy. "Interval processing of electrochemical data." Journal of Computational and Applied Mathematics 380 (December 2020): 112961. http://dx.doi.org/10.1016/j.cam.2020.112961.

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Hartati, Tuti, Nida Fitria, Maulana Al Aziz Harahap, and Dadan Dasari. "Data-Driven Education: Data Processing as a Key to Improving the Quality of Mathematics Education." ALSYSTECH Journal of Education Technology 2, no. 1 (December 20, 2023): 45–57. http://dx.doi.org/10.58578/alsystech.v2i1.2361.

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Education is a major pillar in the development of a nation, and the quality of education becomes a key factor in determining the progress of a society. In this digital era, there is a rapid development in the use of information technology, and this has a significant impact on the education sector. The purpose of this study is for data-driven education: data processing as the key to improving the quality of mathematics education. This research uses a mixed method that combines qualitative and quantitative methods. This approach was chosen to address the complexity of data management and the comprehensiveness of this topic. The research begins with an in-depth literature analysis to understand the potential of Data-Driven Education. By using data as a guide, educators can make smarter decisions, detail instructional strategies, and create an educational environment that allows each student to reach his or her full potential. This is done by combining the results of various data of educator learners who can make informational decisions to improve the quality of mathematics learning. The application of data management in the context of mathematics education can provide concrete solutions to overcome challenges and improve the quality of learning. The right use of data provides the basis for smarter decisions and more adaptive learning, creating a learning environment that fits the needs of each student. By integrating Data-Driven Education effectively, we can move towards a more inclusive, personalized and successful mathematics education. The use of data contributes significantly in improving the quality and inclusivity of mathematics education. The integration of data management in mathematics education is a critical step towards a more adaptive, inclusive, and effective education

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Oursatyev, Oleksii A. "Data Research in Industrial Data Mining Projects in the Big Data Generation Era." Control Systems and Computers, no. 3 (303) (2023): 33–53. http://dx.doi.org/10.15407/csc.2023.03.033.

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Introduction. The review material is based mainly on business intelligence (BI) solutions designed for tasks with corporate data. But all the main aspects of working with data discussed in the work are also used on data processing platforms (Data Science Platform). Many BI vendors have expanded the capabilities of their systems to perform more advanced analytics, including Data Science. They added the phrase “Data Science” to their marketing research, and the term “advanced analytics” lost some popularity in relation to corporate data. The Data Science Platform provides a comprehensive set of tools for use by advanced users who traditionally work with data. Capabilities that allow you to connect to multi-structured data across different types of storage platforms, both on-premises and in the cloud, and the infrastructure architecture of a modern BI analytics platform enable high-performance workloads, including business intelligence. It uses distributed architecture, massively parallel processing, data virtualization, in-memory computing, etc. The combination of traditional relational data processing with calculations on the well-known Apache Hadoop software infrastructure, which integrates a number of components of the Hadoop ecosystem (Apache Hive, HBase, Spark, Solr, etc.) with the necessary target functions, allows you to create a fully functional platform for storing and processing structured and non-structures data. Purpose. A review of data processing problems and an analysis of the use of world-class mathematical apparatus and tools for obtaining knowledge from information were carried out. Methods. The paper describes the use of Data Mining methods in big data processing tasks, as well as methods of business, recommendation and predictive analytics. Result. The study suggests that machine learning-enhanced master data management (MDM), data quality, data preparation, and data catalogs will converge into a single, modern Enterprise Information Management (EIM) platform applicable to most new analytics projects. The results of the analysis of the process of identifying useful data can be useful to researchers and developers of modern platforms for processing and researching data in various spheres of society. Conclusion. A review of data processing problems and an analysis of the use of world-class mathematical apparatus and tools for obtaining knowledge from information were carried out. It is shown that a high-quality solution to the problems of working with first-level data indicated in this review will be provided by data research in modern analytical platforms. Successful penetration into their essence at the level of obtaining knowledge using machine learning and artificial intelligence algorithms will make it possible to predict future results in managed objects (processes) and make informed decisions.

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Moshchenko, I., O. Nikitenko, Yu Kozlov, and Yu Zharko. "Feature of statistical data processing by computer mathematics systems tools." Radiotekhnika, no. 206 (September 24, 2021): 131–36. http://dx.doi.org/10.30837/rt.2021.3.206.12.

