Relevant bibliographies by topics / Analytics Computing

Contents

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

Academic literature on the topic 'Analytics Computing'

Author: Grafiati
Published: 5 June 2025
Last updated: 24 June 2025

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Journal articles on the topic "Analytics Computing"

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Sabbani, Goutham. "Big Data Analytics in Cloud Computing." International Journal of Science and Research (IJSR) 13, no. 6 (2024): 359–63. http://dx.doi.org/10.21275/sr24604002336.

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Papageorgiou, Nikos, Yiannis Verginadis, Dimitris Apostolou, and Gregoris Mentzas. "Fog computing context analytics." IEEE Instrumentation & Measurement Magazine 22, no. 6 (2019): 53–59. http://dx.doi.org/10.1109/mim.2019.8917904.

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KIM, JENNIFER, DAVID A. OSTROWSKI, HIROSHI YAMAGUCHI, and PHILLIP C. Y. SHEU. "SEMANTIC COMPUTING AND BUSINESS INTELLIGENCE." International Journal of Semantic Computing 07, no. 01 (2013): 87–117. http://dx.doi.org/10.1142/s1793351x13500013.

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With rapidly expanding data collections becoming increasingly available, the application of Semantic Computing has become imperative to leverage this resource for industrial applications. This paper presents a survey of Semantic Computing in the area of Business Intelligence. We examine semantic analytical techniques and tools as applied for prediction analysis and decision support. We also define the role of Semantic Computing as applied in the context of Data Mining, Text Mining and Big Data Analytics. Additionally, we describe how business data is queried with Structured Natural Language as well as the use of On-Line Analytic Processing.
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S Pathak, Shantanu, and D. Rajeswara Rao. "Reservoir Computing for Healthcare Analytics." International Journal of Engineering & Technology 7, no. 2.32 (2018): 240. http://dx.doi.org/10.14419/ijet.v7i2.32.15576.

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In this data age tools for sophisticated generation and handling of data are at epitome of usage. Data varying in both space and time poses a breed of challenges. Challenges they possess for forecasting can be well handled by Reservoir computing based neural networks. Challenges like class imbalance, missing values, locality effect are discussed here. Additionally, popular statistical techniques for forecasting such data are discussed. Results show how Reservoir Computing based technique outper-forms traditional neural networks.
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Brahmaji Kanagarla, Krishna Prasanth. "Quantum Computing For Data Analytics." International Journal of All Research Education and Scientific Methods 11, no. 05 (2023): 3389–94. http://dx.doi.org/10.56025/ijaresm.2024.1105233389.

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The focus of the study lies in the transformative power of quantum computing with regard to finance, health and artificial intelligence. New algorithms enabled but quantum allow for previously unattainable speeds in complex processing. The current work is gone into references of some of these quantum algorithms among those put forward by Grover and Shor besides the assessment of the impact of each in enhancing analytics. Major challenges in term of practical implementation in real applications such as scalability and error correction-are reviewed. Future directions of research would then indicate refinement in such a way that these systems can ensure reliability and industry integration. The research identifies the heights of promise quantum computing holds for innovation to change the face of data analytics as a leading force.
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Earley, Seth. "Cognitive Computing, Analytics, and Personalization." IT Professional 17, no. 4 (2015): 12–18. http://dx.doi.org/10.1109/mitp.2015.55.

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Aafreen, Qureshi, and Gaurav Indra Dr. "Machine Learning Driven Edge Analytics for Healthcare: Problems, Difficulties, Future Directions, and Applications-A Review." International Journal of Innovative Science and Research Technology 7, no. 12 (2023): 1427–41. https://doi.org/10.5281/zenodo.7525315.

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With the use of edge technology, cloud resources (particularly computing, storage, and network) will be made available in close proximity to edge devices, or smart gadgets where data is generated and consumed. Edge computing and edge analytics are two new ideas in edge technology that have emerged as a result of computer and application integration in edge devices. To examine the information produced through edge gadgets, edge analytics employs a number of methods or algorithms. The development of edge analytics has made the edge gadgets a whole set. Edge analytics is currently unable to fully accomodate the analytic methodologies. Due to several limitations like a low power supply, a tiny memory, a lack of resources, etc., the edge gadgets cannot conduct complex and refined analytic algorithms. The purpose of this paper is to give a thorough explanation of edge analytics. The following are the paper's main contributions: a detailed description of the differences among the three edge technology ideas of edge gadgets, edge computing, and edge analytics, as well as their problems. The article also examines how edge analytics are being used in numerous industries, including retail, agriculture, industry, and healthcare, to solve a variety of issues. Additionally, the research papers based on cutting-edge analytics are thoroughly examined in this article to analyse the current problems, new difficulties, research prospects, as well as utilizations.
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Kushwaha, Ashok, and Dr Kalyan Acharya. "Big Data Analytics in Cloud Computing for Scientific Analytics." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 1713–17. http://dx.doi.org/10.22214/ijraset.2022.42636.

