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Journal articles on the topic 'Statistical processing of real data'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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1

Parygin, D. S., V. P. Malikov, A. V. Golubev, N. P. Sadovnikova, T. M. Petrova, and A. G. Finogeev. "Categorical data processing for real estate objects valuation using statistical analysis." Journal of Physics: Conference Series 1015 (May 2018): 032102. http://dx.doi.org/10.1088/1742-6596/1015/3/032102.

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Anju Santosh Yedatkar. "Real-time data analytics in distributed systems." International Journal of Scientific Research in Modern Science and Technology 3, no. 6 (2024): 09–16. http://dx.doi.org/10.59828/ijsrmst.v3i6.215.

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Real-time data analytics involves the processing and analysis of data as it arrives, delivering immediate insights that are crucial for time-sensitive applications. This research explores the platforms and techniques necessary for supporting real-time analytics, extending beyond traditional Event Processing Systems (EPS) to include broader big data contexts that integrate both 'data at rest' and 'data in motion' solutions. A detailed case study is presented, showcasing the application of the Event Swarm complex event processing engine in addressing financial analytics challenges. The study ide

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Nikolov, Asen, Nadezhda Sertova, and Maya Ignatova. "Real and statistical processing of barley yield data naturally contaminated with fusariotoxin." Bulgarian Journal of Animal Husbandry 61, no. 2 (2024): 49–53. http://dx.doi.org/10.61308/tixe6954.

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Fumonisins are produced by fungi of the genus Fusarium spp., which are widely spread out in the nature. They are produced during grain growth, ripening and harvesting and are known as “field mycotoxins“. Fusarium species can produce compounds that are toxic to animals and humans. In this study, the development of Fusarium toxins was determined in healthy and mechanically damaged barley grains treated and untreated with fungicide, respectively, under natural contamination. It was found that healthy kernels treated with fungicide had the highest yield of 6700 kg.ha-1 followed by the yield of dam

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Bhargavi, Tanneru. "Application of Kafka Messaging in Microservices for Real-Time Data Processing." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 11, no. 5 (2023): 1–4. https://doi.org/10.5281/zenodo.14945204.

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The growing need for scalable and efficient data processing in distributed systems has led to the widespread adoption of microservices architectures. Kafka, a distributed streaming platform, has gained significant traction as a messaging system in microservices-based applications, enabling real-time data processing and seamless communication between services. This paper explores the application of Kafka messaging in microservices for real-time data processing, discussing its implementation's benefits, challenges, and impact. The paper further highlights use cases, solutions to common problems,

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Haoxuan Sun. "Statistical analysis of clinical trial data in cancer research." World Journal of Biology Pharmacy and Health Sciences 20, no. 3 (2024): 417–28. https://doi.org/10.30574/wjbphs.2024.20.3.0972.

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Statistical methods are fundamental to designing and executing cancer clinical trials, which present unique challenges such as patient diversity, incomplete data, and ethical considerations. This article underscores the pivotal role of advanced statistical techniques in ensuring reliable outcomes, with a focus on sample size estimation, randomization, and endpoint selection. Key methodologies discussed include Cox regression, Bayesian modeling, and machine learning applications for predictive analytics and real-time data processing. Practical solutions to challenges like treatment effect asses

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Sun, Hong Feng, Ying Li, and Hong Lv. "Statistical Analysis of the Massive Traffic Data Based on Cloud Platform." Advanced Materials Research 717 (July 2013): 662–66. http://dx.doi.org/10.4028/www.scientific.net/amr.717.662.

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Currently, with the rapid development of various geographic data acquisition technologies, the data-intensive geographic calculation is becoming more and more important. The urban motor vehicles loaded with GPS, namely the transport vehicles, can real-timely collect a large number of urban traffic information. If these massive transportation vehicle data can be real-timely collected and analyzed, the real-time and accurate basic information will be provided for monitoring the large area of traffic status as well as the intelligent traffic management. Based on the requirements of the organizati

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Nalisnik, E. V. "Methods of clinical data analysis: comparison of different methods of processing and interpretation of clinical data to improve the effectiveness of therapy." Glavvrač (Chief Medical Officer), no. 4 (March 22, 2025): 66–77. https://doi.org/10.33920/med-03-2504-13.