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Processes of error accumulation were analysed during arithmetic operations on statistical data obtained in the course of research on oscillations in cross-field electron vacuum devices. The features of statistical data processing obtained as a result of experimental research were investigated using the most widespread computer mathematical packages. The features of statistical data processing were investigated by processing a sample of 80 values of the magnetron generation frequency using popular mathematical packages Excel, Maple, Matlab and MathCad and comparing the results obtained with calculations using theoretical formulas. Calculation results for all packages give the same results for mean, variance and standard deviation. As for the coefficients of skewness and kurtosis, most of the results do not coincide. Analysis of the calculation results showed that the difference in the obtained values of the skewness and kurtosis is due to different definitions of these indicators in mathematical packages Excel, Maple, Matlab and MathCad. It is proved that in Microsoft Excel we cannot correctly construct a histogram without using additional operations, because the interval limits are calculated with errors. It leads to an incorrect determination of the number of elements into these intervals. To build correctly a histogram using the Excel package, it is necessary to calculate the interval limits in advance. It is concluded that before using computer mathematical packages for processing statistical data, it is necessary to analyze first by what formulas the required parameters are calculated and take appropriate measures to eliminate possible discrepancies with the parameters calculated using theoretical formulas.

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Unaenah, Een, Gabriella Sinthia Marctines, and Nanda Nahzifa. "Pembelajaran Pengolahan Data dengan Metode Permainan Pos Berantai pada Siswa Kelas 4 Sekolah Dasar." MASALIQ 3, no. 6 (August 1, 2023): 1031–39. http://dx.doi.org/10.58578/masaliq.v3i6.1576.

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Data processing is an important aspect of learning mathematics at the elementary school level. Next will understand mathematics in data processing. Data processing is the process of collecting, compiling, analyzing, and presenting data in graphical or tabular form so that it is easy to understand. There are two types of data that are often used in data processing, namely qualitative data and quantitative data. Qualitative data are those that are measured based on research as they are and cannot be measured by numbers, such as color, smell, and taste. Meanwhile, quantitative data is data that can be measured based on numbers, such as numbers or sizes. There is a method that can be used to better understand interactive data processing, namely the serial post game method. With this method in order to be able to explore the use of the postal game method in teaching data processing to grade 4 students in elementary schools so that it is more fun and does not make students easily bored. There is also the purpose of this study is to know how to improve learning outcomes in mathematics lessons regarding data processing using the serial post method for class IV Elementary School and to find out the responses to mathematics lessons on data processing materials that link students' activeness.

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Gavrilović, Branko. "Innovative approach to teaching mathematics using data analytics." Zbornik radova Pedagoskog fakulteta Uzice, no. 25 (2023): 217–36. http://dx.doi.org/10.5937/zrpfu2325191g.

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With the development of information technology, the Internet, and social networks, the amount of collected data is growing year by year at a high rate. Data processing and analysis becomes a necessity without which quality decisions cannot be made. Education, as an important social segment, must follow global changes and provide an adequate response to new social needs. International tests have shown that students from Serbia have problems with processing and analyzing data as well as applying mathematical knowledge in modeling real-life situations. In this paper, an innovative approach to teaching mathematics based on data analytics will be presented. The goal of the paper is to show the possibility of using data analytics techniques in the teaching process. A teaching model using data analytics will be presented, as well as a complete preparation for the realization of a mathematics class. By building a mathematical model using data analytics, students will gain new knowledge in the field of financial mathematics. The possibility of using data analytics in the implementation of other teaching units in mathematics, as well as in the teaching of other subjects, will be presented.

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Tagiev, F. K., M. A. Fradkina, N. G. Chernoguz, and I. L. Shain. "Adaptive noise-immune data-processing system." Measurement Techniques 30, no. 9 (September 1987): 833–36. http://dx.doi.org/10.1007/bf00865883.

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Zhang, Yujian, and Hechen Zhang. "Application of Random Walks in Data Processing." Highlights in Science, Engineering and Technology 31 (February 10, 2023): 263–67. http://dx.doi.org/10.54097/hset.v31i.5152.