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Abstract: Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions. Keywords: Big data, Analytics, Hadoop, Healthcare, Framework, Methodology.
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Oluwole Temidayo Modupe, Aanuoluwapo Ayodeji Otitoola, Oluwatayo Jacob Oladapo, et al. "REVIEWING THE TRANSFORMATIONAL IMPACT OF EDGE COMPUTING ON REAL-TIME DATA PROCESSING AND ANALYTICS." Computer Science & IT Research Journal 5, no. 3 (2024): 693–702. http://dx.doi.org/10.51594/csitrj.v5i3.929.

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Edge computing has emerged as a pivotal paradigm shift in the realm of data processing and analytics, revolutionizing the way organizations handle real-time data. This review presents a comprehensive review of the transformational impact of edge computing on real-time data processing and analytics. Firstly, the review delves into the fundamental concepts of edge computing, elucidating its architectural framework and highlighting its distinct advantages over traditional cloud-centric approaches. By distributing computational resources closer to data sources, edge computing mitigates latency issues and enhances responsiveness, thereby enabling real-time data processing at the edge. Furthermore, this review explores how edge computing facilitates the seamless integration of analytics capabilities into edge devices, empowering organizations to derive actionable insights at the source of data generation. Leveraging advanced analytics algorithms, such as machine learning and artificial intelligence, edge computing enables autonomous decision-making and predictive analytics in real time, fostering innovation across diverse industry verticals. Moreover, the review examines the transformative implications of edge computing on various sectors, including healthcare, manufacturing, transportation, and smart cities. By enabling localized data processing and analytics, edge computing enhances operational efficiency, ensures data privacy and security, and unlocks new opportunities for business optimization and value creation. This review underscores the profound impact of edge computing on real-time data processing and analytics, revolutionizing the way organizations harness data to drive informed decision-making and gain competitive advantage in today's dynamic business landscape. As edge computing continues to evolve, its transformative potential is poised to redefine the future of data-driven innovation and digital transformation.
 Keywords: Edge, Computing, Analytics, Data, Impact, Review.
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Singh, Kiran Deep, Prabh Deep Singh, Rohan Verma, and Harsh Taneja. "Optimizing health data analytics in fog computing using hyperparameter tuning and grid search." Journal of Information and Optimization Sciences 45, no. 2 (2024): 429–38. http://dx.doi.org/10.47974/jios-1560.

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The integration of fog computing with health data analytics signifies a paradigm shift in the field of healthcare, offering the potential for streamlined and prompt analysis of patient welfare. The increasing volume of health data necessitates the development of efficient analytical models in fog computing settings. The objective of this research is to examine the integration of fog computing and health data analytics, specifically emphasizing the utilization of hyperparameter tuning and grid search techniques to enhance optimization approaches. Hyperparameter tuning and grid search are two techniques utilized in machine learning to optimize the performance of models. These methods are employed in the context of health data analytics inside fog computing with the objective of improving accuracy, reducing latency, and enhancing resource efficiency. Our research endeavors to provide significant contributions to the advancement of adaptable and responsive healthcare systems, therefore promoting enhanced patient outcomes in the era of data-driven decision-making.
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Dissertations / Theses on the topic "Analytics Computing"

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Singh, Vivek Kumar. "Essays on Cloud Computing Analytics." Scholar Commons, 2019. https://scholarcommons.usf.edu/etd/7943.

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This dissertation research focuses on two key aspects of cloud computing research – pricing and security using data-driven techniques such as deep learning and econometrics. The first dissertation essay (Chapter 1) examines the adoption of spot market in cloud computing and builds IT investment estimation models for organizations adopting cloud spot market. The second dissertation essay (Chapter 2 and 3) studies proactive threat detection and prediction in cloud computing. The final dissertation essay (Chapter 4) develops a secured cloud files system which protects organizations using cloud computing in accidental data leaks.
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Chakrabarti, Aniket. "Scaling Analytics via Approximate and Distributed Computing." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500473400586782.

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Le, Quoc Do. "Approximate Data Analytics Systems." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-234219.

