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Статті в журналах з теми "Apache Hive"
Martinez-Mosquera, Diana, Rosa Navarrete, and Sergio Luján-Mora. "Efficient processing of complex XSD using Hive and Spark." PeerJ Computer Science 7 (August 17, 2021): e652. http://dx.doi.org/10.7717/peerj-cs.652.
Повний текст джерелаJankatti, Santosh, Raghavendra B. K., Raghavendra S., and Meenakshi Meenakshi. "Performance evaluation of Map-reduce jar pig hive and spark with machine learning using big data." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (August 1, 2020): 3811. http://dx.doi.org/10.11591/ijece.v10i4.pp3811-3818.
Повний текст джерелаWankhede, Manish, Vijay Trivedi, and Vineet Richhariya. "Location based Analysis of Twitter Data using Apache Hive." International Journal of Computer Applications 153, no. 10 (November 17, 2016): 21–26. http://dx.doi.org/10.5120/ijca2016912170.
Повний текст джерелаRavindra, Padmashree, and Kemafor Anyanwu. "Nesting Strategies for Enabling Nimble MapReduce Dataflows for Large RDF Data." International Journal on Semantic Web and Information Systems 10, no. 1 (January 2014): 1–26. http://dx.doi.org/10.4018/ijswis.2014010101.
Повний текст джерелаHacimahmud, Abdullayev Vugar, Ragimova Nazila Ali, and Khalilov Matlab Etibar. "The research of social processes at the university using big data." MATEC Web of Conferences 348 (2021): 01003. http://dx.doi.org/10.1051/matecconf/202134801003.
Повний текст джерелаRitika Siril Paul, Yazala, and Dilipkumar A. Borikar. "An Approach To Twitter Sentiment Analysis Over Hadoop." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 2018): 374. http://dx.doi.org/10.14419/ijet.v7i4.5.20110.
Повний текст джерелаChang, Bao-Rong, Hsiu-Fen Tsai, Yun-Che Tsai, Chin-Fu Kuo, and Chi-Chung Chen. "Integration and optimization of multiple big data processing platforms." Engineering Computations 33, no. 6 (August 1, 2016): 1680–704. http://dx.doi.org/10.1108/ec-08-2015-0247.
Повний текст джерелаYu, Dongjin, Wensheng Dou, Zhixiang Zhu, and Jiaojiao Wang. "Materialized View Selection Based on Adaptive Genetic Algorithm and Its Implementation with Apache Hive." International Journal of Computational Intelligence Systems 8, no. 6 (2015): 1091. http://dx.doi.org/10.1080/18756891.2015.1113744.
Повний текст джерелаTidke, Bharat, Rupa Mehta, Dipti Rana, and Hullash Jangir. "Topic Sensitive User Clustering Using Sentiment Score and Similarity Measures." International Journal of Web-Based Learning and Teaching Technologies 15, no. 2 (April 2020): 34–45. http://dx.doi.org/10.4018/ijwltt.2020040103.
Повний текст джерелаGomathy, Dr C. K. "Efficient Transfer of data from RDBMS to HDFS and conversion to JSON format." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (October 31, 2021): 1869–71. http://dx.doi.org/10.22214/ijraset.2021.38710.
Повний текст джерелаДисертації з теми "Apache Hive"
Brotánek, Jan. "Apache Hadoop jako analytická platforma." Master's thesis, Vysoká škola ekonomická v Praze, 2017. http://www.nusl.cz/ntk/nusl-358801.
Повний текст джерелаПеревертайло, Андрій Ігорович. "Організація розподілених обчислень при відстеженні траєкторії об‘єктів у мережі сенсорів". Master's thesis, Київ, 2018. https://ela.kpi.ua/handle/123456789/23181.