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A random walk is known as a process that a random walker makes consecutive steps in space at equal intervals of time and the length and direction of each step is determined independently. It is an example of Markov processes, meaning that future movements of the random walker are independent of the past. The applications of random walks are quite popular in the field of mathematics, probability and computer science. Random walk related models can be used in different areas such as prediction, recommendation algorithm to recent supervised learning and networks. It is noticeable that there are few reviews about randoms for the beginners and how random walks are used nowadays in distinctive areas. Hence, the aim of the article is to provide a brief review of classical random walks, including basic concepts and models of the algorithm and then some applications in the field of computer science for the beginners to understand the significance and future of random walks.

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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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Suprayogo, Rofiq, Sutrisno Sutrisno, and Supandi Supandi. "Eksperimentasi Pendekatan RME terhadap Prestasi Belajar Matematika Ditinjau dari Motivasi Belajar Siswa." Media Penelitian Pendidikan : Jurnal Penelitian dalam Bidang Pendidikan dan Pengajaran 13, no. 2 (December 20, 2019): 189. http://dx.doi.org/10.26877/mpp.v13i2.5103.

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The purpose of this research is to determine the effect of Realistic Mathematic Education on mathematics learning achivement in terms of student motivation. The research design used in this research is Quasi Experimental Design with factorial 2 × 3. Sampling by Cluster Random Sampling technique. The population in this study is all students of class IX in MTS Futuhiyyah 1 Mranggen. The data in this study is obtained from the posttest result of mathematics learing achivement and questionnaire of student motivation. Data processing is done by using anova calculation of two different cell paths followed by scheffe test '. The conclusions of the results of data processing are (1) Student achievement in kesjangunanan material and kekongruenan using learning approach Realistic Mathematic Education provides better learning achievement compared with conventional approach, (2) highly motivated students have better learning achievement than low motivated students, and students with moderate motivation have better learning outcomes than students with low motivation, and (3) there is no interaction between learning models with student motivation on student achievement.

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KRASNOZHON, O. B., and V. V. MATSIUK. "КОМП’ЮТЕРНО-ОРІЄНТОВАНІ ЕЛЕМЕНТИ НАВЧАННЯ МАТЕМАТИЧНИХ ДИСЦИПЛІН МАЙБУТНІХ УЧИТЕЛІВ МАТЕМАТИКИ." Scientific papers of Berdiansk State Pedagogical University Series Pedagogical sciences 1, no. 2 (October 4, 2021): 255–62. http://dx.doi.org/10.31494/2412-9208-2021-1-2-255-262.

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The article is devoted to the issues of constructing effective computer-oriented components of the methodological system of teaching the disciplines «Linear Algebra» and «Probability Theory with Elements of Mathematical Statistics» provided for in the educational and professional program «Secondary Education (Mathematics)» of the first level of higher education in the specialty 014 Secondary Education (Mathematics). The article analyzes the methodological aspects of the effective organization of computations when finding the angle between a given vector and a nonzero subspace of Euclidean space, as well as using the least squares method for processing experimental data. The theoretical and practical information known to students-mathematicians from the corresponding sections of these academic disciplines is briefly presented. Analyzed educational, methodological and scientific literature used in teaching linear algebra and probability theory with elements of mathematical statistics; the expediency of using computer-oriented elements of teaching mathematical disciplines of future mathematics teachers has been substantiated. The authors proposed the use of computer-oriented learning elements in the processing of the content of disciplines and the development of test tasks of different levels of complexity in linear algebra and probability theory with elements of mathematical statistics in order to objectively assess the level of students' knowledge and timely correct individual educational trajectories. The article provides examples of the application of computer-oriented elements of teaching linear algebra and probability theory with elements of mathematical statistics, and also analyzes the methodological features of the organization of calculations in the software mathematical environment Mathcad. The methodological and practical materials presented in the article can be useful for students to organize and activate independent scientific and pedagogical activities, teachers of secondary educational institutions, heads of optional and circle work of students, teachers of linear algebra and probability theory courses with elements of mathematical statistics of pedagogical higher educational institutions. Key words: methods of teaching mathematics, computer-oriented elements of teaching mathematics, linear algebra, probability theory, mathematical statistics, Euclidean space, non-zero subspace of Euclidean space, least squares method.

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Ames, W. F. "Underwater acoustic data processing." Mathematics and Computers in Simulation 31, no. 6 (February 1990): 594. http://dx.doi.org/10.1016/0378-4754(90)90066-r.

