Relevant bibliographies by topics / Hadoop framework

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

Academic literature on the topic 'Hadoop framework'

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

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Journal articles on the topic "Hadoop framework"

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Sudirman, Ahmad, Irawan, and Zawiyah Saharuna. "PENGEMBANGAN SISTEM BIG DATA: RANCANG BANGUN INFRASTRUKTUR DENGAN FRAMEWORK HADOOP." Journal of Informatics and Computer Engineering Research 1, no. 1 (2024): 25–32. https://doi.org/10.31963/jicer.v1i1.4919.

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Hadoop is a distributed data storage platform that provides a parallel processing framework. HDFS (Hadoop Distributed File System) and Map Reduce are two very important parts of Hadoop, HDFS is a distributed storage system built on java, while Map Reduce is a program for managing large data in parallel and distributed. The research focused on testing the data transfer speed on HDFS, testing using 3 types of data, namely video, ISO Image, and text with a total of 512 MB, 1 GB, and 2 GB of data. Testing will be carried out by entering data into HDFS using the Hadoop command and changing the size
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Li, Xin Liang, and Jian De Zheng. "Improvement of Hadoop Security Mechanism." Applied Mechanics and Materials 484-485 (January 2014): 912–15. http://dx.doi.org/10.4028/www.scientific.net/amm.484-485.912.

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Hadoop, as an open-source cloud computing framework, is increasingly applied in many fields,while the weakness of security mechanism now becomes one of the main problems hindering its development. This paper first analyzes the current security mechanisms of Hadoop,then through study of Hadoop's security mechanism and analysis of security risk in its current version,proposes a corresponding solution based on secure multicast to resovle the security risks. All these could provide certain technical supports for the enterprises in their Hadoop applications with new security needs.
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Husain, Baydaa Hassan, and Subhi R. M. Zeebaree. "Improvised Distributions framework of Hadoop: A review." International Journal of Science and Business 5, no. 2 (2021): 31–41. https://doi.org/10.5281/zenodo.4461761.

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HADOOP is an open-source virtualization technology that allows the distributed processing of large data sets across standardized server clusters. With two modules, HADOOP Distributed File System (HDFS) and MapReduce framework, it is designed to scale single servers to thousands of computers, providing local computation and storage. Over a decade after HADOOP emerged on the forefront as an open system for Big Data analysis. Its growth has prompted several improvisations for particular data processing needs, based on the type of processing conditions at various periods of computation. This paper
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Giri, Pratit Raj, and Gajendra Sharma. "Apache Hadoop Architecture, Applications, and Hadoop Distributed File System." Semiconductor Science and Information Devices 4, no. 1 (2022): 14. http://dx.doi.org/10.30564/ssid.v4i1.4619.

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The data and internet are highly growing which causes problems in management of the big-data. For these kinds of problems, there are many software frameworks used to increase the performance of the distributed system. This software is used for the availability of large data storage. One of the most beneficial software frameworks used to utilize data in distributed systems is Hadoop. This paper introduces Apache Hadoop architecture, components of Hadoop, their significance in managing vast volumes of data in a distributed system. Hadoop Distributed File System enables the storage of enormous ch
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Sahu, kapil, Kaveri Bhatt, Prof Amit Saxena, and Kaptan Singh. "Implementation of Big-Data Applications Using Map Reduce Framework." International Journal of Engineering and Computer Science 9, no. 08 (2020): 25125–31. http://dx.doi.org/10.18535/ijecs/v9i08.4504.

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Clustering As a result of the rapid development in cloud computing, it & fundamental to investigate the performance of extraordinary Hadoop MapReduce purposes and to realize the performance bottleneck in a cloud cluster that contributes to higher or diminish performance. It is usually primary to research the underlying hardware in cloud cluster servers to permit the optimization of program and hardware to achieve the highest performance feasible. Hadoop is founded on MapReduce, which is among the most popular programming items for huge knowledge analysis in a parallel computing environment
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Tripathi, A. K., S. Agrawal, and R. D. Gupta. "A COMPARATIVE ANALYSIS OF CONVENTIONAL HADOOP WITH PROPOSED CLOUD ENABLED HADOOP FRAMEWORK FOR SPATIAL BIG DATA PROCESSING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-5 (November 15, 2018): 425–30. http://dx.doi.org/10.5194/isprs-annals-iv-5-425-2018.