Повний текст джерелаTopicality. Acoustic sensory networks are increasingly used in many applications of human activity. Also, the hardware component of the computing resource of such networks becomes more productive. At the same time, requirements for the speed and the maximum number of objects that must be simultaneously localized in such networks increase, therefore the question of applying new algorithms for providing distributed computing on the network nodes is relevant. The object of study are methods of designing distributed computing systems in sensory networks when locating sources of acoustic signal. The study examines the implementation of the distributed computing system in sensory networks in the localization of sources of acoustic signal. Objective: to develop a model simulating the localization of acoustic signal sources in the sensor network and consists of a network of sensors and servers forming a cluster, as well as the development of an algorithm that will allow the time difference of the Arrival (TDOA) to implement the signal localization and distribute computations between cluster servers using Apache Spark platform.
Актуальность темы. Акустические сенсорные сети все чаще используются во многих прикладных областях человеческой деятельности. Также более продуктивной становится аппаратная составляющая вычислительного ресурса таких сетей. Вместе с этим увеличиваются требования по быстродействию и предельному количеству объектов, которые должны быть одновременно локализованы в таких сетях, поэтому вопрос применения новых алгоритмов для обеспечения распределенных вычислений на узлах сети является актуальным. Объектом работы являются методы проектирования системы распределенных вычислений в сенсорных сетях при локализации источников акустического сигнала. Предметом работы является реализация системы распределенных вычислений в сенсорных сетях при локализации источников акустического сигнала. Цель работы: разработка модели, имитирующей локализацию источников акустических сигналов в сенсорной сети и состоит из сети сенсоров и серверов, образующих кластер, а также разработка алгоритма, который позволит реализовать метод TDOA (Time Difference of Arrival) для локализации сигналов и распределить вычисления между серверами кластера используя платформу Apache Spark.
Venumuddala, Ramu Reddy. "Distributed Frameworks Towards Building an Open Data Architecture." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc801911/.
Повний текст джерелаLa, Ferrara Massimiliano. "Elaborazione di Big Data: un’applicazione dello Speed Layer di Lambda Architecture." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016.
Знайти повний текст джерелаFouche, Jean-Paul. "A diffusion tensor imaging study in HIV patients with and without apathy." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/5146.
Повний текст джерелаENGLISH ABSTRACT: HIV/AIDS is a global epidemic that accounts for a large percentage of the mortality in South Africa every year. Since the implementation of anti-retroviral treatment, HIV positive individuals have been living longer, and the cognitive impairment associated with the disease is becoming increasingly apparent. During the initial systemic infection of HIV, the virus migrates through the blood-brain barrier and inflicts axonal injury by causing upregulation of cytokines and neurotoxic proteins. HIV-associated dementia is a neuropsychological classification of cognitive impairment in HIV and a variety of symptoms have been classified as a part of the dementia complex. One of these is apathy, which is thought to be a precursor for dementia in HIV patients. Three groups of individuals have been recruited and scanned using magnetic resonance imaging (MRI) to examine changes in the brain. These are an HIV non-apathetic cohort, an HIV apathetic cohort and a healthy control cohort. Diffusion tensor imaging (DTI) is an MRI technique used to quantitatively assess white matter (WM) integrity using metrics such as fractional anisotropy (FA). Voxel-based analysis, tract-based spatial statistics (TBSS) and tractography are three established DTI analysis methods that have been applied in numerous studies. However, there are certain methodological strengths and limitations associated with each technique and therefore all three of these techniques were used to compare WM differences across groups. The frontal-subcortical pathways are known to be abnormal in apathy, and this has been demonstrated in a number of imaging studies. Most of these studies have examined apathy in the context of neurodegenerative disorders such as Alzheimer’s disease and Parkinson’s. However, to our knowledge this is the first DTI study in HIV apathetic patients. With the tractography method, the anterior thalamic radiation and the corpus callosum were reconstructed for each individual to determine whether there were any global changes in these tracts. No significant changes were found. However, a variety of regions in the WM were significantly abnormal in the HIV cohorts when comparing the data at a voxel-based level and using TBSS. This included areas such as the genu and splenium of the corpus callosum, the internal capsule and corona radiata. Changes in frontal WM for the HIV apathy group are an indication of dysfunction in the frontal-striatal circuits, and previous literature has implicated these circuits in the neuropathology of apathy in a variety of central nervous system (CNS) disorders.
