Relevant bibliographies by topics / Emerging Data

Academic literature on the topic 'Emerging Data'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Emerging Data"

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Spath, Jeff. "Emerging Frontiers: Big Data!" Journal of Petroleum Technology 66, no. 01 (January 1, 2014): 14–16. http://dx.doi.org/10.2118/0114-0014-jpt.

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Chung-Sheng Li and Y. Ofek. "Emerging Data Communication Standards." IEEE Communications Magazine 34, no. 8 (August 1996): 36. http://dx.doi.org/10.1109/mcom.1996.533917.

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Forster, Kenneth, and Michael Masson. "Introduction: Emerging data analysis." Journal of Memory and Language 59, no. 4 (November 2008): 387–88. http://dx.doi.org/10.1016/j.jml.2008.08.005.

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Galpin, Ixent. "Data science: an emerging discipline." CITAS 2, no. 1 (July 1, 2016): 39–46. http://dx.doi.org/10.15332/24224529.5178.

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The role of data scientist has been described as the “sexiest job of the 21st Century”. While possibly there is a degree of hype associated with such a claim, there are factors at play such as the unprecedented growth in the amount of data being generated. This paper characterises the already established disciplines which underpin data science, viz., data engineering, statistics, and data mining. Following a characterisation of the previous fields, data science is found to be most closely related to data mining. However, in contrast to data mining, data science promises to operate over datasets that exhibit significant challenges in terms of the four Vs: Volume, Variety, Velocity and Veracity. This paper notes that the current emphasis, both in industry and academia, is on the first three Vs, which pose mainly scientific or technological challenges, rather than Veracity, which is a truly scientific (and arguably a more complex) challenge. Data Science can be seen to have a more ambitious objective than what traditionally data mining has: as a science, data science aims to lead to the creation of new theories and knowledge. This paper notes that, ironically, the veracity dimension, which is arguably the closest one relating to this objective, is being neglected. Despite the current media frenzy about data science, the paper concludes that more time is needed to see whether it will emerge as discipline in its own right.
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Nandhini, P. "Emerging Technologies in Big Data." International Journal for Research in Applied Science and Engineering Technology 6, no. 6 (June 30, 2018): 2114–31. http://dx.doi.org/10.22214/ijraset.2018.6313.

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Raloff, J., and J. Silberner. "Chernobyl: Emerging Data on Accident." Science News 129, no. 19 (May 10, 1986): 292. http://dx.doi.org/10.2307/3970668.

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Vakali, A., B. Catania, and A. Maddalena. "XML data stores: emerging practices." IEEE Internet Computing 9, no. 2 (March 2005): 62–69. http://dx.doi.org/10.1109/mic.2005.48.

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TUCKER, MIRIAM E. "Emerging Data Support Switching Biologics." Skin & Allergy News 43, no. 2 (February 2012): 1–9. http://dx.doi.org/10.1016/s0037-6337(12)70034-7.

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Clifton, Chris, and Bhavani Thuraisingham. "Emerging standards for data mining." Computer Standards & Interfaces 23, no. 3 (July 2001): 187–93. http://dx.doi.org/10.1016/s0920-5489(01)00072-1.

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Kundu, Shakti, and M. L. "Emerging Role of Data Scientist in Big Data." International Journal of Computer Applications 123, no. 3 (August 18, 2015): 15–18. http://dx.doi.org/10.5120/ijca2015905258.

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Dissertations / Theses on the topic "Emerging Data"

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Chan, Wing-yan Sarah, and 陳詠欣. "Emerging substrings for sequence classification." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B2971672X.

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Peng, Ivy Bo. "Data Movement on Emerging Large-Scale Parallel Systems." Doctoral thesis, KTH, Beräkningsvetenskap och beräkningsteknik (CST), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-218338.