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Clinical data analysis is a cornerstone of evidence-based medicine and a key to improving the effectiveness of therapy. This article reviews various methods of statistical processing of clinical data - from classical statistical tests to modern machine learning methods - and their comparative effectiveness in interpreting results to improve therapeutic outcomes. We will discuss examples of real-life studies demonstrating the application of these methods, as well as approaches to summarizing data (meta-analysis, analysis of real-world data) to make better informed treatment recommendations.

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Kulakova, Irina, Ol'ga Lebedeva, and Ermak Erofeev. "PROCESSING AND ANALYSIS OF BUS FLEET DATA IN A TABLE PROCESSOR." Bulletin of the Angarsk State Technical University 1, no. 18 (2024): 213–17. https://doi.org/10.36629/2686-777x-2024-1-18-213-217.

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The article discusses the methodology of preliminary processing of statistical data of the bus fleet using Microsoft Excel. This study is conducted on the basis of real data of a transport company. The analysis is performed using such tools as functions for working with data, including pivot tables, graphs and builtin formulas

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Liu, Hua, and Nan Zhang. "Data Processing in the Key Factors Affecting China's Endowment Real Estate Enterprises Financing." Applied Mechanics and Materials 730 (January 2015): 349–52. http://dx.doi.org/10.4028/www.scientific.net/amm.730.349.

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Financing problem is one of the main reasons for restricting the development of endowment real estate enterprises in China. By analyzing the present situation of endowment real estate enterprises financing and researching relevant literatures, we sum up 20 general influence factors. Using the data processing model of the principal component analysis method to analyze the 20 general influence factors under the help of SPSS 19.0 statistical analysis software, we can find out the key influence factors which affect endowment real estate enterprises financing. Aiming at the key influence factors, w

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Sun, Jun, Feng Ye, Nadia Nedjah, Ming Zhang, and Dong Xu. "A Practical Yet Accurate Real-Time Statistical Analysis Library for Hydrologic Time-Series Big Data." Water 15, no. 4 (2023): 708. http://dx.doi.org/10.3390/w15040708.

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Using different statistical analysis methods to examine hydrologic time-series data is the basis of accurate hydrologic status analysis. With the wide application of the Internet of Things and sensor technologies, traditional statistical analysis methods are unable to meet the demand for real-time and accurate hydrologic data analysis. The existing mainstream big-data analysis platforms lack analysis methods oriented to hydrologic data. In this context, a real-time statistical analysis library based on the new generation of big data processing engine Flink, called HydroStreamingLib, was propos

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Liu, Mou Zhong, and Min Sun. "Application of Multidimensional Data Model in the Traffic Accident Data Warehouse." Applied Mechanics and Materials 548-549 (April 2014): 1857–61. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.1857.

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The traffic administrative department would record real-time information of accidents and update the corresponding database when dealing with daily traffic routines. It is of great significance to study and analyze these data. In this paper, we propose a Multi-dimensional Data Warehouse Model (M-DWM) combined with the concept of Data Warehouse and multi-dimensional data processing theory. The model can greatly improve the efficiency for statistical analysis and data mining.

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Vollmar, Melanie, James M. Parkhurst, Dominic Jaques, et al. "The predictive power of data-processing statistics." IUCrJ 7, no. 2 (2020): 342–54. http://dx.doi.org/10.1107/s2052252520000895.

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This study describes a method to estimate the likelihood of success in determining a macromolecular structure by X-ray crystallography and experimental single-wavelength anomalous dispersion (SAD) or multiple-wavelength anomalous dispersion (MAD) phasing based on initial data-processing statistics and sample crystal properties. Such a predictive tool can rapidly assess the usefulness of data and guide the collection of an optimal data set. The increase in data rates from modern macromolecular crystallography beamlines, together with a demand from users for real-time feedback, has led to pressu

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Kasimova, A. R., and Wenlong Zhao. "Analysis of real-world data for medical device evaluation: Chinese manual." Real-World Data & Evidence 4, no. 2 (2024): 13–21. http://dx.doi.org/10.37489/2782-3784-myrwd-53.

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Real-word data obtained using medical devices differ from the use of drugs. When using medical devices, the effectiveness is usually not in doubt, and the issues of biocompatibility, safety, and long-term use come to the fore. This article presents a translation of the clinical guidelines of the People’s Republic of China on planning and conducting studies on the use of medical devices in real-world clinical practice, as well as statistical processing of the information obtained in an abbreviated form.

