Relevant bibliographies by topics / Online data stream processing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Online data stream processing'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Online data stream processing"

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CHEN, HUI. "EFFICIENTLY MINING RECENT FREQUENT PATTERNS OVER ONLINE TRANSACTIONAL DATA STREAMS." International Journal of Software Engineering and Knowledge Engineering 19, no. 05 (2009): 707–25. http://dx.doi.org/10.1142/s0218194009004325.

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Recent emerging applications, such as network traffic analysis, web click stream mining, power consumption measurement, sensor network data analysis, and dynamic tracing of stock fluctuation, call for study of a new kind of data, stream data. Many data stream management systems, prototype systems and software components have been developed to manage the streams or extract knowledge from stream data. Mining frequent patterns is a foundational job for the methods of data mining and knowledge discovery. This paper proposes an algorithm for mining the recent frequent patterns over an online data s
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Chen, Zhenhua, Jielong Xu, Jian Tang, Kevin A. Kwiat, Charles Alexandre Kamhoua, and Chonggang Wang. "GPU-Accelerated High-Throughput Online Stream Data Processing." IEEE Transactions on Big Data 4, no. 2 (2018): 191–202. http://dx.doi.org/10.1109/tbdata.2016.2616116.

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Alzghoul, Ahmad. "Monitoring Big Data Streams Using Data Stream Management Systems: Industrial Needs, Challenges, and Improvements." Advances in Operations Research 2023 (June 27, 2023): 1–12. http://dx.doi.org/10.1155/2023/2596069.

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Real-time monitoring systems are important for industry since they allow for avoiding unplanned system stops and keeping system availability high. The technical requirements for such systems include being both scalable and online, as the amount of generated data is increasing with time. Therefore, monitoring systems must integrate tools that can manage and analyze the data streams. The data stream management system is a stream processing tool that has the ability to manage and support operations on data streams in real-time. Several researchers have proposed and tested real-time monitoring sys
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Wang, Haibo, Chaoyi Ma, Olufemi O. Odegbile, Shigang Chen, and Jih-Kwon Peir. "Randomized error removal for online spread estimation in data streaming." Proceedings of the VLDB Endowment 14, no. 6 (2021): 1040–52. http://dx.doi.org/10.14778/3447689.3447707.

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Measuring flow spread in real time from large, high-rate data streams has numerous practical applications, where a data stream is modeled as a sequence of data items from different flows and the spread of a flow is the number of distinct items in the flow. Past decades have witnessed tremendous performance improvement for single-flow spread estimation. However, when dealing with numerous flows in a data stream, it remains a significant challenge to measure per-flow spread accurately while reducing memory footprint. The goal of this paper is to introduce new multi-flow spread estimation designs
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Li, Jun, and Yanzhao Liu. "An Efficient Data Analysis Framework for Online Security Processing." Journal of Computer Networks and Communications 2021 (April 1, 2021): 1–12. http://dx.doi.org/10.1155/2021/9290853.

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Industrial cloud security and internet of things security represent the most important research directions of cyberspace security. Most existing studies on traditional cloud data security analysis were focused on inspecting techniques for block storage data in the cloud. None of them consider the problem that multidimension online temp data analysis in the cloud may appear as continuous and rapid streams, and the scalable analysis rules are continuous online rules generated by deep learning models. To address this problem, in this paper we propose a new LCN-Index data security analysis framewo
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Ramzan, Faisal, and Muawaz Ayyaz. "A COMPREHENSIVE REVIEW ON DATA STREAM MINING TECHNIQUES FOR DATA CLASSIFICATION; AND FUTURE TRENDS." EPH - International Journal of Science And Engineering 9, no. 3 (2023): 1–29. http://dx.doi.org/10.53555/ephijse.v9i3.201.

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Data Mining is a developing interdisciplinary control managing Data Reclamation and Data Stream Mining techniques, whose subject is gathering, overseeing, processing, breaking down, and visualizing the huge volume of organized or unstructured data. Data stream mining indicates how to look at Unknown patterns from a massive amount of data over algorithms. It has experienced quick improvement with significant progress in math, statistics, data science, and computer science domains. Data streams are commonly generated by various sources such as sensor networks, social media feeds, financial trans
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Al Jawarneh, Isam Mashhour, Paolo Bellavista, Antonio Corradi, Luca Foschini, and Rebecca Montanari. "QoS-Aware Approximate Query Processing for Smart Cities Spatial Data Streams." Sensors 21, no. 12 (2021): 4160. http://dx.doi.org/10.3390/s21124160.