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Today, most modern online services make use of big data analytics systems to extract useful information from the raw digital data. The data normally arrives as a continuous data stream at a high speed and in huge volumes. The cost of handling this massive data can be significant. Providing interactive latency in processing the data is often impractical due to the fact that the data is growing exponentially and even faster than Moore’s law predictions. To overcome this problem, approximate computing has recently emerged as a promising solution. Approximate computing is based on the observation that many modern applications are amenable to an approximate, rather than the exact output. Unlike traditional computing, approximate computing tolerates lower accuracy to achieve lower latency by computing over a partial subset instead of the entire input data. Unfortunately, the advancements in approximate computing are primarily geared towards batch analytics and cannot provide low-latency guarantees in the context of stream processing, where new data continuously arrives as an unbounded stream. In this thesis, we design and implement approximate computing techniques for processing and interacting with high-speed and large-scale stream data to achieve low latency and efficient utilization of resources. To achieve these goals, we have designed and built the following approximate data analytics systems: • StreamApprox—a data stream analytics system for approximate computing. This system supports approximate computing for low-latency stream analytics in a transparent way and has an ability to adapt to rapid fluctuations of input data streams. In this system, we designed an online adaptive stratified reservoir sampling algorithm to produce approximate output with bounded error. • IncApprox—a data analytics system for incremental approximate computing. This system adopts approximate and incremental computing in stream processing to achieve high-throughput and low-latency with efficient resource utilization. In this system, we designed an online stratified sampling algorithm that uses self-adjusting computation to produce an incrementally updated approximate output with bounded error. • PrivApprox—a data stream analytics system for privacy-preserving and approximate computing. This system supports high utility and low-latency data analytics and preserves user’s privacy at the same time. The system is based on the combination of privacy-preserving data analytics and approximate computing. • ApproxJoin—an approximate distributed joins system. This system improves the performance of joins — critical but expensive operations in big data systems. In this system, we employed a sketching technique (Bloom filter) to avoid shuffling non-joinable data items through the network as well as proposed a novel sampling mechanism that executes during the join to obtain an unbiased representative sample of the join output. Our evaluation based on micro-benchmarks and real world case studies shows that these systems can achieve significant performance speedup compared to state-of-the-art systems by tolerating negligible accuracy loss of the analytics output. In addition, our systems allow users to systematically make a trade-off between accuracy and throughput/latency and require no/minor modifications to the existing applications.
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Katzenbach, Alfred, and Holger Frielingsdorf. "Big Data Analytics für die Produktentwicklung." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-214517.

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Aus der Einleitung: "Auf der Hannovermesse 2011 wurde zum ersten Mal der Begriff "Industrie 4.0" der Öffentlichkeit bekannt gemacht. Die Akademie der Technikwissenschaften hat in einer Arbeitsgruppe diese Grundidee der vierten Revolution der Industrieproduktion weiterbearbeitet und 2013 in einem Abschlussbericht mit dem Titel „Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0“ veröffentlicht (BmBF, 2013). Die Grundidee besteht darin, wandlungsfähige und effiziente Fabriken unter Nutzung moderner Informationstechnologie zu entwickeln. Basistechnologien für die Umsetzung der intelligenten Fabriken sind: — Cyber-Physical Systems (CPS) — Internet of Things (IoT) und Internet of Services (IoS) — Big Data Analytics and Prediction — Social Media — Mobile Computing Der Abschlussbericht fokussiert den Wertschöpfungsschritt der Produktion, während die Fragen der Produktentwicklung weitgehend unberücksichtigt geblieben sind. Die intelligente Fabrik zur Herstellung intelligenter Produkte setzt aber auch die Weiterentwicklung der Produktentwicklungsmethoden voraus. Auch hier gibt es einen großen Handlungsbedarf, der sehr stark mit den Methoden des „Modellbasierten Systems-Engineering“ einhergeht. ..."
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Flatt, Taylor. "CrowdCloud: Combining Crowdsourcing with Cloud Computing for SLO Driven Big Data Analysis." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/theses/2234.

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The evolution of structured data from simple rows and columns on a spreadsheet to more complex unstructured data such as tweets, videos, voice, and others, has resulted in a need for more adaptive analytical platforms. It is estimated that upwards of 80% of data on the Internet today is unstructured. There is a drastic need for crowdsourcing platforms to perform better in the wake of the tsunami of data. We investigated the employment of a monitoring service which would allow the system take corrective action in the event the results were trending in away from meeting the accuracy, budget, and time SLOs. Initial implementation and system validation has shown that taking corrective action generally leads to a better success rate of reaching the SLOs. Having a system which can dynamically adjust internal parameters in order to perform better can lead to more harmonious interactions between humans and machine algorithms and lead to more efficient use of resources.
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Rossi, Tisbeni Simone. "Big data analytics towards predictive maintenance at the INFN-CNAF computing centre." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18430/.