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A. SULTAN, Nagham, and Dhuha B. ABDULLAH. "A COMPREHENSIVE STUDY ON BIG DATA FRAMEWORKS." MINAR International Journal of Applied Sciences and Technology 05, no. 01 (March 1, 2023): 34–48. http://dx.doi.org/10.47832/2717-8234.14.4.

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With the advent of cloud computing technology, the generation of data from various sources has increased during the last few years. The current data processing technology must handle the enormous volumes of newly created data. Therefore, the studies in the literature have concentrated on big data, which has enormous volumes of almost unstructured data. Dealing with such data needs well-designed frameworks that fulfil developers’ needs and fit colourful purposes. Moreover, these frameworks can use for storing, processing, structuring, and analyzing data. The main problem facing cloud computing developers is selecting the most suitable framework for their applications. The literature includes many works on these frameworks. However, there is still a severe gap in providing comprehensive studies on this crucial area of research. Hence, this article presents a novel comprehensive comparison among the most popular frameworks for big data, such as Apache Hadoop, Apache Spark, Apache Flink, Apache Storm, and MongoDB. In addition, the main characteristics of each framework in terms of advantages and drawbacks are also deeply investigated in this article. Our research provides a comprehensive analysis of various metrics related to data processing, including data flow, computational model, overall performance, fault tolerance, scalability, interval processing, language support, latency, and processing speed. To our knowledge, no previous research has conducted a detailed study of all these characteristics simultaneously. Therefore, our study contributes significantly to the understanding of the factors that impact data processing and provides valuable insights for practitioners and researchers in the field

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Aksenov, E. A., A. I. Lopatin, and O. N. Khazov. "CAMAC modules for processing metrological-system data." Measurement Techniques 30, no. 11 (November 1987): 1055–56. http://dx.doi.org/10.1007/bf00865053.

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Bas, S. V., and K. I. Slovak. "Ways of processing requests and characteristics of mobile access to Wolfram|Alpha." CTE Workshop Proceedings 2 (March 20, 2014): 309–16. http://dx.doi.org/10.55056/cte.226.

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This paper examines the ways of different types inquiries to Wolfram|Alpha, in particular, the possibility of natural language queries. The characteristic of mobile access to Wolfram|Alpha are highlighted. The data representation format was considered to visual representations, images, HTML, Mathematica Cell, text view, plain text, MathML, Mathematica input, Mathematica output, audio presentation, special types of Wolfram|Alpha output.The paper aim is the analysis and synthesis of capabilities for requests to Wolfram|Alpha in many ways and opportunities to provide characteristics of mobile access to Wolfram|Alpha.The subject of the study is Wolfram|Alpha as cloud-based service of mathematics learning.In preparing the paper material used case studies and experimental research presentation capabilities of different types of requests to Wolfram|Alpha.Conclusion: Wolfram|Alpha work is based on natural language processing (currently only English), a large library of algorithms and NKS-approach to answering queries. Wolfram|Alpha does not issue a list of links based on query results, and calculates the answer based on their own knowledge base. Service is able to convert data between different measurement units, counting systems, etc.

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Hyun, Seung Ho, Bogun Jin, and Seung Jae Lee. "A novel Bad Data Processing algorithm for analog data in substation automation systems." Applied Mathematics and Computation 205, no. 2 (November 2008): 824–31. http://dx.doi.org/10.1016/j.amc.2008.05.137.

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Tompkins, Diane L., Linda B. Bourque, and Virginia A. Clark. "Processing Data: The Survey Example." American Statistician 47, no. 3 (August 1993): 235. http://dx.doi.org/10.2307/2684986.

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Tang, Yuzhe, and Bugra Gedik. "Autopipelining for Data Stream Processing." IEEE Transactions on Parallel and Distributed Systems 24, no. 12 (December 2013): 2344–54. http://dx.doi.org/10.1109/tpds.2012.333.

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Babbysh, N. A. "Software Platform for Reading, Processing and Analyzing EEG Data." Programmnaya Ingeneria 14, no. 5 (May 23, 2023): 254–60. http://dx.doi.org/10.17587/prin.14.254-260.