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Pashko, Anatolii, Olena Chaikovska, and Yurii Kharchenko. "Use of Statistical Analysis Tools for ECG Processing." Digital Platform: Information Technologies in Sociocultural Sphere 6, no. 2 (2023): 284–98. http://dx.doi.org/10.31866/2617-796x.6.2.2023.293593.

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The purpose of the article is to study the algorithms of statistical and intellectual data analysis and their use for processing and analysing electrocardiograms (ECG). The methods and algorithms that form the basis of statistical data processing and analysis are considered. The research methods are based on the application of statistical methods and algorithms for the analysis and pre-processing of medical data. Pre-processing is a necessary step in data processing, which makes it possible to analyze more efficiently, build more accurate models and reduce their dimensionality. Scientific nove

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Pashko, Anatolii, Olena Chaikovska, and Yurii Kharchenko. "Use of Statistical Analysis Tools for ECG Processing." Digital Platform: Information Technologies in Sociocultural Sphere 6, no. 2 (2023): 284–98. https://doi.org/10.31866/2617-796X.6.2.2023.293593.

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<strong>The purpose of the article </strong>is to study the algorithms of statistical and intellectual data analysis and their use for processing and analysing electrocardiograms (ECG). The methods and algorithms that form the basis of statistical data processing and analysis are considered. <strong>The research methods </strong>are based on the application of statistical methods and algorithms for the analysis and pre-processing of medical data. Pre-processing is a necessary step in data processing, which makes it possible to analyze more efficiently, build more accurate models and

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Ferencz, Katalin, József Domokos, and Levente Kovács. "Analysis of Industrial Sensor Data Using Statistical and Regression Methods." SYSTEM THEORY, CONTROL AND COMPUTING JOURNAL 3, no. 1 (2023): 36–44. http://dx.doi.org/10.52846/stccj.2023.3.1.48.

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Today's industrial landscape is primarily driven by rapid and effective data processing and evaluation. Consequently, industries should devote considerable attention and resources towards real-time examination of the large data sets acquired, enabling timely extraction of vital information for outlier detection, fake data identification, and predictive analysis to mitigate unforeseen expenses. This rigorous process of data analysis necessitates the employment of a diverse set of algorithms that align with the specific objectives, spanning a wide spectrum of potential solutions. In this manuscr

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Zhao, Yu Qian, and Zhi Gang Li. "FPGA Implementation of Real-Time Adaptive Bidirectional Equalization for Histogram." Advanced Materials Research 461 (February 2012): 215–19. http://dx.doi.org/10.4028/www.scientific.net/amr.461.215.

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According to the characteristics of infrared images, a contrast enhancement algorithm was presented. The principium of FPGA-based adaptive bidirectional plateau histogram equalization was given in this paper. The plateau value was obtained by finding local maximum and whole maximum in statistical histogram based on dimensional histogram statistic. Statistical histogram was modified by the plateau value and balanced in gray scale and gray spacing. Test data generated by single frame image, which was simulated by FPGA-based real-time adaptive bidirectional plateau histogram equalization. The sim

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Zhang, Jianbing. "From Data to Decisions Exploring the Role of Data Analysis in Big Data." Journal of Computer, Signal, and System Research 2, no. 1 (2025): 19–27. https://doi.org/10.71222/8wj6pe10.

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With the rapid growth of big data, data analysis plays an increasingly important role across various industries. This paper explores the key techniques, applications, and challenges of data analysis in the context of big data. It begins by defining big data and its characteristics, focusing on the methodologies used in data analysis, such as statistical analysis, machine learning, and artificial intelligence. The paper then examines the real-world applications of data analysis in sectors like healthcare, finance, marketing, and e-commerce, showing how it drives decision-making, optimizes opera

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Zenkevich, Igor G. "Calculation of average chronological values – an undeservedly neglected method of statistical data processing." Аналитика и контроль 27, no. 1 (2023): 51–58. http://dx.doi.org/10.15826/analitika.2023.27.1.005.

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In real analytical practice involving parallel determinations there are certain objective reasons (limited measurement time, available resources, etc.) that prevent performing sufficient number of measurements required for rigorous statistical data processing. Such parameters as standard deviations are particularly unreliable for small data sets (they are usually too high in comparison with the results obtained for more representative data sets). This problem can be minimized by changing the character of data processing, i.e. calculating so-called chronological averages instead of average arit

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Chundru, Swathi. "AI-Driven Data Provenance: Tracking and Verifying Data Lineage." FMDB Transactions on Sustainable Computing Systems 2, no. 3 (2024): 107–18. https://doi.org/10.69888/ftscs.2024.000258.