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Large-scale HPC systems are an important driver for solving computational problems in scientific communities. Next-generation HPC systems will not only grow in scale but also in heterogeneity. This increased system complexity entails more challenges to data movement in HPC applications. Data movement on emerging HPC systems requires asynchronous fine-grained communication and efficient data placement in the main memory. This thesis proposes an innovative programming model and algorithm to prepare HPC applications for the next computing era: (1) a data streaming model that supports emerging data-intensive applications on supercomputers, (2) a decoupling model that improves parallelism and mitigates the impact of imbalance in applications, (3) a new framework and methodology for predicting the impact of largescale heterogeneous memory systems on HPC applications, and (4) a data placement algorithm that uses a set of rules and a decision tree to determine the data-to-memory mapping in heterogeneous main memory. The proposed approaches in this thesis are evaluated on multiple supercomputers with different processors and interconnect networks. The evaluation uses a diverse set of applications that represent conventional scientific applications and emerging data-analytic workloads on HPC systems. The experimental results on the petascale testbed show that the approaches obtain increasing performance improvements as system scale increases and this trend supports the approaches as a valuable contribution towards future HPC systems.
Storskaliga HPC-system är en viktig drivkraft för att lösa datorproblem i vetenskapliga samhällen. Nästa generations HPC-system kommer inte bara att växa i skala utan också i heterogenitet. Denna ökade systemkomplexitet medför flera utmaningar för dataförflyttning i HPC-applikationer. Dataförflyttning på nya HPC-system kräver asynkron, finkorrigerad kommunikation och en effektiv dataplacering i huvudminnet. Denna avhandling föreslår en innovativ programmeringsmodell och algoritm för att förbereda HPC-applikationer för nästa generation: (1) en dataströmningsmodell som stöder nya dataintensiva applikationer på superdatorer, (2) en kopplingsmodell som förbättrar parallelliteten och minskar obalans i applikationer, (3) en ny metologi och struktur för att förutse effekten av storskaliga, heterogena minnessystem på HPC-applikationer, och (4) en datalägesalgoritm som använder en uppsättning av regler och ett beslutsträd för att bestämma kartläggningen av data-till-minnet i det heterogena huvudminnet. Den föreslagna programmeringsmodellen i denna avhandling är utvärderad på flera superdatorer med olika processorer och sammankopplingsnät. Utvärderingen använder en mängd olika applikationer som representerar konventionella vetenskapliga applikationer och nya dataanalyser på HPC-system. Experimentella resultat på testbädden i petascala visar att programmeringsmodellen förbättrar prestandan när systemskalan ökar. Denna trend indikerar att modellen är ett värdefullt bidrag till framtida HPC-system.

QC 20171128

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Aldana, Walter Alberto 1977. "Data mining industry : emerging trends and new opportunities." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/81535.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, June 2000.
"May 2000."
Includes bibliographical references (leaves 170-179).
by Walter Alberto Aldana.
M.Eng.
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Lu, Qingda. "Data Layout Optimization Techniques for Modern and Emerging Architectures." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1228324157.

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Buehrer, Gregory T. "Scalable mining on emerging architectures." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1198866625.

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Ben, Mahmoud Mohamed Slim. "Addressing Security Challenges in Emerging Data-based Aeronautical Communications." Thesis, Toulouse, INSA, 2012. http://www.theses.fr/2012ISAT0006/document.

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Ce travail de thèse s'intéresse à la sécurité des futures communications aéronautiques de donnée. Le travail est divisé en trois grandes parties. La première contribution est une architecture de sécurité adaptative pour les communications aéronautiques intégrant un segment sol-bord par satellite. Un module de gestion de la sécurité a été conçu, développé, puis validé lors de la phase finale d'intégration du projet FAST (Fibre-like Aircraft Satellite Communications). La deuxième contribution est une méthodologie quantitative d'estimation du risque lié à la sécurité réseau. L'originalité de notre approche est d'être basée sur la notion de propagation du risque au sein des différents noeuds du réseau. Commecas d'étude, un réseau de communication aéroportuaire utilisant le protocole AeroMACS a été étudié dans le cadre du projet SESAR (Single European Sky ATM Research). La troisième contribution est une infrastructure à clés publiques (PKI) qui permet d'optimiser les échanges de signalisation (échanges de clés, certificats, vérification des signatures) entre l'avion et l'autorité de certification au sol. Le modèle de PKI proposé est un modèle hiérarchique utilisant la certification croisée entre les autorités de certification mères
This research work deals with the information and network security in the aeronautical communication domain. Three fundamental research axes are explored. First, a quantitative network security risk assessment methodology is proposed. Our approach is based on the risk propagation within the network nodes. As study cases, the algorithm has been validated in the scope of the European industrial project entitled SESAR (Single European Sky ATM Research) and the Aerospace Valley FAST (Fibrelike Aircraft Satellite Communications). Particularly, experimental results relative to the case study devoted to the FAST project shown that the global network risk in the non secured system architecture is relatively high, meaning the system needs more consideration from a security point of view. To cope with this issue, an adaptive security management framework for a satellite-based aeronauticalcommunication architecture has been proposed as a second contribution. A security manager module has been designed, implemented, then tested in the scope of the FAST project. Finally, as the security primitives used in the adaptive security management framework need to be efficiently exchanged, the last contribution consists in a scalable PKI adapted for the upcoming network-enabled aircraft. The idea is to minimize the air-ground additional overhead induced by the security procedures (keys, digital certificates, revocation/verification procedures). The PKI model we propose is a cross-certified multirooted hierarchical model
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Gao, Xing. "Investigating Emerging Security Threats in Clouds and Data Centers." W&M ScholarWorks, 2018. https://scholarworks.wm.edu/etd/1550153840.