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Electroencephalogram (EEG) data can be used in many different areas. For example, for diagnosing brain diseases, in brain computer interfaces, for conducting various studies, and much more. To apply EEG data, a large set of different algorithms for preprocessing and analyzing these data is needed. This paper describes a software platform containing a set of tools for automated processing of EEG signals and their analysis, including machine learning methods. The platform has a flexible architecture and consists of modules, which allows it to be used for various purposes. Data can be obtained both from files and directly from the electroencephalograph device in real time. The graphical interface provides a convenient way to configure the modules of the software. The software interface of client applications (API) makes it possible to use this platform to create prototypes of devices that use EEG data for their work.

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Kondo, Kentaro, Yasufumi Nojiri, Osamu Hasegawa, and Naoaki Shinohara. "Integrated aircraft noise data processing at Narita International Airport." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 268, no. 8 (November 30, 2023): 341–46. http://dx.doi.org/10.3397/in_2023_0062.

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In the area around Narita International Airport, the relevant local governments and Narita International Airport Corporation (NAA) have set up aircraft noise monitoring stations to continuously monitor and measure aircraft noise in order to confirm compliance with the Environmental Quality Standards of Aircraft Noise, to respond to complaints from residents, and to study noise control measures. The measurement data are aggregated and processed by Narita Airport Regional Symbiosis Promotion Foundation (NSF) from a centralized and neutral standpoint, and the results are released to the public after deliberation and verification by a third-party organization established at NSF. This paper introduces NSF's unprecedented efforts to centrally aggregate and process measurement data from monitoring stations installed around the airport. Keywords: Aircraft Noise, Data Processing

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Muckromin, Ali, Rini Budiwati, Ani Budiarti, Budi Murtiyasa, and Sumardi Sumardi. "ANALISIS KESULITAN BELAJAR MATEMATIKA MATERI PENGOLAHAN DATA PADA SISWA KELAS V SD." Pendas : Jurnal Ilmiah Pendidikan Dasar 8, no. 1 (June 24, 2023): 3977–90. http://dx.doi.org/10.23969/jp.v8i1.6989.

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Research procedures with a qualitative approach (naturalistic). The information collection methods used were questionnaires, interviews, observation and documentation during mathematics education activities on data processing materials. With research subjects participating in class V SDN 2 Banyusri as many as 15 children consisting of 6 men and 9 women. Information analysis using the triangulation method and the accuracy of the observer. The purpose of this research is to analyze aspects of learning difficulties in mathematics data processing materials in fifth grade elementary school students. Research results include aspects of internal learning difficulties 46.67% and aspects of external learning difficulties 50.00%. Internal and external aspects 48, 34%.

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Reyes-Herrera, Paula H., and Elisa Ficarra. "Computational Methods for CLIP-Seq Data Processing." Bioinformatics and Biology Insights 8 (January 2014): BBI.S16803. http://dx.doi.org/10.4137/bbi.s16803.

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RNA-binding proteins (RBPs) are at the core of post-transcriptional regulation and thus of gene expression control at the RNA level. One of the principal challenges in the field of gene expression regulation is to understand RBPs mechanism of action. As a result of recent evolution of experimental techniques, it is now possible to obtain the RNA regions recognized by RBPs on a transcriptome-wide scale. In fact, CLIP-seq protocols use the joint action of CLIP, crosslinking immunoprecipitation, and high-throughput sequencing to recover the transcriptome-wide set of interaction regions for a particular protein. Nevertheless, computational methods are necessary to process CLIP-seq experimental data and are a key to advancement in the understanding of gene regulatory mechanisms. Considering the importance of computational methods in this area, we present a review of the current status of computational approaches used and proposed for CLIP-seq data.

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Istikomah, Endang, Sari Herlina, and Nurmaliza Nurmaliza. "IT - Based Mathematics Learning Module To Decrease Students’ Mathematical Anxiety." Mathline : Jurnal Matematika dan Pendidikan Matematika 7, no. 1 (April 18, 2022): 156–66. http://dx.doi.org/10.31943/mathline.v7i1.242.