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Large amounts of georeferenced data streams arrive daily to stream processing systems. This is attributable to the overabundance of affordable IoT devices. In addition, interested practitioners desire to exploit Internet of Things (IoT) data streams for strategic decision-making purposes. However, mobility data are highly skewed and their arrival rates fluctuate. This nature poses an extra challenge on data stream processing systems, which are required in order to achieve pre-specified latency and accuracy goals. In this paper, we propose ApproxSSPS, which is a system for approximate processin
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Huang, Xinyuan, Xiaoming Gao, Sida Ouyang, and Zhengbo Fu. "Image Data Stream Organization and Online Analysis Application Based on Data Cube Technology." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1-2024 (May 10, 2024): 271–76. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-2024-271-2024.

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Abstract. This study aims to explore the important role of data-like cube structures in modern remote sensing data processing and data analysis through ArcPy and Python multiprocessing techniques. A multi-scale spatial data cube is innovatively developed to improve the efficiency of remote sensing data management and optimize data analysis. The core of this study is to define and implement grid cells of different sizes that form the basis of data cube, and to quantify the efficient coverage of specific areas using Python multiprocessing techniques. Experiments were conducted in Hainan Province
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Dexter, Philip, Yu David Liu, and Kenneth Chiu. "The essence of online data processing." Proceedings of the ACM on Programming Languages 6, OOPSLA2 (2022): 899–928. http://dx.doi.org/10.1145/3563320.

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Data processing systems are a fundamental component of the modern computing stack. These systems are routinely deployed online: they continuously receive the requests of data processing operations, and continuously return the results to end users or client applications. Online data processing systems have unique features beyond conventional data processing, and the optimizations designed for them are complex, especially when data themselves are structured and dynamic. This paper describes DON Calculus, the first rigorous foundation for online data processing. It captures the essential behavior
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Li, Guang Di, Guo Yin Wang, Xue Rui Zhang, Wei Hui Deng, and Fan Zhang. "Forest Cover Types Classification Based on Online Machine Learning on Distributed Cloud Computing Platforms of Storm and SAMOA." Advanced Materials Research 955-959 (June 2014): 3803–12. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.3803.

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Storm is the most popular realtime stream processing platform, which can be used to deal with online machine learning. Similar to how Hadoop provides a set of general primitives for doing batch processing, Storm provides a set of general primitives for doing realtime computation. SAMOA includes distributed algorithms for the most common machine learning tasks like Mahout for Hadoop. SAMOA is both a platform and a library. In this paper, Forest cover types, a large benchmaking dataset available at the UCI KDD Archive is used as the data stream source. Vertical Hoeffding Tree, a parallelizing st
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Dissertations / Theses on the topic "Online data stream processing"

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Ahmed, Abdulbasit. "Online network intrusion detection system using temporal logic and stream data processing." Thesis, University of Liverpool, 2013. http://livrepository.liverpool.ac.uk/12153/.

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These days, the world is becoming more interconnected, and the Internet has dominated the ways to communicate or to do business. Network security measures must be taken to protect the organization environment. Among these security measures are the intrusion detection systems. These systems aim to detect the actions that attempt to compromise the confidentiality, availability, and integrity of a resource by monitoring the events occurring in computer systems and/or networks. The increasing amounts of data that are transmitted at higher and higher speed networks created a challenging problem for
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Tahiri, Ardit. "Online Stream Processing di Big Data su Apache Storm per Applicazioni di Instant Coupon." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10311/.

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Big data è il termine usato per descrivere una raccolta di dati così estesa in termini di volume,velocità e varietà da richiedere tecnologie e metodi analitici specifici per l'estrazione di valori significativi. Molti sistemi sono sempre più costituiti e caratterizzati da enormi moli di dati da gestire,originati da sorgenti altamente eterogenee e con formati altamente differenziati,oltre a qualità dei dati estremamente eterogenei. Un altro requisito in questi sistemi potrebbe essere il fattore temporale: sempre più sistemi hanno bisogno di ricevere dati significativi dai Big Data il prima p
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VASCONCELOS, IGOR OLIVEIRA. "A MOBILE AND ONLINE OUTLIER DETECTION OVER MULTIPLE DATA STREAMS: A COMPLEX EVENT PROCESSING APPROACH FOR DRIVING BEHAVIOR DETECTION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2017. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=30648@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>Dirigir é uma tarefa diária que permite uma locomoção mais rápida e mais confortável, no entanto, mais da metade dos acidentes fatais estão relacionados à imprudência. Manobras imprudentes podem ser detectadas com boa precisão, analisando dados relativos à interação motorista-veículo, por exemplo, curvas, aceleração e desaceleração abruptas. Embora existam algoritmos para detecç
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Silva, Ticiana Linhares Coelho da. "Online clustering of trajectory data stream." reponame:Repositório Institucional da UFC, 2016. http://www.repositorio.ufc.br/handle/riufc/22045.