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La Fisica delle Alte Energie (HEP) è da lungo tra i precursori nel gestire e processare enormi dataset scientifici e nell'operare alcuni tra i più grandi data centre per applicazioni scientifiche. HEP ha sviluppato una griglia computazionale (Grid) per il calcolo al Large Hadron Collider (LHC) del CERN di Ginevra, che attualmente coordina giornalmente le operazioni di calcolo su oltre 800k processori in 170 centri di calcolo e gestendo mezzo Exabyte di dati su disco distribuito in 5 continenti. Nelle prossime fasi di LHC, soprattutto in vista di Run-4, il quantitativo di dati gestiti dai centri di calcolo aumenterà notevolmente. In questo contesto, la HEP Software Foundation ha redatto un Community White Paper (CWP) che indica il percorso da seguire nell'evoluzione del software moderno e dei modelli di calcolo in preparazione alla fase cosiddetta di High Luminosity di LHC. Questo lavoro ha individuato in tecniche di Big Data Analytics un enorme potenziale per affrontare le sfide future di HEP. Uno degli sviluppi riguarda la cosiddetta Operation Intelligence, ovvero la ricerca di un aumento nel livello di automazione all'interno dei workflow. Questo genere di approcci potrebbe portare al passaggio da un sistema di manutenzione reattiva ad uno, più evoluto, di manutenzione predittiva o addirittura prescrittiva. La tesi presenta il lavoro fatto in collaborazione con il centro di calcolo dell'INFN-CNAF per introdurre un sistema di ingestione, organizzazione e processing dei log del centro su una piattaforma di Big Data Analytics unificata, al fine di prototipizzare un modello di manutenzione predittiva per il centro. Questa tesi contribuisce a tale progetto con lo sviluppo di un algoritmo di clustering dei messaggi di log basato su misure di similarità tra campi testuali, per superare il limite connesso alla verbosità ed eterogeneità dei log raccolti dai vari servizi operativi 24/7 al centro.
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Parikh, Nidhi Kiranbhai. "Behavior Modeling and Analytics for Urban Computing: A Synthetic Information-based Approach." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/84967.

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The rapid increase in urbanization poses challenges in diverse areas such as energy, transportation, pandemic planning, and disaster response. Planning for urbanization is a big challenge because cities are complex systems consisting of human populations, infrastructures, and interactions and interdependence among them. This dissertation focuses on a synthetic information-based approach for modeling human activities and behaviors for two urban science applications, epidemiology and disaster planning, and with associated analytics. Synthetic information is a data-driven approach to create a detailed, high fidelity representation of human populations, infrastructural systems and their behavioral and interaction aspects. It is used in developing large-scale simulations to model what-if scenarios and for policy making. Big cities have a large number of visitors visiting them every day. They often visit crowded areas in the city and come into contact with each other and the area residents. However, most epidemiological studies have ignored their role in spreading epidemics. We extend the synthetic population model of the Washington DC metro area to include transient populations, consisting of tourists and business travelers, along with their demographics and activities, by combining data from multiple sources. We evaluate the effect of including this population in epidemic forecasts, and the potential benefits of multiple interventions that target transients. In the next study, we model human behavior in the aftermath of the detonation of an improvised nuclear device in Washington DC. Previous studies of this scenario have mostly focused on modeling physical impact and simple behaviors like sheltering and evacuation. However, these models have focused on optimal behavior, not naturalistic behavior. In other words, prior work is focused on whether it is better to shelter-in-place or evacuate, but has not been informed by the literature on what people actually do in the aftermath of disasters. Natural human behaviors in disasters, such as looking for family members or seeking healthcare, are supported by infrastructures such as cell-phone communication and transportation systems. We model a range of behaviors such as looking for family members, evacuation, sheltering, healthcare-seeking, worry, and search and rescue and their interactions with infrastructural systems. Large-scale and complex agent-based simulations generate a large amount of data in each run of the simulation, making it hard to make sense of results. This leads us to formulate two new problems in simulation analytics. First, we develop algorithms to summarize simulation results by extracting causally-relevant state sequences - state sequences that have a measurable effect on the outcome of interest. Second, in order to develop effective interventions, it is important to understand which behaviors lead to positive and negative outcomes. It may happen that the same behavior may lead to different outcomes, depending upon the context. Hence, we develop an algorithm for contextual behavior ranking. In addition to the context mentioned in the query, our algorithm also identifies any additional context that may affect the behavioral ranking.<br>Ph. D.
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Worthy, William Tuley. "Aligning Social Media, Mobile, Analytics, and Cloud Computing Technologies and Disaster Response." ScholarWorks, 2018. https://scholarworks.waldenu.edu/dissertations/4696.

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After nearly 2 decades of advances in information and communications technologies (ICT) including social media, mobile, analytics, and cloud computing, disaster response agencies in the United States have not been able to improve alignment between ICT-based information and disaster response actions. This grounded theory study explored emergency response ICT managers' understanding of how social media, mobile, analytics, and cloud computing technologies (SMAC) are related to and can inform disaster response strategies. Sociotechnical theory served as the conceptual framework to ground the study. Data were collected from document reviews and semistructured interviews with 9 ICT managers from emergency management agencies in the state of Hawaii who had experience in responding to major disasters. The data were analyzed using open, axial coding, and selective coding. Three elements of a theory emerged from the findings: (a) the ICT managers were hesitant about SMAC technologies replacing first responder's radios to interoperate between emergency response agencies during major disasters, (b) the ICT managers were receptive to converging conventional ICT with SMAC technologies, and (c) the ICT managers were receptive to joining legacy information sharing strategies with new information sharing strategies based on SMAC technologies. The emergent theory offers a framework for aligning SMAC technologies and disaster response strategies. The implications for positive social change include reduced interoperability failures between disaster agencies during major catastrophes, which may lower the risk of casualties and deaths to emergency responders and disaster victims, thus benefiting them and their communities.
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Panneerselvam, John. "A prescriptive analytics approach for energy efficiency in datacentres." Thesis, University of Derby, 2018. http://hdl.handle.net/10545/622460.