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The purpose of this study was to determine the level of students' mathematics anxiety and student learning outcomes after using the PMB IT module. The type of this research is Pre-Experiment with One Group Pretest-Posttest design. The instruments used are student mathematics anxiety questionnaire sheets and student response questionnaires, while student learning outcomes can be seen after the UAS exam. The data analysis technique used is qualitative and quantitative data analysis. For data processing, the writer used SPSS 16 software and Microsoft Excel 2013. The students' mathematical anxiety data obtained were analyzed descriptively and student learning outcomes data were processed using Inferential Statistics One-Sample T-Test. The results showed that the level of students' mathematics anxiety after using the PMBIT module decreased by an average of 11.55. While the level of students' mathematics anxiety after using the module in the low, medium, and high categories, respectively, was 40.5%, 56.8%, and 2.7%. Furthermore, there is an increase in student mathematics learning outcomes using the PMBIT Module with sig. 0.029 and a mean of 81.59

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Kahsay, Robel, Jeet Vora, Rahi Navelkar, Reza Mousavi, Brian C. Fochtman, Xavier Holmes, Nagarajan Pattabiraman, et al. "GlyGen data model and processing workflow." Bioinformatics 36, no. 12 (April 23, 2020): 3941–43. http://dx.doi.org/10.1093/bioinformatics/btaa238.

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Abstract Summary Glycoinformatics plays a major role in glycobiology research, and the development of a comprehensive glycoinformatics knowledgebase is critical. This application note describes the GlyGen data model, processing workflow and the data access interfaces featuring programmatic use case example queries based on specific biological questions. The GlyGen project is a data integration, harmonization and dissemination project for carbohydrate and glycoconjugate-related data retrieved from multiple international data sources including UniProtKB, GlyTouCan, UniCarbKB and other key resources. Availability and implementation GlyGen web portal is freely available to access at https://glygen.org. The data portal, web services, SPARQL endpoint and GitHub repository are also freely available at https://data.glygen.org, https://api.glygen.org, https://sparql.glygen.org and https://github.com/glygener, respectively. All code is released under license GNU General Public License version 3 (GNU GPLv3) and is available on GitHub https://github.com/glygener. The datasets are made available under Creative Commons Attribution 4.0 International (CC BY 4.0) license. Supplementary information Supplementary data are available at Bioinformatics online.

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Garcia de la Nava, J., S. van Hijum, and O. Trelles. "PreP: gene expression data pre-processing." Bioinformatics 19, no. 17 (November 20, 2003): 2328–29. http://dx.doi.org/10.1093/bioinformatics/btg318.

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Golovan, A. A., and N. B. Vavilova. "Satellite navigation. Raw data processing for geophysical applications." Journal of Mathematical Sciences 146, no. 3 (October 2007): 5920–30. http://dx.doi.org/10.1007/s10958-007-0406-9.

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Et.al, Anjana Yadav. "Improving the Performance of Multidimensional Clinical Data for OLAP using an Optimized Data Clustering approach." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 3269–75. http://dx.doi.org/10.17762/turcomat.v12i3.1575.

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Medicine is a fresh way to utilize for curing, analyzing and detecting the diseases through data clustering with OLAP (Online Analytical Processing). The large amount of multidimensional clinical data is reduced the efficiency of OLAP query processing by enhancing the query accessing time. Hence, the performance of OLAP model is improved by using data clustering in which huge data is divided into several groups (clusters) with cluster heads to achieve fast query processing in least time. In this paper, a Dragon Fly Optimization based Clustering (DFOC) approach is proposed to enhance the efficiency of data clustering by generating optimal clusters from multidimensional clinical data for OLAP. The results are evaluated on MATLAB 2019a tool and shown the better performance of DFOC against other clustering methods ACO, GA and K-Means in terms of intra-cluster distance, purity index, F-measure, and standard deviation

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Wiaux, Y., J. D. McEwen, and P. Vielva. "Complex Data Processing: Fast Wavelet Analysis on the Sphere." Journal of Fourier Analysis and Applications 13, no. 4 (April 11, 2007): 477–93. http://dx.doi.org/10.1007/s00041-006-6917-9.

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Romansky, Radi. "Mathematical Modelling and Study of Stochastic Parameters of Computer Data Processing." Mathematics 9, no. 18 (September 12, 2021): 2240. http://dx.doi.org/10.3390/math9182240.