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Data centers have been growing rapidly in recent years to meet the surging demand of cloud services. However, the expanding scale of a data center also brings new security threats. This dissertation studies emerging security issues in clouds and data centers from different aspects, including low-level cooling infrastructures and different virtualization techniques such as container and virtual machine (VM). We first unveil a new vulnerability called reduced cooling redundancy that might be exploited to launch thermal attacks, resulting in severely worsened thermal conditions in a data center. Such a vulnerability is caused by the wide adoption of aggressive cooling energy saving policies. We conduct thermal measurements and uncover effective thermal attack vectors at the server, rack, and data center levels. We also present damage assessments of thermal attacks. Our results demonstrate that thermal attacks can negatively impact the thermal conditions and reliability of victim servers, significantly raise the cooling cost, and even lead to cooling failures. Finally, we propose effective defenses to mitigate thermal attacks. We then perform a systematic study to understand the security implications of the information leakage in multi-tenancy container cloud services. Due to the incomplete implementation of system resource isolation mechanisms in the Linux kernel, a spectrum of system-wide host information is exposed to the containers, including host-system state information and individual process execution information. By exploiting such leaked host information, malicious adversaries can easily launch advanced attacks that can seriously affect the reliability of cloud services. Additionally, we discuss the root causes of the containers' information leakage and propose a two-stage defense approach. The experimental results show that our defense is effective and incurs trivial performance overhead. Finally, we investigate security issues in the existing VM live migration approaches, especially the post-copy approach. While the entire live migration process relies upon reliable TCP connectivity for the transfer of the VM state, we demonstrate that the loss of TCP reliability leads to VM live migration failure. By intentionally aborting the TCP connection, attackers can cause unrecoverable memory inconsistency for post-copy, significantly increase service downtime, and degrade the running VM's performance. From the offensive side, we present detailed techniques to reset the migration connection under heavy networking traffic. From the defensive side, we also propose effective protection to secure the live migration procedure.
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Buehrer, Gregory T. "Scalable mining on emerging architectures." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1198866625.

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Patterton, Louise Hilda. "Research data management practices of emerging researchers at a South African research council." Diss., University of Pretoria, 2016. http://hdl.handle.net/2263/59502.

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Management of research data is globally being seen as part of good research practice. As a result of this, funders are increasingly insisting on proof of good research data management (RDM) practices when funding proposals are submitted. This study aimed at establishing the data management practices of emerging researchers at the Council for Scientific and Industrial Research (CSIR), South Africa. With no official RDM procedures currently being implemented at the CSIR, it was hoped that by gaining information about the RDM practices of emerging CSIR researchers, as well as insight into the RDM challenges experienced by them, this researcher would be able to put forward recommendations enabling the establishing of an RDM regime at the CSIR. The study aimed at answering several research questions. The main research question was: How can an organisation like the CSIR ensure that future researchers apply best practices when managing the CSIR’s research data? Five research sub-questions were identified: 1. What are the international RDM requirements, standards, best practices and expectations that are being developed? 2. What data practices need more formalised support: at CSIR, nationally, internationally? 3. What data are collected and held by emerging researchers in the CSIR? 4. What are the current RDM practices and themes among emerging researchers in the CSIR? 5. What are the RDM-related challenges, issues and concerns facing emerging researchers at the CSIR? A total of 48 emerging researchers from the Council for Scientific and Industrial Research (CSIR), South Africa completed an online survey investigating their RDM practices. RDM practices investigated included the use of data management plans, data storage and backup locations, creation of metadata, metadata standard adherence, and data sharing practices. Challenges faced when managing research data, as well as RDM needs and requirements, also formed part of the survey. Results of the online questionnaire revealed that the RDM practices of the group studied do not show to differ significantly from experienced CSIR researchers, or from researchers studied elsewhere on the globe. Findings enabled this researcher to put forward several recommendations which would assist in the implementing of a formalised RDM structure at the CSIR. Recommendations addressed, but were not limited to: formalization of RDM procedures, RDM marketing, and RDM training.
Dissertation (MIS)--University of Pretoria, 2016.
Information Science
MIS
Unrestricted
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Oller, Westerberg Amelia. "Revising installed photovoltaic capacities on emerging markets by analysing customs data." Thesis, Uppsala universitet, Byggteknik och byggd miljö, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-438780.