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The paper looks into AI-driven data provenance systems for their feasibility in tracing and verification of lineage for healthcare and financial transaction domains. We will use sample data points from Electronic Health Records and transaction data to understand the trade-offs between real-time processing speed and tracking accuracy in the former domain and between detection accuracy and false positives in the latter domain. MATLAB and Python were utilized to analyze the data and model the system. MATLAB was used to create the simulation environment for signal processing tasks, whereas Python,

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A. Aldo Tenis. "Smart IoT Data Handling Using Deep Learning and Data Mining Approaches." Journal of Information Systems Engineering and Management 10, no. 48s (2025): 882–91. https://doi.org/10.52783/jisem.v10i48s.9658.

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The rapid proliferation of Internet of Things (IoT) devices has led to the generation of vast amounts of data, necessitating efficient data processing and analysis techniques. This research explores the synergy between deep learning and data mining in optimizing IoT data processing. By leveraging advanced algorithmic approaches, including neural networks and statistical methods, this study aims to develop effective strategies for extracting meaningful insights from complex IoT datasets. Specific techniques such as supervised learning, unsupervised learning, and reinforcement learning are evalu

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Liu, Yuxi, Yiping Zhu, and Mingzhe Wei. "Application of Point Cloud Data Processing in River Regulation." Marine Technology Society Journal 55, no. 2 (2021): 198–204. http://dx.doi.org/10.4031/mtsj.55.2.15.

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Abstract Geotextile materials are often used in river regulation projects to cut down sand loss caused by water erosion, to thus ensure a stable and safe river bed. In order to measure the overlap width in the geotextile-laying procedure, we proposed a point processing method for cloud data, which engages point cloud data obtained by 3-D imaging sonar to do automatic measurements. Firstly, random sampling and consensus point cloud segmentation and outer point filtering based on statistical analysis on density were used to extract the upper and lower plane data of the geotextile. Secondly, clus

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Daume III, H., and D. Marcu. "Domain Adaptation for Statistical Classifiers." Journal of Artificial Intelligence Research 26 (June 21, 2006): 101–26. http://dx.doi.org/10.1613/jair.1872.

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The most basic assumption used in statistical learning theory is that training data and test data are drawn from the same underlying distribution. Unfortunately, in many applications, the "in-domain" test data is drawn from a distribution that is related, but not identical, to the "out-of-domain" distribution of the training data. We consider the common case in which labeled out-of-domain data is plentiful, but labeled in-domain data is scarce. We introduce a statistical formulation of this problem in terms of a simple mixture model and present an instantiation of this framework to maximum ent

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Munavvarkhon Mukhitdinova. "INTEGRATION OF NEUROTECHNOLOGIES AND MACHINE LEARNING: A NOVEL METHODOLOGY FOR STATISTICAL ANALYSIS OF BIG DATA STREAMS." International Journal of Economics and Innovative Technologies 13, no. 1 (2025): 97–100. https://doi.org/10.55439/eit/vol13_iss1/636.

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This paper presents a new method of integrating neurotechnologies and machine learning (ML) for streaming big data statistical analysis. The method combines the flexibility of ML with cognitive modeling to address challenges in real-time processing, multidimensional data, and decision making in situations of uncertainty. The results show dramatic improvements in scalability, precision, and flexibility, with applications in finance, healthcare, and smart cities.

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Boyoukliev, I., and S. Gocheva-Ilieva. "STATISTICAL MODELING AND FORECASTING BANK DEPOSIT DATA USING RANDOM FORESTS." Sciences of Europe, no. 129 (November 27, 2023): 124–30. https://doi.org/10.5281/zenodo.10209391.

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In the field of financial and foreign exchange markets, large amounts of data of various nature are accumulated. Extracting essential information from this data aiming to provide a base for taking proper management decisions of the banks executive boards, companies and other financial institutions is an important practical task. The process of solving problems related to the processing of financial data in the era of ubiquitous digitalization is increasingly achieved with the application of the most modern and powerful mathematical tools and the techniques and algorithms developed on their bas

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Frazier, Garth. "Real-time, sample-by-sample estimation of multiple signal waveforms from acoustic array data using B-splines." Journal of the Acoustical Society of America 155, no. 3_Supplement (2024): A316. http://dx.doi.org/10.1121/10.0027646.