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<p><strong>Abstract.</strong> The emergence of new tools and technologies to gather the information generate the problem of processing spatial big data. The solution of this problem requires new research, techniques, innovation and development. Spatial big data is categorized by the five V’s: volume, velocity, veracity, variety and value. Hadoop is a most widely used framework which address these problems. But it requires high performance computing resources to store and process such huge data. The emergence of cloud computing has provided, on demand, elastic, scalable and pa
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Tyagi, Adhishtha, and Sonia Sharma. "A Framework of Security and Performance Enhancement for Hadoop." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (2017): 437. http://dx.doi.org/10.23956/ijarcsse/v7i6/0171.

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Hadoop framework has been emerged as the most effective and widely adopted framework for Big Data processing. Map Reduce programming model is used for processing as well as generating large data sets. Data security has become an important issue as far as storage is concerned. By default theres no security mechanism in hadoop and it is the first choice of the business analyst and industrialists to store and manage data as well as theres a need to introduce security solutions to Hadoop in order to secure the important data in the Hadoop environment. We implemented and evaluated Dynamic Task Spli
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Srinivasa, Rao Putta. "COMPARATIVE ANALYSIS OF BIG DATA USING HADOOP FRAMEWORK." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 6, no. 4 (2019): 477–80. https://doi.org/10.5281/zenodo.2657666.

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Big data is an important concept in the field of information technology where companies and organization take advantage of the data that they have stored to find meaningful patterns and predictions to help them in making informed decisions. Big data analysis involves the use of advanced tools and techniques that are used in the processing the large volumes of data that is produced by the organization (Sammer, 2012, p. 23). The Hadoop framework is an important framework in enabling easy and efficient processing of large data sets thus making it one of the most popular big data analytics fr
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Ahmed, Eman, Amin A. Sorrour, Mohamed A. Sobh, and Ayman M. Bahaa-Eldin. "A Cloud-based Malware Detection Framework." International Journal of Interactive Mobile Technologies (iJIM) 11, no. 2 (2017): 113. http://dx.doi.org/10.3991/ijim.v11i2.6577.

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<p class="Els-Abstract-text">Malwares are increasing rapidly. The nature of distribution and effects of malwares attacking several applications requires a real-time response. Therefore, a high performance detection platform is required. In this paper, Hadoop is utilized to perform static binary search and detection for malwares and viruses in portable executable files deployed mainly on the cloud. The paper presents an approach used to map the portable executable files to Hadoop compatible files. The Boyer–Moore-Horspool Search algorithm is modified to benefit from the distribution of Ha
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Revathy, P., and Rajeswari Mukesh. "HadoopSec 2.0: Prescriptive analytics-based multi-model sensitivity-aware constraints centric block placement strategy for Hadoop." Journal of Intelligent & Fuzzy Systems 39, no. 6 (2020): 8477–86. http://dx.doi.org/10.3233/jifs-189165.

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Like many open-source technologies such as UNIX or TCP/IP, Hadoop was not created with Security in mind. Hadoop however evolved from the other tools over time and got widely adopted across large enterprises. Some of Hadoop’s architectural features present Hadoop its unique security issues. Given this security vulnerability and potential invasion of confidentiality due to malicious attackers or internal customers, organizations face challenges in implementing a strong security framework for Hadoop. Furthermore, given the method in which data is placed in Hadoop Cluster adds to the only growing
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Dissertations / Theses on the topic "Hadoop framework"

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Raja, Anitha. "A Coordination Framework for Deploying Hadoop MapReduce Jobs on Hadoop Cluster." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-196951.