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SILVA, Ticiana Linhares Coelho da. Online clustering of trajectory data stream. 2016. 113 f. Tese (Doutorado em Ciência da Computação)-Universidade Federal do Ceará, Fortaleza, 2016.<br>Submitted by Jairo Viana (jairo@ufc.br) on 2017-02-17T18:32:07Z No. of bitstreams: 1 2016_tese_tlcsilva.pdf: 21709584 bytes, checksum: 1454aec7cf746b2ad56eda5865264deb (MD5)<br>Approved for entry into archive by Jairo Viana (jairo@ufc.br) on 2017-02-17T18:32:27Z (GMT) No. of bitstreams: 1 2016_tese_tlcsilva.pdf: 21709584 bytes, checksum: 1454aec7cf746b2ad56eda5865264deb (MD5)<br>Made available in DSpace on 2017
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Schmidt, Sven. "Quality of service aware data stream processing." Doctoral thesis, [S.l.] : [s.n.], 2007. http://deposit.ddb.de/cgi-bin/dokserv?idn=983780625.

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Vijayakumar, Nithya Nirmal. "Data management in distributed stream processing systems." [Bloomington, Ind.] : Indiana University, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3278228.

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Thesis (Ph.D.)--Indiana University, Dept. of Computer Science, 2007.<br>Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6093. Adviser: Beth Plale. Title from dissertation home page (viewed May 9, 2008).
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Schmidt, Sven. "Quality-of-Service-Aware Data Stream Processing." Doctoral thesis, Technische Universität Dresden, 2006. https://tud.qucosa.de/id/qucosa%3A23955.

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Data stream processing in the industrial as well as in the academic field has gained more and more importance during the last years. Consider the monitoring of industrial processes as an example. There, sensors are mounted to gather lots of data within a short time range. Storing and post-processing these data may occasionally be useless or even impossible. On the one hand, only a small part of the monitored data is relevant. To efficiently use the storage capacity, only a preselection of the data should be considered. On the other hand, it may occur that the volume of incoming data is general
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Bai, Yijian. "Data stream processing and query optimization techniques." Diss., Restricted to subscribing institutions, 2007. http://proquest.umi.com/pqdweb?did=1472132461&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Chen, Jian. "Maintaining Stream Data Distribution Over Sliding Window." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35321.

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In modern applications, it is a big challenge that analyzing the order statistics about the most recent parts of the high-volume and high velocity stream data. There are some online quantile algorithms that can keep the sketch of the data in the sliding window and they can answer the quantile or rank query in a very short time. But most of them take the GK algorithm as the subroutine, which is not known to be mergeable. In this paper, we propose another algorithm to keep the sketch that maintains the order statistics over sliding windows. For the fixed-size window, the existing algorithms can’
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Works, Karen E. "Targeted Prioritized Processing in Overloaded Data Stream Systems." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-dissertations/414.

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"We are in an era of big data, sensors, and monitoring technology. One consequence of this technology is the continuous generation of massive volumes of streaming data. To support this, stream processing systems have emerged. These systems must produce results while meeting near-real time response obligations. However, computation intensive processing on high velocity streams is challenging. Stream arrival rates are often unpredictable and can fluctuate. This can cause systems to not always be able to process all incoming data within their required response time.Yet inherently some results may
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Books on the topic "Online data stream processing"

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Babichev, Sergii, Dmytro Peleshko, and Olena Vynokurova, eds. Data Stream Mining & Processing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61656-4.

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Eliason, Alan L. Online business computer applications. 2nd ed. Science Research Associates, 1987.

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Eliason, Alan L. Online business computer applications. 3rd ed. Macmillan Pub. Co., 1991.

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Claybrook, Billy G. OLTP, online transaction processing systems. J. Wiley, 1992.

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Ashley, Ruth. Online communications software. McGraw-Hill, 1989.

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United States. Federal Trade Commission. Division of Consumer and Business Education. Your health online. Federal Trade Commission, Bureau of Consumer Protection, Division of Consumer & Business Education, 2009.

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Ashley, Ruth. Online communications software. McGraw-Hill, 1989.