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This work presents a real-time signal processing algorithm that estimates time-domain waveforms of multiple plane wave signals on a sample-by-sample basis from data measured by an acoustic array. Moreover, the algorithm provides sample-by-sample estimates of the direction-of-arrival (DOA) of the waveforms. In this case sample-by-sample means that as each sample of data is measured by the array estimates of the waveforms and their directions-of-arrival are updated. While the basic idea can be based on almost any finite-dimensional approximation to a function space, this algorithm makes use of B

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Lytvynenko, T. I. "Problem of data analysis and forecasting using decision trees method." PROBLEMS IN PROGRAMMING, no. 2-3 (June 2016): 220–26. http://dx.doi.org/10.15407/pp2016.02-03.220.

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This study describes an application of the decision tree approach to the problem of data analysis and forecasting. Data processing bases on the real observations that represent sales level in the period between 2006 and 2009. R (programming language and software environment) is used as a tool for statistical computing. Paper includes comparison of the method with well-known approaches and solutions in order to improve accuracy of the gained consequences.

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Zhu, Tiexuan. "Analysis of Different Statistical Indicators for Machine Learning." Highlights in Science, Engineering and Technology 88 (March 29, 2024): 189–94. http://dx.doi.org/10.54097/7rx88a53.

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As a matter of fact, machine learning has been widely adopted in various fields on account of rapid development of computing ability in recent years. In reality, with advances in computer hardware and the explosion of data, machine learning has become an ideal choice for processing this data. With this in mind, processing such large amounts of data requires a lot of computing power and algorithms, and also offers a wide range of applications for machine learning. On this basis, this article mainly considers the formulation and application of statistical measures from his three aspects: classif

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Jiang, He, Ruochen Wang, Jiarui Zheng, Yaohao Fan, Qitong Liang, and Jiawei Tian. "US recession prediction using statistical and natural language processing methods." Theoretical and Natural Science 19, no. 1 (2023): 184–92. http://dx.doi.org/10.54254/2753-8818/19/20230535.

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This study mainly predicts the recession in the United States. We build our model based on the data of more than ten recessions experienced by the United States since the mid-20th century. Our research can be divided into two parts, one part is a machine learning model constructed using econometrics theory, and the other part is a text analysis model based on natural language processing (NLP) techniques. We collected quarterly data from January 1, 1950, to September 1, 2020, to examine each historical recessionary period. We select key macroeconomic variables such as real GDP growth rate, unem

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Osaulenko, Oleksandr H., and Olena Horobets. "Using Big Data by Ukrainian official statistics when martial law applies: problems and solutions." Statistics in Transition new series 24, no. 1 (2023): 29–43. http://dx.doi.org/10.59170/stattrans-2023-003.

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The article is focused on issues of the secure operation of official statistics in Ukraine during the application of martial law. It was found that the gaps in conventional sources of statistical data caused by the war needed to be filled with data from alternative sources, including Big Data. The level of digitalisation in Ukraine as the basis for using Big Data was analysed by the proposed indices of internetisation, social progress and digital transformation. Thanks to our research, several problems (methodological, legal, financial, and managerial) were identified as vital for statistical

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Majumdar, Chitradeep, Miguel Lopez-Benitez, and Shabbir N. Merchant. "Real Smart Home Data-Assisted Statistical Traffic Modeling for the Internet of Things." IEEE Internet of Things Journal 7, no. 6 (2020): 4761–76. http://dx.doi.org/10.1109/jiot.2020.2969318.

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Somplak, Radovan, Zlata Smidova, Veronika Smejkalova, and Vlastimir Nevrly. "Statistical Evaluation of Large-Scale Data Logistics System." MENDEL 24, no. 2 (2018): 9–16. http://dx.doi.org/10.13164/mendel.2018.2.009.

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Data recording is struggling with the occurrence of errors, which worsen the accuracy of follow-up calculations. Achievement of satisfactory results requires the data processing to eliminate the influence of errors. This paper applies a data reconciliation technique for mining of data from ecording movement vehicles. The database collects information about the start and end point of the route (GPS coordinates) and total duration.The presented methodology smooths available data and allows to obtain an estimation of transportation time through individual parts of the entire recorded route. This

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Dominguez, Oscar. "Theory of Sampling and QAQC enabling the application and expectations of new technology and data processing." TOS Forum 2022, no. 11 (2022): 11. http://dx.doi.org/10.1255/tosf.130.