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Apache Hadoop is an open source framework that delivers reliable, scalable, and distributed computing. Hadoop services are provided for distributed data storage, data processing, data access, and security. MapReduce is the heart of the Hadoop framework and was designed to process vast amounts of data distributed over a large number of nodes. MapReduce has been used extensively to process structured and unstructured data in diverse fields such as e-commerce, web search, social networks, and scientific computation. Understanding the characteristics of Hadoop MapReduce workloads is the key to ach
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Capitão, Micael José Pedrosa. "Mediator framework for inserting data into hadoop." Master's thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/14697.

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Mestrado em Engenharia de Computadores e Telemática<br>Data has always been one of the most valuable resources for organizations. With it we can extract information and, with enough information on a subject, we can build knowledge. However, it is first needed to store that data for later processing. On the last decades we have been assisting what was called “information explosion”. With the advent of the new technologies, the volume, velocity and variety of data has increased exponentially, becoming what is known today as big data. Telecommunications operators gather, using network moni
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Donepudi, Harinivesh. "An Apache Hadoop Framework for Large-Scale Peptide Identification." TopSCHOLAR®, 2015. http://digitalcommons.wku.edu/theses/1527.

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Peptide identification is an essential step in protein identification, and Peptide Spectrum Match (PSM) data set is huge, which is a time consuming process to work on a single machine. In a typical run of the peptide identification method, PSMs are positioned by a cross correlation, a statistical score, or a likelihood that the match between the trial and hypothetical is correct and unique. This process takes a long time to execute, and there is a demand for an increase in performance to handle large peptide data sets. Development of distributed frameworks are needed to reduce the processing t
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Bock, Matthew. "A Framework for Hadoop Based Digital Libraries of Tweets." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78351.

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The Digital Library Research Laboratory (DLRL) has collected over 1.5 billion tweets for the Integrated Digital Event Archiving and Library (IDEAL) and Global Event Trend Archive Research (GETAR) projects. Researchers across varying disciplines have an interest in leveraging DLRL's collections of tweets for their own analyses. However, due to the steep learning curve involved with the required tools (Spark, Scala, HBase, etc.), simply converting the Twitter data into a workable format can be a cumbersome task in itself. This prompted the effort to build a framework that will help in developing
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Bhatt, Parth. "A hadoop based framework for analyzing intrusion activities of advanced persistent threats." Instituto Tecnológico de Aeronáutica, 2013. http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=2831.

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Intruders often remain persistent and stealthy in order to regularly exfilterate the continuously evolving critical information of their target organization. This compels them to rapidly discover new and advanced techniques for exploiting the target environment in order to trespass the security mechanisms. Such adversaries are known as Advanced Persistent Threats (APT). APTs heavily use their target system';s unknown vulnerabilities. Therefore, even with highly monitored networks, defenders are able to detect their footprints only in later phases of the intrusion. Moreover, highly monitoring t
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Cavallo, Marco. "H2F: a hierarchical Hadoop framework to process Big Data in geo-distributed contexts." Doctoral thesis, Università di Catania, 2018. http://hdl.handle.net/10761/3801.

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L ampia diffusione di tecnologie ha portato alla generazione di enormi quantità di dati, o di Big Data, che devono essere raccolti, memorizzati e elaborati attraverso nuove tecniche per produrre valore nel modo migliore. I framework distribuiti di calcolo come Hadoop, basati sul paradigma MapRe- duce, sono stati utilizzati per elaborare tali quantità di dati sfruttando la potenza di calcolo di molti nodi di cluster. Purtroppo, in molte applicazioni di big data, i dati da elaborare risiedono in diversi data center computazionali eterogeni e distribuiti in luoghi diversi. In questo contesto le p
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Ramanayaka, Mudiyanselage Asanga. "Analyzing vertical crustal deformation induced by hydrological loadings in the US using integrated Hadoop/GIS framework." Bowling Green State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1525431761678148.

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Lakkimsetti, Praveen Kumar. "A framework for automatic optimization of MapReduce programs based on job parameter configurations." Kansas State University, 2011. http://hdl.handle.net/2097/12011.