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The global solar PV market is growing fast, and so is the production and trade with photovoltaic products and peripherals. Until now, the largest development has taken place in highly developed and electrified countries with good administrative control over their electricity system. Recently, however, new markets in developing countries have become increasingly relevant in terms of market share, system sizes and installed capacities. Statistics from these types of countries are often weak or non-existent, leading to problems for global organizations such as the International Energy Agency (IEA) or the International Renewable Energy Agency (IRENA), whose task is to follow, analyze and document named development.  In this report, a method is presented in which customs data monitored by the ‘Market Analysis and Research’ section of the International Trade Centre, an agency of UN’s World Trade Organization, is analyzed and converted into annual installed PV capacity volumes. By complementing the basic data from the customs database with price statistics from IEA PVPS task 1 along with national module production data from IEA PVPS task 1 and the RTS cooperation a data conversion is executed.  The method has been improved incrementally, where different assumptions have been modified or added, so that the data conversion of exported and imported PV products, expressed in dollar per yearly quarter, match the official statistics of annual installed capacity for a number of reference countries with comprehensive PV capacity statistics. The sensitivity analysis shows that the method is sensitive to the accuracy of the annual domestic national PV module production data and to price changes of Chinese PV modules. For countries with accurate PV module production data, or countries with no module production, the method seems to be able to estimate the annual installed capacity in 2018 with an average difference of 21% and a maximum difference of ±38% and a total average difference of 12%, 17% and 11% for 2016, 2017 and 2018 respectively.  By implementing this method, an estimate on yearly installed capacities can be generated in all countries connected to the UN customs database and where the domestic module production is known. This gives the opportunity to at least get an assessment of how much PV that has been installed in developing countries that lack official statistics about their domestic PV market. The regions with the lowest existing data coverage in the world have been determined to be Africa and the Middle East. When applying the method on countries in Africa and the Middle East, larger capacities than the reference data were obtained.
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Books on the topic "Emerging Data"

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Black, Uyless D. Emerging communications technologies. 2nd ed. Upper Saddle River, NJ: Prentice Hall, 1997.

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Black, Uyless D. Emerging communications technologies. Englewood Cliffs, N.J: PTR Prentice Hall, 1994.

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Mora, José Antonio Núñez, and M. Beatriz Mota Aragón, eds. Data Analytics Applications in Emerging Markets. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4695-0.

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Zelkowitz, Marvin V. Emerging technologies. London: Academic Press/Elsevier, 2008.

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Naiouf, Marcelo, Enzo Rucci, Franco Chichizola, and Laura De Giusti, eds. Cloud Computing, Big Data & Emerging Topics. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84825-5.

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Rucci, Enzo, Marcelo Naiouf, Franco Chichizola, Laura De Giusti, and Armando De Giusti, eds. Cloud Computing, Big Data & Emerging Topics. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14599-5.

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Rucci, Enzo, Marcelo Naiouf, Franco Chichizola, and Laura De Giusti, eds. Cloud Computing, Big Data & Emerging Topics. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61218-4.

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Saha, Debashis. Next generation data communication technologies: Emerging trends. Hershey, PA: Information Science Reference, 2012.

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Hassanien, Aboul Ella, Siddhartha Bhattacharyya, Satyajit Chakrabati, Abhishek Bhattacharya, and Soumi Dutta, eds. Emerging Technologies in Data Mining and Information Security. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4367-2.

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Tavares, João Manuel R. S., Satyajit Chakrabarti, Abhishek Bhattacharya, and Sujata Ghatak, eds. Emerging Technologies in Data Mining and Information Security. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9774-9.

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Book chapters on the topic "Emerging Data"

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Vong, John, and Insu Song. "Securing Online Medical Data." In Emerging Technologies for Emerging Markets, 133–43. Singapore: Springer Singapore, 2014. http://dx.doi.org/10.1007/978-981-287-347-7_10.