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Currently, there are high expectations in the mining industry, across the Supply Chain, on how sensors and new technology providing real time data can support and optimise business decisions. In addition, sophisticated statistical algorithms, such as machine learning or conditional simulations, are more and more explored/used to address topics as uncertainty and “optimisations” in the plans, at different horizons, to “maximise the value of the business”. Despite the future of data collection is heading in the direction where sensors will be providing real time in-formation, this is still in th

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Tigani, Smail. "Geo-Statistics and Deep Learning-Based Algorithm Design for Real-Time Bus Geo-Location and Arrival Time Estimation Features with Load Resiliency Capacity." AI 6, no. 7 (2025): 142. https://doi.org/10.3390/ai6070142.

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This paper introduces a groundbreaking decentralized approach for real-time bus monitoring and geo-location, leveraging advanced geo-statistical and multivariate statistical methods. The proposed long short-term memory (LSTM) model predicts bus arrival times with confidence intervals and reconstructs missing positioning data, offering cities an accurate, resource-efficient tracking solution within typical infrastructure limits. By employing decentralized data processing, our system significantly reduces network traffic and computational load, enabling data sharing and sophisticated analysis. U

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Naga, Srinivasa Rao Balajepally, and Krishna Prasad Bodapati Rama. "Modernizing Cloud Software Systems: Data Integration Techniques with SQL and Google Maps API." Sarcouncil Journal of Engineering and Computer Sciences 4, no. 1 (2025): 9–16. https://doi.org/10.5281/zenodo.15047589.

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The integration of SQL and the Google Maps API in modern cloud software systems presents a powerful approach to combining structured and geospatial data for enhanced decision-making and operational efficiency. This study explores techniques for seamless data integration, focusing on performance, scalability, and real-world applicability. Through a mixed-methods approach, the research evaluates key metrics such as data retrieval time, query execution time, system latency, and resource utilization under varying conditions. A logistics management case study demonstrates the practical benefits of

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Branisavljević, Nemanja, Zoran Kapelan, and Dušan Prodanović. "Improved real-time data anomaly detection using context classification." Journal of Hydroinformatics 13, no. 3 (2011): 307–23. http://dx.doi.org/10.2166/hydro.2011.042.

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The number of automated measuring and reporting systems used in water distribution and sewer systems is dramatically increasing and, as a consequence, so is the volume of data acquired. Since real-time data is likely to contain a certain amount of anomalous values and data acquisition equipment is not perfect, it is essential to equip the SCADA (Supervisory Control and Data Acquisition) system with automatic procedures that can detect the related problems and assist the user in monitoring and managing the incoming data. A number of different anomaly detection techniques and methods exist and c

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Bolívar, Sergio, Alicia Nieto-Reyes, and Heather L. Rogers. "Statistical Depth for Text Data: An Application to the Classification of Healthcare Data." Mathematics 11, no. 1 (2023): 228. http://dx.doi.org/10.3390/math11010228.

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This manuscript introduces a new concept of statistical depth function: the compositional D-depth. It is the first data depth developed exclusively for text data, in particular, for those data vectorized according to a frequency-based criterion, such as the tf-idf (term frequency–inverse document frequency) statistic, which results in most vector entries taking a value of zero. The proposed data depth consists of considering the inverse discrete Fourier transform of the vectorized text fragments and then applying a statistical depth for functional data, D. This depth is intended to address the

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Lauritsen, K. B., S. Syndergaard, H. Gleisner, et al. "Processing and validation of refractivity from GRAS radio occultation data." Atmospheric Measurement Techniques Discussions 4, no. 2 (2011): 2189–205. http://dx.doi.org/10.5194/amtd-4-2189-2011.

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Abstract. We discuss the processing of GRAS radio occultation (RO) data done at the GRAS Satellite Application Facility. The input data consists of operational near-real time bending angles from December 2010 from the Metop-A satellite operated by EUMETSAT. The data are processed by an Abel inversion algorithm in combination with statistical optimization based on a two-parameter fit to an MSIS climatology. We compare retrieved refractivity to analyses from ECMWF. It is found that for global averages, the mean differences to ECMWF analyses are smaller than 0.2% below 30 km (except near the surf

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Lauritsen, K. B., S. Syndergaard, H. Gleisner, et al. "Processing and validation of refractivity from GRAS radio occultation data." Atmospheric Measurement Techniques 4, no. 10 (2011): 2065–71. http://dx.doi.org/10.5194/amt-4-2065-2011.