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Horton, William K. Designing and writing online documentation: Help files to hypertext. Wiley, 1990.

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Holleman, Gary. Travel and hospitality online: A guide to online services. Van Nostrand Reinhold, 1996.

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Group, Yankee, ed. On-line data services. Yankee Group, 1985.

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Book chapters on the topic "Online data stream processing"

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Gama, João. "Trends in Data Stream Mining." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_15.

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AbstractLearning from data streams is a hot topic in machine learning and data mining. This article presents our recent work on the topic of learning from data streams. We focus on emerging topics, including fraud detection and hyper-parameter tuning for streaming data. The first study is a case study on interconnected by-pass fraud. This is a real-world problem from high-speed telecommunications data that clearly illustrates the need for online data stream processing. In the second study, we present an optimization algorithm for online hyper-parameter tuning from nonstationary data streams.
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Ding, Jiafeng, Junhua Fang, Pingfu Chao, Jiajie Xu, PengPeng Zhao, and Lei Zhao. "A Distributed Framework for Online Stream Data Clustering." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60245-1_13.

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Hüwel, Jan David, Florian Haselbeck, Dominik G. Grimm, and Christian Beecks. "Dynamically Self-adjusting Gaussian Processes for Data Stream Modelling." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15791-2_10.

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AbstractOne of the major challenges in time series analysis are changing data distributions, especially when processing data streams. To ensure an up-to-date model delivering useful predictions at all times, model reconfigurations are required to adapt to such evolving streams. For Gaussian processes, this might require the adaptation of the internal kernel expression. In this paper, we present dynamically self-adjusting Gaussian processes by introducing Event-Triggered Kernel Adjustments in Gaussian process modelling (ETKA), a novel data stream modelling algorithm that can handle evolving and
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Sanghavi, Jay, Devshree Jadeja, Veerangi Mehta, Abhi Vakil, Jahnavi Lalwani, and Manan Shah. "Online Stream Processing and Multimedia-Oriented IoT: Tools for Sustainable Development of Smart Cities." In Studies in Big Data. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0924-5_10.

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Liu, Xuejun, Hongbing Xu, Yisheng Dong, Yongli Wang, and Jiangbo Qian. "Dynamically Mining Frequent Patterns over Online Data Streams." In Parallel and Distributed Processing and Applications. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11576235_65.

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Burattin, Andrea, Hugo A. López, and Lasse Starklit. "Uncovering Change: A Streaming Approach for Declarative Processes." In Lecture Notes in Business Information Processing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27815-0_12.

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AbstractProcess discovery is a family of techniques that helps to comprehend processes from their data footprints. Yet, as processes change over time so should their corresponding models, and failure to do so will lead to models that under- or over-approximate behaviour. We present a discovery algorithm that extracts declarative processes as Dynamic Condition Response (DCR) graphs from event streams. Streams are monitored to generate temporal representations of the process, later processed to create declarative models. We validated the technique by identifying drifts in a publicly available da
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Le-Phuoc, Danh, and Manfred Hauswirth. "Semantic Stream Processing." In Encyclopedia of Big Data Technologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_287-1.

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Le-Phuoc, Danh, and Manfred Hauswirth. "Semantic Stream Processing." In Encyclopedia of Big Data Technologies. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-77525-8_287.

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Le-Phuoc, Danh, Josiane Xavier Parreira, and Manfred Hauswirth. "Linked Stream Data Processing." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33158-9_7.

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Ghesmoune, Mohammed, Hanene Azzag, and Mustapha Lebbah. "G-Stream: Growing Neural Gas over Data Stream." In Neural Information Processing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12637-1_26.

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Conference papers on the topic "Online data stream processing"

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Du, Xiaorui, Andrea Piccione, Adriano Pimpini, Stefano Bortoli, Alessandro Pellegrini, and Alois Knoll. "Online Analytics with Local Operator Rebinding for Simulation Data Stream Processing." In 2024 28th International Symposium on Distributed Simulation and Real Time Applications (DS-RT). IEEE, 2024. https://doi.org/10.1109/ds-rt62209.2024.00019.

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Hozhabr Pour, Hawzhin, Gabriela Ciortuz, André Lüers, and Sebastian Fudickar. "Performance Analysis of a Data Stream Processing System for Online Activity Classification via Wearable Sensor Data." In 18th International Conference on Health Informatics. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013166100003911.