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Given the evolution of Cloud Computing in recent years, users and clients adopting Cloud Computing for both personal and business needs have increased at an unprecedented scale. This has naturally led to the increased deployments and implementations of Cloud datacentres across the globe. As a consequence of this increasing adoption of Cloud Computing, Cloud datacentres are witnessed to be massive energy consumers and environmental polluters. Whilst the energy implications of Cloud datacentres are being addressed from various research perspectives, predicting the future trend and behaviours of workloads at the datacentres thereby reducing the active server resources is one particular dimension of green computing gaining the interests of researchers and Cloud providers. However, this includes various practical and analytical challenges imposed by the increased dynamism of Cloud systems. The behavioural characteristics of Cloud workloads and users are still not perfectly clear which restrains the reliability of the prediction accuracy of existing research works in this context. To this end, this thesis presents a comprehensive descriptive analytics of Cloud workload and user behaviours, uncovering the cause and energy related implications of Cloud Computing. Furthermore, the characteristics of Cloud workloads and users including latency levels, job heterogeneity, user dynamicity, straggling task behaviours, energy implications of stragglers, job execution and termination patterns and the inherent periodicity among Cloud workload and user behaviours have been empirically presented. Driven by descriptive analytics, a novel user behaviour forecasting framework has been developed, aimed at a tri-fold forecast of user behaviours including the session duration of users, anticipated number of submissions and the arrival trend of the incoming workloads. Furthermore, a novel resource optimisation framework has been proposed to avail the most optimum level of resources for executing jobs with reduced server energy expenditures and job terminations. This optimisation framework encompasses a resource estimation module to predict the anticipated resource consumption level for the arrived jobs and a classification module to classify tasks based on their resource intensiveness. Both the proposed frameworks have been verified theoretically and tested experimentally based on Google Cloud trace logs. Experimental analysis demonstrates the effectiveness of the proposed framework in terms of the achieved reliability of the forecast results and in reducing the server energy expenditures spent towards executing jobs at the datacentres.
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Spruth, Wilhelm G. "Enterprise Computing." Universitätsbibliothek Leipzig, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-126859.

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Das vorliegende Buch entstand aus einer zweisemestrigen Vorlesung „Enterprise Computing“, die wir gemeinsam über viele Jahre als Teil des Bachelor- oder Master-Studienganges an der Universität Leipzig gehalten haben. Das Buch führt ein in die Welt des Mainframe und soll dem Leser einen einführenden Überblick geben. Band 1 ist der Einführung in z/OS gewidmet, während sich Band 2 mit der Internet Integration beschäftigt. Ergänzend werden in Band 3 praktische Übungen unter z/OS dargestellt.
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Books on the topic "Analytics Computing"

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Sharma, Rajnish, Archana Mantri, and Sumeet Dua, eds. Computing, Analytics and Networks. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0755-3.

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Nayak, Janmenjoy, Ajith Abraham, B. Murali Krishna, G. T. Chandra Sekhar, and Asit Kumar Das, eds. Soft Computing in Data Analytics. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-0514-6.

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International Business Machines Corporation. International Technical Support Organization, ed. IBM smart analytics cloud. IBM, International Technical Support Organization, 2010.

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Das, Himansu, Prasant Kumar Pattnaik, Siddharth Swarup Rautaray, and Kuan-Ching Li, eds. Progress in Computing, Analytics and Networking. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2414-1.

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Pattnaik, Prasant Kumar, Siddharth Swarup Rautaray, Himansu Das, and Janmenjoy Nayak, eds. Progress in Computing, Analytics and Networking. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7871-2.

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Hurwitz, Judith, Marcia Kaufman, and Adrian Bowles, eds. Cognitive Computing and Big Data Analytics. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119183648.

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Mazumder, Sourav, Robin Singh Bhadoria, and Ganesh Chandra Deka, eds. Distributed Computing in Big Data Analytics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59834-5.

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Trovati, Marcello, Richard Hill, Ashiq Anjum, Shao Ying Zhu, and Lu Liu, eds. Big-Data Analytics and Cloud Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25313-8.

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Karthikeyan, P., Sathish Kumar, and V. Anbarasu, eds. Drone Data Analytics in Aerial Computing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5056-0.

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Patil, Mukesh, Vishwesh Vyawahare, and Gajanan Birajdar, eds. Intelligent Computing and Big Data Analytics. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-74701-4.