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Abstract. We discuss the processing of GRAS radio occultation (RO) data done at the GRAS Satellite Application Facility. The input data consists of operational near-real time bending angles from December 2010 from the Metop-A satellite operated by EUMETSAT. The data are processed by an Abel inversion algorithm in combination with statistical optimization based on a two-parameter fit to an MSIS climatology. We compare retrieved refractivity to analyses from ECMWF. It is found that for global averages, the mean differences to ECMWF analyses are smaller than 0.2% below 30 km (except near the surf

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Pathe, Anirudh Reddy. "Machine Learning-Based Outlier Detection for Business Intelligence: A Scalable Time Series Analysis Framework." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–6. https://doi.org/10.55041/ijsrem6716.

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The exponential growth in digital business operations has resulted in an unprecedented volume of time series data generated from diverse business metrics, creating an urgent need for sophisticated anomaly detection systems. This paper presents a comprehensive framework for detecting outliers in business time series data using advanced machine learning techniques, addressing the challenges of scale, accuracy, and real-time processing. We propose a novel hybrid approach that seamlessly integrates statistical methods with deep learning architectures to identify both point anomalies and pattern de

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Allam, Tahani M. "Estimate the Performance of Cloudera Decision Support Queries." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 01 (2022): 127–38. http://dx.doi.org/10.3991/ijoe.v18i01.27877.

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Hive and Impala queries are used to process a big amount of data. The overwriting amount of information requires an efficient data processing system. When we deal with a long-term batch query and analysis Hive will be more suitable for this query. Impala is the most powerful system suitable for real-time interactive Structured Query Language (SQL) query which are added a massive parallel processing to Hadoop distributed cluster. The data growth makes a problem with SQL Cluster because the execution processing time is increased. In this paper, a comparison is demonstrated between the performanc

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Zhang, Chunzhen, and Lei Hou. "Data middle platform construction: The strategy and practice of National Bureau of Statistics of China." Statistical Journal of the IAOS 36, no. 4 (2020): 979–86. http://dx.doi.org/10.3233/sji-200754.

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To address the data ‘islandization’ issue in the statistical field and to take advantage of the opportunity of the Statistical Cloud construction, the National Bureau of Statistics of China (NBS) started adopting the concept of a “data middle platform” for data resource planning. With it, NBS aims to build a comprehensive data capability platform that includes data collection and exchange; data sharing and integration; data organizing and processing; data modeling and analyses; data management and governance; and data service and application. The statistical data middle platform provides the b

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Sarkar, Agnidipta, Rojina Khatun, Sudeshna Sengupta, and Malavika Bhattacharya. "Microscopy-based Data Processing in Cell Biology." Cell Biology 13, no. 1 (2025): 1–22. https://doi.org/10.11648/j.cb.20251301.11.

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By making it possible to extract intricate and significant biological information from visual imaging, data processing based on microscopy has completely changed contemporary cell biology. Researchers have overcome historical constraints by combining microscopy with sophisticated image processing technologies, opening up new possibilities for comprehending cellular architecture and functions in unprecedented detail. The goal of this study is to present a thorough examination of the methods and new developments in microscopy-driven data analysis, emphasizing both the theoretical underpinnings a

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Patsidis, Angelos, Adam Dyśko, Campbell Booth, Anastasios Oulis Rousis, Polyxeni Kalliga, and Dimitrios Tzelepis. "Digital Architecture for Monitoring and Operational Analytics of Multi-Vector Microgrids Utilizing Cloud Computing, Advanced Virtualization Techniques, and Data Analytics Methods." Energies 16, no. 16 (2023): 5908. http://dx.doi.org/10.3390/en16165908.