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Master of Science<br>Department of Computing and Information Sciences<br>Mitchell L. Neilsen<br>Recently, cost-effective and timely processing of large datasets has been playing an important role in the success of many enterprises and the scientific computing community. Two promising trends ensure that applications will be able to deal with ever increasing data volumes: first, the emergence of cloud computing, which provides transparent access to a large number of processing, storage and networking resources; and second, the development of the MapReduce programming model, which provides a high
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Silva, Balocchi Erika Fernanda. "Análisis y comparación entre el motor de bases de datos orientado a columnas Infobright y el framework de aplicaciones distribuidas Hadoop en escenarios de uso de bases de datos analíticas." Tesis, Universidad de Chile, 2014. http://www.repositorio.uchile.cl/handle/2250/116665.

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Ingeniera Civil en Computación<br>Business Intelligence es la habilidad para transformar datos en información, y la información en conocimiento, de forma que se pueda optimizar la toma de decisiones en los negocios. Debido al aumento exponencial en la cantidad de datos disponibles en los ultimos años y a la complejidad de estos, las herramientas tradicionales de bases de datos y business intelligence pueden no dar a basto, suponiendo numerosos riesgos para las empresas. El objetivo de la presente memoria fue analizar el uso del framework de aplicaciones distribuidas Hadoop en comparación a l
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Alves, Francisco Marco Morais. "Framework for location based system sustained by mobile phone users." Master's thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/23817.

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mestrado em Engenharia de Computadores e Telemática<br>Vivemos na era da informação e da Internet das coisas e por isso nunca antes a informação teve tanto valor, ao mesmo tempo nunca existiu tão elevada troca de informação. Com toda esta quantidade de dados e com o aumento substancial do poder computacional, tem-se assistido a uma explosão de ferramentas para o processamento destes dados em tempo real. Um novo paradigma também emergiu, pelo facto de que muita dessa informação tem meta informação da qual é possível extrair conhecimento adicional quando enriquecida. No caso dos operadores de
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Books on the topic "Hadoop framework"

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Practical Hadoop Ecosystem: A Definitive Guide to Hadoop-Related Frameworks and Tools. Apress, 2016.

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Campbell, John, Joey Huston, and Frank Krauss. The Black Book of Quantum Chromodynamics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199652747.001.0001.

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The LHC (Large Hadron Collider) will serve as the energy frontier for high-energy physics for the next 20 years. The highlight of the LHC running so far has been the discovery of the Higgs boson, but the LHC programme has also consisted of the measurement of a myriad of other Standard Model processes, as well as searches for Beyond-the-Standard-Model physics, and the discrimination between possible new physics signatures and their Standard Model backgrounds. Essentially all of the physics processes at the LHC depend on quantum chromodynamics, or QCD, in the production, or in the decay stages,
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Book chapters on the topic "Hadoop framework"

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Jeyaraj, Rathinaraja, Ganeshkumar Pugalendhi, and Anand Paul. "Hadoop Framework." In Big Data with Hadoop MapReduce. Apple Academic Press, 2020. http://dx.doi.org/10.1201/9780429321733-2.

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Wadkar, Sameer, and Madhu Siddalingaiah. "Getting Started with the Hadoop Framework." In Pro Apache Hadoop. Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-4864-4_3.

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Antonellis, Panagiotis, Christos Makris, and Georgios Pispirigos. "Distributed XML Filtering Using HADOOP Framework." In Algorithmic Aspects of Cloud Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29919-8_6.

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Rahul, P. K., and T. GireeshKumar. "A Novel Authentication Framework for Hadoop." In Advances in Intelligent Systems and Computing. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2126-5_37.

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Filaly, Youness, Nisrine Berros, Hamza Badri, Fatna El mendil, and Younes El Bouzekri EL Idrissi. "Security of Hadoop Framework in Big Data." In Artificial Intelligence and Smart Environment. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26254-8_103.

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Akhgarnush, Eljar, Lars Broeckers, and Thorsten Jakoby. "Hadoop: A Standard Framework for Computer Cluster." In The Impact of Digital Transformation and FinTech on the Finance Professional. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23719-6_18.

