Apache Ambari

Wadkar, Sameer; Siddalingaiah, Madhu

doi:10.1007/978-1-4302-4864-4_20

Cited by 15 publications

(7 citation statements)

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“…Google MapReduce provides an efficient and effective framework for parallel processing of big data. Moreover, the Google file system (GFS) [146] can be used along with this framework to provide distributed, reliable, and efficient data storage for big datasets. Furthermore, MapReduce can be employed to provide an abstraction of problems such as distributed programming and support effective and efficient parallelization.…”

Section: Rq5) What Are the Shortcomings And Problems Of The Existing Methods And Techniques?mentioning

confidence: 99%

Support Vector Machines in Big Data Classification: A Systematic Literature Review

Almaspoor

Safaei

Salajegheh

et al. 2021

Preprint

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Classification is one of the most important and widely used issues in machine learning, the purpose of which is to create a rule for grouping data to sets of pre-existing categories is based on a set of training sets. Employed successfully in many scientific and engineering areas, the Support Vector Machine (SVM) is among the most promising methods of classification in machine learning. With the advent of big data, many of the machine learning methods have been challenged by big data characteristics. The standard SVM has been proposed for batch learning in which all data are available at the same time. The SVM has a high time complexity, i.e., increasing the number of training samples will intensify the need for computational resources and memory. Hence, many attempts have been made at SVM compatibility with online learning conditions and use of large-scale data. This paper focuses on the analysis, identification, and classification of existing methods for SVM compatibility with online conditions and large-scale data. These methods might be employed to classify big data and propose research areas for future studies. Considering its advantages, the SVM can be among the first options for compatibility with big data and classification of big data. For this purpose, appropriate techniques should be developed for data preprocessing in order to covert data into an appropriate form for learning. The existing frameworks should also be employed for parallel and distributed processes so that SVMs can be made scalable and properly online to be able to handle big data.

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Section: Rq5) What Are the Shortcomings And Problems Of The Existing Methods And Techniques?mentioning

confidence: 99%

Support Vector Machines in Big Data Classification: A Systematic Literature Review

Almaspoor

Safaei

Salajegheh

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The hosts , VSAS and video stream processing modules. This server also hosts Ambari Server [ 45 ]. The Agent-1 deploys HDFS Name Node [ 4 ], Zookeeper Server [ 46 ], Yarn Resource Manager [ 47 ] and Spark2 History Server [ 5 ].…”

Section: Tornado Evaluationmentioning

confidence: 99%

TORNADO: Intermediate Results Orchestration Based Service-Oriented Data Curation Framework for Intelligent Video Big Data Analytics in the Cloud

Alam

Lee

2020

Sensors

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In the recent past, the number of surveillance cameras placed in the public has increased significantly, and an enormous amount of visual data is produced at an alarming rate. Resultantly, there is a demand for a distributed system for video analytics. However, a majority of existing research on video analytics focuses on improving video content management and rely on a traditional client/server framework. In this paper, we develop a scalable and flexible framework called TORNADO on top of general-purpose big data technologies for intelligent video big data analytics in the cloud. The proposed framework acquires video streams from device-independent data-sources utilizing distributed streams and file management systems. High-level abstractions are provided to allow the researcher to develop and deploy video analytics algorithms and services in the cloud under the as-a-service paradigm. Furthermore, a unified IR Middleware has been proposed to orchestrate the intermediate results being generated during video big data analytics in the cloud. We report results demonstrating the performance of the proposed framework and the viability of its usage in terms of better scalability, less fault-tolerance, and better performance.

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“…The Apache Ambari initially started as sub-project of Hadoop to provide system administrators facilities of provision, monitoring and management of Hadoop clusters. Apache Ambari [28] is now a top-level project managed by its own community to facilitate integration of Hadoop with prevailing enterprise infrastructure. The Apache Flume [29] and Apache Sqoop [30] are two reliable, distributed and available systems to efficiently collect, move, and aggregate and huge chunks of log data from number of various sources to a centralized data store.…”

Section: Toolsmentioning

confidence: 99%

Role of Apache Software Foundation in Big Data Projects

Akhtar

2020

Preprint

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With the increase in amount of Big Data being generated each year, tools and technologies developed and used for the purpose of storing, processing and analyzing Big Data has also improved. Open-Source software has been an important factor in the success and innovation in the field of Big Data while Apache Software Foundation (ASF) has played a crucial role in this success and innovation by providing a number of state-of-the-art projects, free and open to the public. ASF has classified its project in different categories. In this report, projects listed under Big Data category are deeply analyzed and discussed with reference to one-of-the seven sub-categories defined. Our investigation has shown that many of the Apache Big Data projects are autonomous but some are built based on other Apache projects and some work in conjunction with other projects to improve and ease development in Big Data space.

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Apache Ambari

Cited by 15 publications

References 0 publications

Support Vector Machines in Big Data Classification: A Systematic Literature Review

Support Vector Machines in Big Data Classification: A Systematic Literature Review

TORNADO: Intermediate Results Orchestration Based Service-Oriented Data Curation Framework for Intelligent Video Big Data Analytics in the Cloud

Role of Apache Software Foundation in Big Data Projects

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