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Microgrids are considered a viable solution for achieving net-zero targets and increasing renewable energy integration. However, there is a lack of conceptual work focusing on practical data analytics deployment schemes and case-specific insights. This paper presents a scalable and flexible physical and digital architecture for extracting data-driven insights from microgrids, with a real-world microgrid utilized as a test-bed. The proposed architecture includes edge monitoring and intelligence, data-processing mechanisms, and edge–cloud communication. Cloud-hosted data analytics have been deve

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Fedotov, S. N. "An approach to computer analysis of the ligand binding assay data on example of radioligand assay data." Journal of Bioinformatics and Computational Biology 18, no. 02 (2020): 2050014. http://dx.doi.org/10.1142/s0219720020500146.

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As a rule, receptor-ligand assay data are fitted by logistic functions (4PL model, 5PL model, Feldman’s model). The preparation of the initial estimates for parameters of these functions is an important problem for processing receptor-ligand interaction data. This study represents a new mathematical approach to calculate the initial estimates more closely to the true values of parameters. The main idea of this approach is in using the modified linear least squares method for calculations of the parameters for the 4PL model and the Feldman’s model. In this study, the convergence of model parame

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Carrillo, Rafael E., Martin Leblanc, Baptiste Schubnel, Renaud Langou, Cyril Topfel, and Pierre-Jean Alet. "High-Resolution PV Forecasting from Imperfect Data: A Graph-Based Solution." Energies 13, no. 21 (2020): 5763. http://dx.doi.org/10.3390/en13215763.

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Operating power systems with large amounts of renewables requires predicting future photovoltaic (PV) production with fine temporal and spatial resolution. State-of-the-art techniques combine numerical weather predictions with statistical post-processing, but their resolution is too coarse for applications such as local congestion management. In this paper we introduce computing methods for multi-site PV forecasting, which exploit the intuition that PV systems provide a dense network of simple weather stations. These methods rely entirely on production data and address the real-life challenges

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Wang, Keying. "Inference of Behavioral Decision-Making Using AI and Statistical Data Analysis in the Big Data Environment." Theoretical and Natural Science 86, no. 1 (2025): 101–7. https://doi.org/10.54254/2753-8818/2025.20168.

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Big data are of essence to enhance the accuracy of making decisions and in improving the ability to forecast. However, working with large and complicated datasets, the traditional methods of analyzing data tend to be grossly inadequate. It is at this point, amongst others, that artificial intelligence has now presented a feasible solution to such limitations, especially with the model called machine learning. Based on that, this research will look into the aspect of integration between artificial intelligence and statistical methods of analysis in inferring behavioral decisions from big data.

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Rinberg, Arik, Alexander Spiegelman, Edward Bortnikov, et al. "Fast Concurrent Data Sketches." ACM Transactions on Parallel Computing 9, no. 2 (2022): 1–35. http://dx.doi.org/10.1145/3512758.

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Data sketches are approximate succinct summaries of long data streams. They are widely used for processing massive amounts of data and answering statistical queries about it. Existing libraries producing sketches are very fast, but do not allow parallelism for creating sketches using multiple threads or querying them while they are being built. We present a generic approach to parallelising data sketches efficiently and allowing them to be queried in real time, while bounding the error that such parallelism introduces. Utilising relaxed semantics and the notion of strong linearisability, we pr

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Vasilyeva, Natalia, and Ivan Pavlyuk. "Analysis of Operational Control Data and Development of a Predictive Model of the Content of the Target Component in Melting Products." Eng 5, no. 3 (2024): 1752–67. http://dx.doi.org/10.3390/eng5030092.

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The relevance of this research is due to the need to stabilize the composition of the melting products of copper–nickel sulfide raw materials. Statistical methods of analyzing the historical data of the real technological object and the correlation analysis of process parameters are described. Factors that exert the greatest influence on the main output parameter (the fraction of copper in a matte) and ensure the physical–chemical transformations are revealed: total charge rate, overall blast volume, oxygen content in the blast (degree of oxygen enrichment in the blowing), temperature of exhau

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Oya, Antonia. "RKHS Representations for Augmented Quaternion Random Signals: Application to Detection Problems." Mathematics 10, no. 23 (2022): 4432. http://dx.doi.org/10.3390/math10234432.

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The reproducing kernel Hilbert space (RKHS) methodology has shown to be a suitable tool for the resolution of a wide range of problems in statistical signal processing both in the real and complex domains. It relies on the idea of transforming the original functional data into an infinite series representation by projection onto an specific RKHS, which usually simplifies the statistical treatment without any loss of efficiency. Moreover, the advantages of quaternion algebra over real-valued three and four-dimensional vector algebra in the modelling of multidimensional data have been proven use

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