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Mohanty, Soumendra, Madhu Jagadeesh, and Harsha Srivatsa. "Emerging Database Landscape." In Big Data Imperatives, 73–106. Berkeley, CA: Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-4873-6_4.

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Dai, Jun, Michael M. Ohadi, Diganta Das, and Michael G. Pecht. "Emerging Trends." In Optimum Cooling of Data Centers, 159–73. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-5602-5_9.

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Hillmer, Ute. "Emerging Data and Data Analysis." In Technology Acceptance in Mechatronics, 143–76. Wiesbaden: Gabler, 2009. http://dx.doi.org/10.1007/978-3-8349-8375-6_9.

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Cheng, Xuntao, Cheng Liu, and Bingsheng He. "Emerging Hardware Technologies." In Encyclopedia of Big Data Technologies, 1–5. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_170-1.

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Cheng, Xuntao, Cheng Liu, and Bingsheng He. "Emerging Hardware Technologies." In Encyclopedia of Big Data Technologies, 699–703. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-77525-8_170.

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Ma, Jingyuan. "Emerging Digital Markets and Regulation." In Regulating Data Monopolies, 17–54. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8766-2_2.

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Jamshed, Aatif, Asmita Dixit, Amit Sinha, Kanika Gupta, and Manish Kumar. "Data Science Practices." In Emerging Technologies in Computing, 117–33. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003121466-6.

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Ziya, Fatima, Meenu Shukla, R. Sharmila, Umang Kant, and Jawwad Zaidi. "Multimedia Big Data." In Emerging Technologies in Computing, 191–216. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003121466-10.

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Dai, Jun, Michael M. Ohadi, Diganta Das, and Michael G. Pecht. "Erratum to: Emerging Trends." In Optimum Cooling of Data Centers, E1—E2. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-5602-5_10.

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Conference papers on the topic "Emerging Data"

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Ioannidis, Yannis. "Emerging data management systems." In the 14th ACM international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1099554.1099556.

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Palanisamy, Balaji, and Chao Li. "Self-Emerging Data Infrastructures." In 2019 IEEE 5th International Conference on Collaboration and Internet Computing (CIC). IEEE, 2019. http://dx.doi.org/10.1109/cic48465.2019.00040.

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Nguyen, Ngan V. T., Vinh T. Nguyen, Vung Pham, and Tommy Dang. "FinanViz: Visualizing Emerging Topics in Financial News." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622097.

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Vahdat, Amin, Hong Liu, Xiaoxue Zhao, and Chris Johnson. "The Emerging Optical Data Center." In Optical Fiber Communication Conference. Washington, D.C.: OSA, 2011. http://dx.doi.org/10.1364/ofc.2011.otuh2.

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Zhang, Jingyuan, Chun-Ta Lu, Bokai Cao, Yi Chang, and Philip S. Yu. "Connecting emerging relationships from news via tensor factorization." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258048.

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Pham, Vung, Vinh T. Nguyen, and Tommy Dang. "IoTViz: Visualizing emerging topics in the internet of things." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622375.

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Khan, Arijit, Yinghui Wu, and Xifeng Yan. "Emerging Graph Queries in Linked Data." In 2012 IEEE International Conference on Data Engineering (ICDE 2012). IEEE, 2012. http://dx.doi.org/10.1109/icde.2012.143.

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Khan, Nadim Akhtar, and Humma Ahangar. "Emerging trends in open research data." In 2017 9th International Conference on Information and Knowledge Technology (IKT). IEEE, 2017. http://dx.doi.org/10.1109/ikt.2017.8258631.

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Hou, Dongdong, Weiming Zhang, Jiayang Liu, Siyan Zhou, Dongdong Chen, and Nenghai Yu. "Emerging applications of reversible data hiding." In the 2nd International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3313950.3313952.

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Balakrishnan, K. J. "Emerging Techniques for Test Data Compression." In 14th Asian Test Symposium (ATS'05). IEEE, 2005. http://dx.doi.org/10.1109/ats.2005.57.

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Reports on the topic "Emerging Data"

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Quach, Tu-Thach, Sapan Agarwal, Conrad D. James, Matthew J. Marinella, and James Bradley Aimone. Sparse Data Acquisition on Emerging Memory Architectures. Office of Scientific and Technical Information (OSTI), July 2018. http://dx.doi.org/10.2172/1530151.