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Prabhat, Parmanand, Syed Umar, Misganu Tuse Abetu, and Tariku Birhanu Yadesa. "A Novel Hadoop-Based Virtualization Automated Framework." In Proceedings of 6th International Conference on Recent Trends in Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4501-0_4.

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Kumar, Pushpendra, and Ramjeevan Singh Thakur. "A Framework for Weblog Data Analysis Using HIVE in Hadoop Framework." In Lecture Notes in Networks and Systems. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8198-9_45.

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Sahoo, Manuj Subhankar, and Pallav Kumar Baruah. "HBasechainDB – A Scalable Blockchain Framework on Hadoop Ecosystem." In Supercomputing Frontiers. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-69953-0_2.

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Jha, Ritesh, V. Bhattacharya, Vinit Singh, and I. Mukherjee. "K-NN Classification for Large Dataset in Hadoop Framework." In Nanoelectronics, Circuits and Communication Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2854-5_43.

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Conference papers on the topic "Hadoop framework"

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Liu, Xinyuan, Devki Nandan Jha, Yinhao Li, Mutaz Barika, Umit Demirbaga, and Rajiv Ranjan. "Automate: Automatic Anomaly Detection and Root Cause Analysis Framework for Hadoop." In 2024 International Conference on Meta Computing (ICMC). IEEE, 2024. https://doi.org/10.1109/icmc60390.2024.00030.

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Vemula, Sridhar, and Christopher Crick. "Hadoop Image Processing Framework." In 2015 IEEE International Congress on Big Data (BigData Congress). IEEE, 2015. http://dx.doi.org/10.1109/bigdatacongress.2015.80.

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Valliyammai, C., S. Ephina Thendral, V. Muthuvalliammai, S. Anandhavalli, and K. M. Madhumathi. "Hadoop framework based ubiquitous learning." In 2015 Seventh International Conference on Advanced Computing(ICoAC). IEEE, 2015. http://dx.doi.org/10.1109/icoac.2015.7562801.

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Fanibhare, Vaibhav, and Vijay Dahake. "SmartGrids: MapReduce framework using Hadoop." In 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2016. http://dx.doi.org/10.1109/spin.2016.7566727.

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Gavahane, Mayura, Derick Sequeira, Abhishek Pandey, and Anush Shetty. "Anti-phishing using Hadoop-framework." In 2015 International Conference on Technologies for Sustainable Development (ICTSD). IEEE, 2015. http://dx.doi.org/10.1109/ictsd.2015.7095870.

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Moon, Sangwhan, Jaehwan Lee, and Yang Suk Kee. "Introducing SSDs to the Hadoop MapReduce Framework." In 2014 IEEE 7th International Conference on Cloud Computing (CLOUD). IEEE, 2014. http://dx.doi.org/10.1109/cloud.2014.45.

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Pereira, Oscar Mortagua, Micael Capitao, Diogo Domingues Regateiro, Rui L. Aguiar, and Joao Bica Osorio. "Mediator framework for inserting xDRs into Hadoop." In 2016 IEEE Symposium on Computers and Communication (ISCC). IEEE, 2016. http://dx.doi.org/10.1109/iscc.2016.7543795.

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Johri, Prashant, Arun Kumar, Sanjoy Das, and Sanchita Arora. "Security framework using Hadoop for big data." In 2017 International Conference on Computing, Communication and Automation (ICCCA). IEEE, 2017. http://dx.doi.org/10.1109/ccaa.2017.8229813.

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Odia, Trust, Sanjay Misra, and Adewole Adewumi. "Evaluation of hadoop/mapreduce framework migration tools." In 2014 Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE). IEEE, 2014. http://dx.doi.org/10.1109/apwccse.2014.7053870.

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Jena, Bibhudutta, Pradeep Kumar Kanaujia, Siddharth Rautaray, and Manjusha Pandey. "Improvising block placement policy in HADOOP framework." In 2017 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2017. http://dx.doi.org/10.1109/iccci.2017.8117743.

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