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Book chapters on the topic "Analytics Computing"

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Matter, Ulrich. "Cloud Computing." In Big Data Analytics. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003378822-7.

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Matter, Ulrich. "Hardware: Computing Resources." In Big Data Analytics. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003378822-5.

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Bolder, David Jamieson. "Computing Exposures." In Fixed-Income Portfolio Analytics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12667-8_2.

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Bharati, Taran Singh. "Cloud Computing." In AI-Based Data Analytics. Auerbach Publications, 2023. http://dx.doi.org/10.1201/9781032614083-12.

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Gupta, Rahul, and Archana Singh. "Edge Computing in Smart Villages." In Asset Analytics. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3643-4_13.

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Falsafi, Babak, Samuel Midkiff, JackB Dennis, et al. "Deep Analytics." In Encyclopedia of Parallel Computing. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_2412.

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Falsafi, Babak, Samuel Midkiff, JackB Dennis, et al. "Data Analytics." In Encyclopedia of Parallel Computing. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_2419.

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Singh, Vaishali, and S. K. Pandey. "Cloud Computing: Vulnerability and Threat Indications." In Asset Analytics. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8253-6_2.

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Sehgal, Naresh Kumar, and Pramod Chandra P. Bhatt. "Analytics in the Cloud." In Cloud Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77839-6_11.

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Annansingh, Fenio, and Joseph Bon Sesay. "Customer Relationship Analytics, Cloud Computing, Blockchain, and Cognitive Computing." In Data Analytics for Business. Routledge, 2022. http://dx.doi.org/10.4324/9781003129356-5.

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Conference papers on the topic "Analytics Computing"

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Scorzato, Luigi. "Responsible Analytics." In European network for Particle physics, Lattice field theory and Extreme computing. Sissa Medialab, 2024. http://dx.doi.org/10.22323/1.451.0023.

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M, Nirmala, Prajwal D. A, K. Sai Dinesh, Bibiana Jennifer J, G. Vidya Shankar, and B. Bharath. "CureIQ: FutureCare Analytics." In 2025 6th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI). IEEE, 2025. https://doi.org/10.1109/icmcsi64620.2025.10883157.

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Gupta, Shubham, Ishwar Bansal, and Geeta Geeta. "Leveraging Neuromorphic Computing for Efficient and Scalable Data Analytics." In 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT). IEEE, 2025. https://doi.org/10.1109/incacct65424.2025.11011365.

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Maheshwari, Prakhar, Alankar Singhal, and Mohammed A. Qadeer. "Data analytics using cloud computing." In 2017 9th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2017. http://dx.doi.org/10.1109/cicn.2017.8319361.

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Das, Anup. "Neuromorphic Computing for Graph Analytics." In ICCAD '24: 43rd IEEE/ACM International Conference on Computer-Aided Design. ACM, 2024. https://doi.org/10.1145/3676536.3698024.

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Olivares, Daniel. "Exploring Learning Analytics for Computing Education." In ICER '15: International Computing Education Research Conference. ACM, 2015. http://dx.doi.org/10.1145/2787622.2787746.

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Wen, Zhenyu, Do Le Quoc, Pramod Bhatotia, Ruichuan Chen, and Myungjin Lee. "ApproxIoT: Approximate Analytics for Edge Computing." In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2018. http://dx.doi.org/10.1109/icdcs.2018.00048.

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Prakash, Saurav, Amirhossein Reisizadeh, Ramtin Pedarsani, and Salman Avestimehr. "Coded Computing for Distributed Graph Analytics." In 2018 IEEE International Symposium on Information Theory (ISIT). IEEE, 2018. http://dx.doi.org/10.1109/isit.2018.8437860.

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Olivares, Daniel M., and Christopher D. Hundhausen. "Supporting learning analytics in computing education." In LAK '17: 7th International Learning Analytics and Knowledge Conference. ACM, 2017. http://dx.doi.org/10.1145/3027385.3029472.

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Perrin, Dimitri, Marija Bezbradica, Martin Crane, Heather J. Ruskin, and Christophe Duhamel. "High-Performance Computing for Data Analytics." In 2012 IEEE/ACM 16th International Symposium on Distributed Simulation and Real Time Applications (DS-RT). IEEE, 2012. http://dx.doi.org/10.1109/ds-rt.2012.41.

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Reports on the topic "Analytics Computing"

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Abell, Thomas, Husar Arndt, and May-Ann Lim. Cloud Computing as a Key Enabler for Digital Government across Asia and the Pacific. Asian Development Bank, 2021. http://dx.doi.org/10.22617/wps210196-2.