AFRIKAANSE OPSOMMING: MIV/VIGS is `n wêreldwye epidemie wat verantwoordelik is vir `n hoë sterftesyfer in Suid- Afrika elke jaar. Sedert die inleiding van anti-retrovirale behandeling, het die MIV-positiewe populasie se lewensduur verleng. Tesame met langer lewensduur, het die kognitiewe verswakking wat geassosieer word met die siekte ook meer prominent na vore gekom. Gedurende die beginstadium van sistemiese infeksie in MIV is daar `n migrasie van die virus deur die bloed-breinskans. MIV kan indirek verantwoordelik wees vir aksonale beskadiging deur verhoging van neurotoksiese proteine en sitokinien te induseer. MIV-geassosieerde demensie is `n neurosielkundige klassifikasie van kognitiewe verswakking in MIV en verskeie simptome is al geïdentifiseer as deel van die demensie kompleks. Een van die simptome is apatie en daar word gespekuleer dat dit `n voorloper is vir demensie in MIV pasiënte. Drie groepe individue was gewerf vir die studie en geskandeer deur magnetiese resonansie beeldvorming (MRB) om sodoende veranderinge in die brein te ondersoek. Die groepe was onderskeidelik `n HIV nie-apatiese kohort, `n HIV apatiese kohort en `n gesonde kontrole kohort. Diffusie tensor beelding (DTB) is `n MRB tegniek wat toegepas word om witstof integriteit te meet deur gebruik te maak van maatstawwe soos fraksionele anisotropie (FA). “Voxel-based analysis”, “tract-based spatial statistics (TBSS)” en “tractography” is drie gevestigde DTB analitiese metodes wat al in talle studies toegepas was. Daar is egter sekere metodologiese voordele en beperkings verbonde aan elke tegniek en daarom is al drie tegnieke gebruik om witstof verskille tussen groepe te vergelyk. Die frontale-subkortikale roetes in die brein is bekend vir abnormaliteite in apatie en dit was ook al gedemonstreer in verskeie studies. Die meeste van die studies het apatie ondersoek in die konteks van neurodegeneratiewe siektes soos Alzheimer se siekte en Parkinson se siekte. Maar sover ons weet is hierdie die eerste DTB studie in MIV pasiënte met apatie. Met die “tractography” metode was die anterior thalamic radiation en corpus callosum herbou vir elke individu. Dit was om te bepaal of daar enige globale veranderinge is in hierdie gebiede, maar geen beduidende veranderinge is gevind nie.`n Verskeidenheid van gebiede in die witstof was beduidend abnormaal in die MIV kohorte wanneer die data vergelyk was met “TBSS” en “voxel-based analysis.” Dit het gebiede ingesluit soos die genu en splenium van die corpus callosum, die internal capsule en die corona radiata. Veranderinge in die frontale witstof vir die MIVapatie groep is `n aanduiding van disfunksie in die frontale-striatale bane. Vorige literatuur impliseer dat hierdie bane betrokke is in die neuro-patologie van apatie in verskeie sentrale senuweestelsel (SS) steurings.
Forster, Rodrigo Richard. "Hive on spark and MapReduce : a methodology for parameter tuning." Master's thesis, 2018. http://hdl.handle.net/10362/52854.