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The main goal of dispatching strategies is to minimize the total time for processing tasks at maximum performance of the computer system, which requires strict regulation of the workload of the processing units. To achieve this, it is necessary to conduct a preliminary study of the applied model for planning. The purpose of this article is to present an approach for automating the investigation and optimization of processes in a computer environment for task planning and processing. A stochastic input flow of incoming tasks for processing is considered and mathematical formalization of some probabilistic characteristics related to the complexity of its servicing has been made. On this basis, a software module by using program language APL2 has been developed to conduct experiments for analytical study and obtaining estimates of stochastic parameters of computer processing and dispatching. The proposed model is part of a generalized environment for program investigation of the computer processing organization and expands its field of application with additional research possibilities.

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Metre, K. V. "Location based Continuous Query Processing over Geo-streaming Data." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (April 11, 2021): 106–14. http://dx.doi.org/10.17762/turcomat.v12i1s.1583.

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In recent years, many data-intensive and location based applications have emerged that need to process stream data in applications such as network monitoring, telecommunications data management, and sensor networks. Unlike regular queries, a continuous query exists for certain period of time and need to be continuously processed during this time. The algorithms used for data processing for the traditional database systems are not suited to tackle complex and various continuous queries over dynamic streaming data. The indexing for finite queries is preferred to indexing on infinite data to avoid expensive operations of index maintenance. Previous related work focused on moving queries on static objects or static queries on moving object. But now-a-days queries as well as objects are dynamic. So, hybrid indexing for queries significantly reduces the space costs and scales well with the increasing data. To deal with the speed of unbounded data, it is necessary to use data parallelism in query processing. The data parallelism in query processing offers better performance, availability and scalability.

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Kumar, D. N. Punith, and Akram Pasha. "Insights of Mathematics for Big Data." International Journal of Engineering and Advanced Technology 8, no. 5s (June 29, 2019): 207–12. http://dx.doi.org/10.35940/ijeat.e1043.0585s19.

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Computer Science can be considered as one of the extensions made to the pure mathematical sciences that exhibit the design and development of many mathematical models to solve various engineering problems. Data storage and data processing are the two major operations that are primarily focused by any computational model while solving a problem. Mathematical modelling has been helped in producing the various computational models across several problems that are found in the field of computer science. Among many problems that are found in the area of computer science, data science and big data have recently geared up to solve many business oriented problems that are purely based on data analytics to enhance the profit by taking critical business decisions. Data Scientists and mathematicians are found to have a skeptical understanding or too little collaboration either in knowing the mathematical concepts behind big data technologies, or too little knowledge of applications of mathematical concepts in applications of big data, respectively. Therefore, in this paper, an effort is made to bring out the major mathematical concepts that have contributed in fueling the solutions for big data problems. The authors hypothesize that the work proposed in this paper would benefit any data scientist or a mathematician to clearly understand the bridge between the math and its application in big data analytics. The authors identify the mathematical concepts and their roles played while solving various tasks that are encountered in the domains of big data. Further, such an endeavor is expected to open up many opportunities for both mathematicians and big data professionals to work collaboratively, while encouraging and contributing in enhancing interdisciplinary research across many domains of engineering.

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Kirianaki, N. V., S. Y. Yurish, N. O. Shpak, and V. P. Deynega. "Data Acquisition and Signal Processing for Smart Sensors." Measurement Science and Technology 13, no. 9 (August 14, 2002): 1501. http://dx.doi.org/10.1088/0957-0233/13/9/706.

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Sadkova, O. V., and S. M. Nikulin. "Processing of measurement data in six-port reflectometry." Measurement Techniques 42, no. 12 (December 1999): 1116–19. http://dx.doi.org/10.1007/bf02512101.

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Pal'chik, R. I., and E. N. Levengagen. "Methods for processing the data of interlaboratory comparisons." Measurement Techniques 35, no. 6 (June 1992): 684–87. http://dx.doi.org/10.1007/bf00977057.

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Fedotov, A. V., and A. N. Kompaneits. "Inductive measurement device with a data-processing unit." Measurement Techniques 29, no. 2 (February 1986): 82–83. http://dx.doi.org/10.1007/bf00868821.

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Tarbeev, Yu V., I. B. Chelpanov, and T. N. Siraya. "Metrological certification of algorithms for measurement-data processing." Measurement Techniques 28, no. 3 (March 1985): 205–8. http://dx.doi.org/10.1007/bf00861980.