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Governments are responding to rapid change and growing demands by citizens and businesses by accelerating the digitalization of public services. They are updating their e-government capabilities, adding new digital tools and services, augmenting their data analytics capabilities, and putting in place digital economy development plans. Many of these changes are enabled by cloud computing technologies that have become commonplace in the digitally connected world. The rapidly scalable computing resources that cloud computing delivers via the internet bring cost benefits, improve agility, ensure resilience, and provide access to the latest solutions that digital technology can offer.
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Ruvinsky, Alicia, Timothy Garton, Daniel Chausse, Rajeev Agrawal, Harland Yu, and Ernest Miller. Accelerating the tactical decision process with High-Performance Computing (HPC) on the edge : motivation, framework, and use cases. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42169.

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Managing the ever-growing volume and velocity of data across the battlefield is a critical problem for warfighters. Solving this problem will require a fundamental change in how battlefield analyses are performed. A new approach to making decisions on the battlefield will eliminate data transport delays by moving the analytical capabilities closer to data sources. Decision cycles depend on the speed at which data can be captured and converted to actionable information for decision making. Real-time situational awareness is achieved by locating computational assets at the tactical edge. Accelerating the tactical decision process leverages capabilities in three technology areas: (1) High-Performance Computing (HPC), (2) Machine Learning (ML), and (3) Internet of Things (IoT). Exploiting these areas can reduce network traffic and shorten the time required to transform data into actionable information. Faster decision cycles may revolutionize battlefield operations. Presented is an overview of an artificial intelligence (AI) system design for near-real-time analytics in a tactical operational environment executing on co-located, mobile HPC hardware. The report contains the following sections, (1) an introduction describing motivation, background, and state of technology, (2) descriptions of tactical decision process leveraging HPC problem definition and use case, and (3) HPC tactical data analytics framework design enabling data to decisions.
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Pasupuleti, Murali Krishna. AI and Quantum-Nano Frontiers: Innovations in Health, Sustainability, Energy, and Security. National Education Services, 2025. https://doi.org/10.62311/nesx/rr525.

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Abstract: This research report explores transformative advancements at the intersection of Artificial Intelligence (AI), Quantum Computing, and Nanotechnology, focusing on breakthrough innovations in health, sustainability, energy, and global security. By integrating quantum algorithms, AI-driven analytics, and advanced nanomaterials, this report highlights revolutionary solutions in precision medicine, predictive diagnostics, sustainable energy storage, universal water purification, and cybersecurity. Real-world case studies and emerging technologies such as graphene-based nanomaterials, quantum-enhanced drug discovery, smart microgrids, and quantum cryptography demonstrate how interdisciplinary integration accelerates global progress. Finally, ethical frameworks, strategic recommendations, and future roadmaps are provided to guide responsible deployment of these transformative technologies for widespread societal benefit. Keywords: Artificial Intelligence, Quantum Computing, Nanotechnology, Precision Medicine, Renewable Energy, Sustainability, Graphene, Smart Microgrids, Quantum Cryptography, Cybersecurity, Neuromorphic Computing, Water Purification, Ethical Implications, Global Security, Interdisciplinary Research.
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Chaudhary, Aashish. From Data to Models and Analytics: An Integrated Scientific Computing Platform to Accelerate Environmental Research. Final Technical Report. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1508100.

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Cimene, Dr Francis Thaise A. Emerging Technological Trends and Business Process Management: Preparing the Philippines for the Future. Asian Productivity Organization, 2024. https://doi.org/10.61145/dktv2301.

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The Philippine IT-BPM sector plays a vital role in driving economic growth and global competitiveness. This mini-report highlights how emerging technologies such as cloud computing, IoT, and big data analytics are transforming traditional business processes. Grounded in endogenous growth theory, the report emphasizes the impact of innovation and human capital on productivity. Policy recommendations are provided to bolster the nation’s position as a leading outsourcing hub and prepare for future technological advancements.
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Pasupuleti, Murali Krishna. Securing AI-driven Infrastructure: Advanced Cybersecurity Frameworks for Cloud and Edge Computing Environments. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv225.

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Abstract: The rapid adoption of artificial intelligence (AI) in cloud and edge computing environments has transformed industries by enabling large-scale automation, real-time analytics, and intelligent decision-making. However, the increasing reliance on AI-powered infrastructures introduces significant cybersecurity challenges, including adversarial attacks, data privacy risks, and vulnerabilities in AI model supply chains. This research explores advanced cybersecurity frameworks tailored to protect AI-driven cloud and edge computing environments. It investigates AI-specific security threats, such as adversarial machine learning, model poisoning, and API exploitation, while analyzing AI-powered cybersecurity techniques for threat detection, anomaly prediction, and zero-trust security. The study also examines the role of cryptographic solutions, including homomorphic encryption, federated learning security, and post-quantum cryptography, in safeguarding AI models and data integrity. By integrating AI with cutting-edge cybersecurity strategies, this research aims to enhance resilience, compliance, and trust in AI-driven infrastructures. Future advancements in AI security, blockchain-based authentication, and quantum-enhanced cryptographic solutions will be critical in securing next-generation AI applications in cloud and edge environments. Keywords: AI security, adversarial machine learning, cloud computing security, edge computing security, zero-trust AI, homomorphic encryption, federated learning security, post-quantum cryptography, blockchain for AI security, AI-driven threat detection, model poisoning attacks, anomaly prediction, cyber resilience, decentralized AI security, secure multi-party computation (SMPC).
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Pasupuleti, Murali Krishna. Quantum Cognition: Modeling Decision-Making with Quantum Theory. National Education Services, 2025. https://doi.org/10.62311/nesx/rrvi225.