Повний текст джерелаAs the era of “big data” has arrived, more and more companies start using distributed file systems to manage and process their data streams like the Hadoop distributed file system framework (HDFS). This software library offers a way to store large files across multiple machines. Large data sets are processed by using its inherent programming model MapReduce. Apache Spark is a relatively new alternative to Hadoop MapReduce and claims to offer a performance boost up to 10 times for certain applications, while maintaining its automatic fault tolerance. To leverage the Data Warehouse capabilities of Hadoop Apache Hive was introduced. It is a concept for Big Data analytics that works on top of Hadoop and provides data analysis tools and most importantly translates queries to MapReduce and Spark jobs. Therefore, it exploits the scalability of Hadoop and offers data exploration and mining capabilities to non-developers. However, it is difficult for users to utilize the full potential of the Apache Spark execution engine. This results in very long execution times. Therefore, this project work gives researches and companies a tuning methodology that significantly can improve the execution time of queries. As a result, this tuning methodology could optimize a real-world batch-processing query by 5 times. Moreover, it gives insides in the underlying reasons of this big improvement by using Apache Spark Monitoring tools. The result can be helpful for many practitioners and researchers that would like to optimise the performance of Spark and MapReduce queries executed in Hive on top of an Apache Hadoop cluster.
Книги з теми "Apache Hive"
AIDS Prevention and Control Project. APAC's communique 2005: A catalogue of BCC materials on STD/HIV/AIDS prevention & care and support. Chennai: AIDS Prevention and Control (APAC) Project, Voluntary Health Services (VHS), 2005.
Знайти повний текст джерелаGupta, Ankur, Scott Shaw, Andreas François Vermeulen, and David Kjerrumgaard. Practical Hive: A Guide to Hadoop's Data Warehouse System. Apress, 2016.
Знайти повний текст джерелаProgramming Hive: Data Warehouse and Query Language for Hadoop. Sebastopol, California: O'Reilly, 2012.
Знайти повний текст джерелаApache Hive Essentials: Essential techniques to help you process, and get unique insights from, big data, 2nd Edition. Packt Publishing, 2018.
Знайти повний текст джерелаRuncan, Patricia. Copilărie și parentalitate cu impact. Editura de Vest, 2020. http://dx.doi.org/10.51820/autentic.2020.vol.1.
Повний текст джерелаRUNCAN, PATRICIA. Copilărie, consiliere și parentalitate cu impact. Vol. 1. Ediție revizuită. Seria AUTENTIC. EDITURA DE VEST, 2021. http://dx.doi.org/10.51820/autentic.2021.vol.1.editie_revizuita.
Повний текст джерелаЧастини книг з теми "Apache Hive"
Srinivasa, K. G., Siddesh G. M., and Srinidhi H. "Apache Hive." In Computer Communications and Networks, 55–72. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77800-6_3.
Повний текст джерелаVohra, Deepak. "Apache Hive." In Practical Hadoop Ecosystem, 209–31. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2199-0_3.
Повний текст джерелаVohra, Deepak. "Using Apache Hive." In Pro Docker, 131–39. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1830-3_9.
Повний текст джерелаElliott, Ed. "Spark SQL and Hive Tables." In Introducing .NET for Apache Spark, 107–18. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6992-3_6.
Повний текст джерелаBansal, Krati, Priyanka Chawla, and Pratik Kurle. "Analyzing Performance of Apache Pig and Apache Hive with Hadoop." In Engineering Vibration, Communication and Information Processing, 41–51. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1642-5_4.
Повний текст джерелаVohra, Deepak. "Creating an Apache Hive Table with MongoDB." In Pro MongoDB™ Development, 405–26. Berkeley, CA: Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-1598-2_11.
Повний текст джерелаMaheswari, N., and M. Sivagami. "Large-Scale Data Analytics Tools: Apache Hive, Pig, and HBase." In Data Science and Big Data Computing, 191–220. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31861-5_9.
Повний текст джерелаRana, Poonam, Vineet Sharma, and P. K. Gupta. "Exploration of Apache Hadoop Techniques: Mapreduce and Hive for Big Data." In Communications in Computer and Information Science, 543–52. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1810-8_54.