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Podio, Fernando, William Vollrath, Joel Williams, Ben Kobler, and Don Crouse. Emerging network storage management standards for intelligent data storage subsystems. Gaithersburg, MD: National Institute of Standards and Technology, 1998. http://dx.doi.org/10.6028/nist.ir.6179.

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Tarko, Andrew P., Qiming Guo, and Raul Pineda-Mendez. Using Emerging and Extraordinary Data Sources to Improve Traffic Safety. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317283.

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The current safety management program in Indiana uses a method based on aggregate crash data for conditions averaged over several-year periods with consideration of only major roadway features. This approach does not analyze the risk of crashes potentially affected by time-dependent conditions such as traffic control, operations, weather and their interaction with road geometry. With the rapid development of data collection techniques, time-dependent data have emerged, some of which have become available for safety management. This project investigated the feasibility of using emerging and existing data sources to supplement the current safety management practices in Indiana and performed a comprehensive evaluation of the quality of the new data sources and their relevance to traffic safety analysis. In two case studies, time-dependent data were acquired and integrated to estimate their effects on the hourly probability of crash and its severity on two selected types of roads: (1) rural freeways and (2) signalized intersections. The results indicate a considerable connection between hourly traffic volume, average speeds, and weather conditions on the hourly probability of crash and its severity. Although some roadway geometric features were found to affect safety, the lack of turning volume data at intersections led to some counterintuitive results. Improvements have been identified to be implemented in the next phase of the project to eliminate these undesirable results.
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Rice, Matthew T., Fabiana I. Paez, Aaron P. Mulhollen, Brandon M. Shore, and Douglas R. Caldwell. Crowdsourced Geospatial Data: A Report on the Emerging Phenomena of Crowdsourced and User-Generated Geospatial Data. Fort Belvoir, VA: Defense Technical Information Center, November 2012. http://dx.doi.org/10.21236/ada576607.

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Bengston, Joel, Robert R. Cronin, Robert B. Davidson, and George Gamata. Emerging Technologies Program Integration Report. Volume 2. Background, Delphi and Workshop Data. Appendices. Fort Belvoir, VA: Defense Technical Information Center, May 1987. http://dx.doi.org/10.21236/ada196840.

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Rowland, Peter, and José Luis Torres. Determinants of spread and creditworthiness for emerging market sovereign debt: a panel data study. Bogotá, Colombia: Banco de la República, July 2004. http://dx.doi.org/10.32468/be.295.

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Middleton, Richard Stephen. Data set for Journal article "The shale gas revolution: barriers, sustainability, and emerging opportunities". Office of Scientific and Technical Information (OSTI), May 2017. http://dx.doi.org/10.2172/1356118.

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Sarmiento, Miguel. Sudden Yield Reversals and Financial Intermediation in Emerging Markets. Banco de la República, October 2022. http://dx.doi.org/10.32468/be.1210.

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Banks in emerging market economies rely on cross-border interbank lending to financing firms in the real sector. By matching cross-border bank-to-bank loan level data with domestic bank-to-firm loan level data, and firm-level data, this paper shows that sudden yield reversal observed during the 2013 Fed taper tantrum resulted in a substantial contraction of cross-border interbank lending in emerging markets that significantly reduced the supply of domestic corporate credit and increased the corporate loan rates. Results show that firms with an ex-ante high concentration of credit granted by exposed banks in the cross-border interbank market exhibited low bank credit and substantial real effects, including a decline in imports and exports. The results further indicate that cross-border intra-group lending and domestic unsecured interbank funding contribute to smoothing the effects of sudden yield reversals on the financial intermediation. Overall, the results are consistent with the notion that banks’ exposition in international credit markets contributes to global financial conditions’ transmission to the economy.
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Garcia, Marcio, and Roberto Rigobon. A Risk Management Approach to Emerging Market's Sovereign Debt Sustainability with an Application to Brazilian Data. Cambridge, MA: National Bureau of Economic Research, March 2004. http://dx.doi.org/10.3386/w10336.

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Walker, Lina, Catherine Gillespie, Erin Mackay, and Dani Gillespie. Shaping the Future of Digital Health: Key Themes Emerging from a Series of Roundtables on the Health Data Revolution. AARP Public Policy Institute, January 2020. http://dx.doi.org/10.26419/ppi.00093.001.

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