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Abstract Quantum cognition applies quantum probability theory and mathematical principles from quantum mechanics to model human decision-making, reasoning, and cognitive processes beyond the constraints of classical probability models. Traditional decision theories, such as expected utility theory and Bayesian inference, struggle to explain context-dependent reasoning, preference reversals, order effects, and cognitive biases observed in human behavior. By incorporating superposition, interference, and entanglement, quantum cognitive models offer a probabilistic framework that better accounts for uncertainty, ambiguity, and adaptive decision-making in complex environments. This research explores the foundations of quantum cognition, its empirical validation in behavioral experiments and neuroscience, and its applications in artificial intelligence (AI), behavioral economics, and decision sciences. Additionally, it examines how quantum-inspired AI models enhance predictive analytics, machine learning algorithms, and human-computer interaction. The study also addresses challenges related to mathematical complexity, cognitive interpretation, and the potential link between quantum mechanics and brain function, providing a comprehensive framework for the integration of quantum cognition into decision science and AI-driven cognitive computing. Keywords Quantum cognition, quantum probability, decision-making models, cognitive science, superposition in cognition, interference effects, entanglement in decision-making, probabilistic reasoning, preference reversals, cognitive biases, order effects, quantum-inspired AI, behavioral economics, neural quantum theory, artificial intelligence, cognitive neuroscience, human-computer interaction, quantum probability in psychology, quantum decision theory, uncertainty modeling, predictive analytics, quantum computing in cognition.
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Pasupuleti, Murali Krishna. AI-Driven Automation: Transforming Industry 5.0 withMachine Learning and Advanced Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rr225.

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Abstract: This article delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in shaping Industry 5.0, a paradigm centered on human- machine collaboration, sustainability, and resilient industrial ecosystems. Beginning with the evolution from Industry 4.0 to Industry 5.0, it examines core AI technologies, including predictive analytics, natural language processing, and computer vision, which drive advancements in manufacturing, quality control, and adaptive logistics. Key discussions include the integration of collaborative robots (cobots) that enhance human productivity, AI-driven sustainability practices for energy and resource efficiency, and predictive maintenance models that reduce downtime. Addressing ethical challenges, the Article highlights the importance of data privacy, unbiased algorithms, and the environmental responsibility of intelligent automation. Through case studies across manufacturing, healthcare, and energy sectors, readers gain insights into real-world applications of AI and ML, showcasing their impact on efficiency, quality, and safety. The Article concludes with future directions, emphasizing emerging technologies like quantum computing, human-machine synergy, and the sustainable vision for Industry 5.0, where intelligent automation not only drives innovation but also aligns with ethical and social values for a resilient industrial future. Keywords: Industry 5.0, intelligent automation, AI, machine learning, sustainability, human- machine collaboration, cobots, predictive maintenance, quality control, ethical AI, data privacy, Industry 4.0, computer vision, natural language processing, energy efficiency, adaptive logistics, environmental responsibility, industrial ecosystems, quantum computing.
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Malej, Matt, Fengyan Shi, Nigel Tozer, et al. FUNWAVE-TVD testbed : analytical, laboratory, and field cases for validation and verification of the phase-resolving nearshore Boussinesq-type numerical wave model. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49183.

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Over the last couple of decades, advancements in high-performance computing have allowed phase-resolving, Boussinesq-type numerical wave models to be more practical in addressing nearshore coastal wave processes. As such, the open-source Fully Nonlinear Wave model–Total Variation Diminishing (FUNWAVE-TVD) numerical wave model has become more ubiquitous across all scientific and engineering-focused R&amp;D organizations, including academic, government, and industry partners. In collaboration with the US Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory; the University of Delaware; and HR Wallingford, a robust testbed has been developed to allow users to benchmark their applications against new releases of the model. The testbed presented here includes analytical, laboratory, and field cases, to provide guidance on the operational utility of FUNWAVE-TVD and examines numerical convergence, accuracy, and performance in modeling wave generation, propagation, wave breaking, and moving shorelines in nearshore wind-wave applications. A brief discussion on the efficiency of the model across parallel computing platforms is also provided.
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Gonzalez-Lima, Maria D., Richard A. Tapia, and Florian A. Potra. On Effectively Computing the Analytic Center of the Solution Set by Primal-Dual Interior-Point Methods. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada445646.

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