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Matveev, Al A., and A. A. Matveev. "Algorithmic software for the processing of measurement data." Measurement Techniques 32, no. 1 (January 1989): 1–7. http://dx.doi.org/10.1007/bf00863627.

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Fateev, V. F., and A. P. Aleshkin. "Processing of multiple-site optical measurement data." Journal of Optical Technology 67, no. 7 (July 1, 2000): 634. http://dx.doi.org/10.1364/jot.67.000634.

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Sprindzuk, M. V., L. P. Titov, A. P. Konchits, and L. V. Mozharovskaya. "Modern transcriptome data processing algorithms: a review of methods and results of approbation." «System analysis and applied information science», no. 2 (August 19, 2021): 54–62. http://dx.doi.org/10.21122/2309-4923-2021-2-54-62.

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Analysis of bioinformatics data is an actual problem in modern computational biology and applied mathematics. With the development of biotechnology and tools for obtaining and processing such information, unresolved issues of the development and application of new algorithms and software have emerged.Authors propose practical algorithms and methods for processing transcriptomic data for efficient results of annotation, visualization and interpretation of bioinformatics data.

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Colling, D., D. Britton, J. Gordon, S. Lloyd, A. Doyle, P. Gronbech, J. Coles, et al. "Processing LHC data in the UK." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371, no. 1983 (January 28, 2013): 20120094. http://dx.doi.org/10.1098/rsta.2012.0094.

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The Large Hadron Collider (LHC) is one of the greatest scientific endeavours to date. The construction of the collider itself and the experiments that collect data from it represent a huge investment, both financially and in terms of human effort, in our hope to understand the way the Universe works at a deeper level. Yet the volumes of data produced are so large that they cannot be analysed at any single computing centre. Instead, the experiments have all adopted distributed computing models based on the LHC Computing Grid. Without the correct functioning of this grid infrastructure the experiments would not be able to understand the data that they have collected. Within the UK, the Grid infrastructure needed by the experiments is provided by the GridPP project. We report on the operations, performance and contributions made to the experiments by the GridPP project during the years of 2010 and 2011—the first two significant years of the running of the LHC.

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Popkov, Yu S. "Randomization and Entropy in Machine Learning and Data Processing." Doklady Mathematics 105, no. 3 (June 2022): 135–57. http://dx.doi.org/10.1134/s1064562422030073.

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Béréziat, Dominique, and Isabelle Herlin. "Solving ill-posed Image Processing problems using Data Assimilation." Numerical Algorithms 56, no. 2 (April 27, 2010): 219–52. http://dx.doi.org/10.1007/s11075-010-9383-z.

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Bednarczuk, Piotr, and Adam Borsuk. "EFFICIENTLY PROCESSING DATA IN TABLE WITH BILLIONS OF RECORDS." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 12, no. 4 (December 30, 2022): 17–20. http://dx.doi.org/10.35784/iapgos.3058.

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Over time, systems connected to databases slow down. This is usually due to the increase in the amount of data stored in individual tables, counted even in the billions of records. Nevertheless, there are methods for making the speed of the system independent of the number of records in the database. One of these ways is table partitioning. When used correctly, the solution can ensure efficient operation of very large databases even after several years. However, not everything is predictable because of some undesirable phenomena become apparent only with a very large amount of data. The article presents a study of the execution time of the same queries with increasing number of records in a table. These studies reveal and present the timing and circumstances of the anomaly for a certain number of records.

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Zimmermann, Ralf. "Hermite Interpolation and Data Processing Errors on Riemannian Matrix Manifolds." SIAM Journal on Scientific Computing 42, no. 5 (January 2020): A2593—A2619. http://dx.doi.org/10.1137/19m1282878.

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Cronin, Anthony, Gizem Intepe, Donald Shearman, and Alison Sneyd. "Analysis using natural language processing of feedback data from two mathematics support centres." International Journal of Mathematical Education in Science and Technology 50, no. 7 (September 11, 2019): 1087–103. http://dx.doi.org/10.1080/0020739x.2019.1656831.

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Xiao, Nong, and Wei Fu. "SDPG: Spatial data processing grid." Journal of Computer Science and Technology 18, no. 4 (July 2003): 523–30. http://dx.doi.org/10.1007/bf02948927.

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Journal articles on the topic 'Mathematics – Data Processing'

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