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Gao, Yu, Wangyang Yu, and Huaiping Jin. "A meta-learning-based online adaptive fine-tuning VAE for industrial process data streams." In Sixteenth International Conference on Signal Processing Systems (ICSPS 2024), edited by Robert Minasian and Li Chai. SPIE, 2025. https://doi.org/10.1117/12.3061994.

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Li, Tuo, and Lei Wang. "Key Technology of Online Auditing Data Stream Processing." In 2015 IEEE 12th Intl. Conf. on Ubiquitous Intelligence and Computing, 2015 IEEE 12th Intl. Conf. on Autonomic and Trusted Computing and 2015 IEEE 15th Intl. Conf. on Scalable Computing and Communications and its Associated Workshops (UIC-ATC-ScalCom). IEEE, 2015. http://dx.doi.org/10.1109/uic-atc-scalcom-cbdcom-iop.2015.156.

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Heinze, Thomas, Lars Roediger, Andreas Meister, Yuanzhen Ji, Zbigniew Jerzak, and Christof Fetzer. "Online parameter optimization for elastic data stream processing." In SoCC '15: ACM Symposium on Cloud Computing. ACM, 2015. http://dx.doi.org/10.1145/2806777.2806847.

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Gu, Xiaohui, Spiros Papadimitriou, Philip S. Yu, and Shu-Ping Chang. "Online Failure Forecast for Fault-Tolerant Data Stream Processing." In 2008 IEEE 24th International Conference on Data Engineering (ICDE 2008). IEEE, 2008. http://dx.doi.org/10.1109/icde.2008.4497565.

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Lyubchyk, Leonid, and Galyna Grinberg. "Online Ranking Learning on Clusters." In 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2018. http://dx.doi.org/10.1109/dsmp.2018.8478520.

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Lyubchyk, Leonid, and Olga Kostiuk. "Online Reduced-Order Kernel Regression for Data Processing in Sensor Network." In 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2020. http://dx.doi.org/10.1109/dsmp47368.2020.9204245.

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Deineko, Anastasiia O., Polina Ye Zhernova, Boris Gordon, Oleksandr O. Zayika, Iryna Pliss, and Nelya Pabyrivska. "Data Stream Online Clustering Based on Fuzzy Expectation-Maximization Approach." In 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2018. http://dx.doi.org/10.1109/dsmp.2018.8478517.

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Perova, Iryna, Iryna Pliss, Gennadiy Churyumov, Franklin M. Eze, and Samer Mohamed Kanaan Mahmoud. "Neo-fuzzy approach for medical diagnostics tasks in online-mode." In 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2016. http://dx.doi.org/10.1109/dsmp.2016.7583502.

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Reports on the topic "Online data stream processing"

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Simmons, Trey. Data quality standards for monitoring stream ecosystems in the Central Alaska Network. National Park Service, 2017. http://dx.doi.org/10.36967/2244207.

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The purpose of this report is to document the standards used by the Central Alaska Inventory and Monitoring Network (CAKN) for activities related to the collection, processing, storage, analysis, and publication of monitoring data as described in the CAKN Stream Monitoring Protocol (Simmons 2017a). The policies and procedures documented in this quality-assurance plan for activities complement the quality-assurance plans for other monitoring activities conducted by the CAKN. The plan also serves as a guide for all CAKN personnel who are involved in protocol/program activities and as a resource
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Starkey, Eric, Jacob McDonald, and Wendy Wright. Monitoring wadeable stream habitat conditions in Southeast Coast Network parks: Data quality standards. National Park Service, 2018. https://doi.org/10.36967/2254651.

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The purpose of this report is to document the standards used by the Southeast Coast Network (SECN) for activities related to the collection, processing, storage, analysis, and publication of monitoring data as described in the Monitoring Wadeable Stream Habitat Conditions in Southeast Coast Network Parks: Protocol Narrative (McDonald et al. 2018). This plan also serves as a guide for all Southeast Coast Network personnel who are involved in protocol or program activities and serves as a resource for identifying memoranda, publications, and other literature that describe associated techniques a
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Stonaha, P. Development of a Data Acquisition Program for the Purpose of Monitoring Processing Statistics Throughout the BaBar Online Computing Infrastructure's Farm Machines. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/833112.

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Chepeliev, Maksym. The GTAP 10A Data Base with Agricultural Production Targeting Based on the Food and Agricultural Organization (FAO) Data. GTAP Research Memoranda, 2020. http://dx.doi.org/10.21642/gtap.rm35.