Повний текст джерелаSethy, Rotsnarani, Santosh Kumar Dash, and Mrutyunjaya Panda. "Performance Comparison Between Apache Hive and Oracle SQL for Big Data Analytics." In Advances in Intelligent Systems and Computing, 130–41. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60618-7_14.
Повний текст джерелаMiles, Sam, Jack Coffin, Amin Ghaziani, Daniel Baldwin Hess, and Alex Bitterman. "After/Lives: Insights from the COVID-19 Pandemic for Gay Neighborhoods." In The Life and Afterlife of Gay Neighborhoods, 393–418. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66073-4_17.
Повний текст джерелаТези доповідей конференцій з теми "Apache Hive"
Camacho-Rodríguez, Jesús, Ashutosh Chauhan, Alan Gates, Eugene Koifman, Owen O'Malley, Vineet Garg, Zoltan Haindrich, et al. "Apache Hive." In SIGMOD/PODS '19: International Conference on Management of Data. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3299869.3314045.
Повний текст джерелаHuai, Yin, Ashutosh Chauhan, Alan Gates, Gunther Hagleitner, Eric N. Hanson, Owen O'Malley, Jitendra Pandey, Yuan Yuan, Rubao Lee, and Xiaodong Zhang. "Major technical advancements in apache hive." In SIGMOD/PODS'14: International Conference on Management of Data. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2588555.2595630.
Повний текст джерелаFuad, Ammar, Alva Erwin, and Heru Purnomo Ipung. "Processing performance on Apache Pig, Apache Hive and MySQL cluster." In 2014 International Conference on Information, Communication Technology and System (ICTS). IEEE, 2014. http://dx.doi.org/10.1109/icts.2014.7010600.
Повний текст джерелаAmirthalingam, Thivviyan, and Helmi Md Rais. "Automated Table Partitioner (ATAP) in Apache Hive." In 2018 4th International Conference on Computer and Information Sciences (ICCOINS). IEEE, 2018. http://dx.doi.org/10.1109/iccoins.2018.8510580.
Повний текст джерелаVasilev, Gordei Vladimirovich, and Alexander Vladimirovich Vasilev. "OPTIMIZED BIG DATA STORAGE WITH APACHE HIVE." In НАУКА, ИННОВАЦИИ И ТЕХНОЛОГИИ: ОТ ИДЕЙ К ВНЕДРЕНИЮ. Комсомольск-на-Амуре: Комсомольский-на-Амуре государственный университет, 2022. http://dx.doi.org/10.17084/978-5-7765-1502-6-2022-89.
Повний текст джерелаAhmad, Mudassar, Safina Kanwal, Maryam Cheema, and Muhammad Asif Habib. "Performance Analysis of ECG Big Data using Apache Hive and Apache Pig." In 2019 8th International Conference on Information and Communication Technologies (ICICT). IEEE, 2019. http://dx.doi.org/10.1109/icict47744.2019.9001287.
Повний текст джерелаGunay, Melih, M. Numan Ince, and Alper Cetinkaya. "Apache Hive Performance Improvement Techniques for Relational Data." In 2019 International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2019. http://dx.doi.org/10.1109/idap.2019.8875898.
Повний текст джерелаHaryono, Givanna Putri, and Ying Zhou. "Profiling apache HIVE query from run time logs." In 2016 International Conference on Big Data and Smart Computing (BigComp). IEEE, 2016. http://dx.doi.org/10.1109/bigcomp.2016.7425802.
Повний текст джерелаKamath, Divya, Praveen Srinivas, Ashika Gopal, B. V. Lanchana, and V. Suma. "A profiling tool for apache hive run-time query." In 2017 International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2017. http://dx.doi.org/10.1109/iccmc.2017.8282740.
Повний текст джерелаChao, Lu, Chundian Li, Fan Liang, Xiaoyi Lu, and Zhiwei Xu. "Accelerating Apache Hive with MPI for Data Warehouse Systems." In 2015 IEEE 35th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2015. http://dx.doi.org/10.1109/icdcs.2015.73.
Повний текст джерела