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This document describes a new source of inputs, based on FAO data, that allows us to estimate agricultural output targets on 133 regions of the GTAP 10A Data Base. This approach allows to overcome several limitations present under the current agricultural production targeting (APT) processing. First, a significant expansion in the regional coverage is achieved, as the number of regions undergoing APT more than doubles. Second, the detailed commodity classification of the FAO dataset allows for a more accurate mapping to the GTAP Data Base sectors. Third, a better commodity coverage in the FAO
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Falfushynska, Halina I., Bogdan B. Buyak, Hryhorii V. Tereshchuk, Grygoriy M. Torbin, and Mykhailo M. Kasianchuk. Strengthening of e-learning at the leading Ukrainian pedagogical universities in the time of COVID-19 pandemic. [б. в.], 2021. http://dx.doi.org/10.31812/123456789/4442.

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Distance education has become the mandatory component of higher education establishments all over the world including Ukraine regarding COVID-19 lockdown and intentions of Universities to render valuable knowledge and provide safe educational experience for students. The present study aimed to explore the student’s and academic staff’s attitude towards e-learning and the most complicated challenges regarding online learning and distance education. Our findings disclosed that the online learning using Zoom, Moodle, Google Meet, BigBlueButton and Cisco has become quite popular among the students
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Lawley, C. J. M., P. Giddy, L. Katz, et al. Canada geological map compilation. Natural Resources Canada/CMSS/Information Management, 2024. http://dx.doi.org/10.4095/pf995j5tgu.

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The Canada Geological Map Compilation (CGMC) is a database of previously published bedrock geological maps sourced from provincial, territorial, and other geological survey organizations. The geoscientific information included within these source geological maps was standardized, translated to English, and combined to provide complete coverage of Canada and support a range of down-stream machine learning applications. Detailed lithological, mineralogical, metamorphic, lithostratigraphic, and lithodemic information was not previously available as one national-scale product. The source map data
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Lawley, C. J. M., P. Giddy, L. Katz, et al. Canada geological map compilation. Natural Resources Canada/CMSS/Information Management, 2024. http://dx.doi.org/10.4095/332596.

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The Canada Geological Map Compilation (CGMC) is a database of previously published bedrock geological maps sourced from provincial, territorial, and other geological survey organizations. The geoscientific information included within these source geological maps was standardized, translated to English, and combined to provide complete coverage of Canada and support a range of down-stream machine learning applications. Detailed lithological, mineralogical, metamorphic, lithostratigraphic, and lithodemic information was not previously available as one national-scale product. The source map data
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8

Apiyo, Eric, Zita Ekeocha, Stephen Robert Byrn, and Kari L. Clase. Improving Pharmacovigilliance Quality Management System in the Pharmacy and Poisions Board of Kenya. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317444.

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The purpose of this study was to explore ways of improving the pharmacovigilance quality system employed by the Pharmacy and Poisons Board of Kenya. The Pharmacy and Poisons Board of Kenya employs a hybrid system of pharmacovigilance that utilizes an online system of reporting pharmacovigilance incidences and a physical system, where a yellow book is physically filled by the healthcare worker and sent to the Pharmacy and Poisons Board for onward processing. This system, even though it has been relatively effective compared to other systems employed in Africa, has one major flaw. It is a slow a
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George. PR-015-08610-R01 Laboratory Conformation of the Effect of Methanol on Gas Chromatograph Performance. Pipeline Research Council International, Inc. (PRCI), 2010. http://dx.doi.org/10.55274/r0010717.

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In natural gas production and processing applications, methanol is commonly injected into natural gas streams containing water to prevent the formation of hydrates in gas lines and subsequent equipment damage. However, gas chromatographs (GCs) at field sites are typically not equipped to identify or measure methanol, and unless excess methanol is expected to carry over into a gas stream, samples sent to a laboratory are not likely to be analyzed for methanol. As a result, the potential exists for errors in gas property determination, particularly in heating value and sound speed. A previous PR
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Wolf, Eva. Chemikalienmanagement in der textilen Lieferkette. Sonderforschungsgruppe Institutionenanalyse, 2022. http://dx.doi.org/10.46850/sofia.9783941627987.

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The World Summit on Sustainable Development in Johannesburg in 2002 set the goal of minimising the adverse impacts of chemicals and waste by 2020. This goal has not been achieved yet. Therefore, other approaches are needed to prevent, minimise, or replace harmful substances. One possible approach is this master thesis which deals with the challenges that the textile importer DELTEX is facing with regard to a transparent communication of chemicals used and contained in the product in its supply chain. DELTEX is bound by legal regulations and requirements of its customer and must ensure that the
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