Hadoop Ecosystem and Its Analysis on Tweets

Uzunkaya, Can; Ensari, Tolga; Kavurucu, Yusuf

doi:10.1016/j.sbspro.2015.06.429

Cited by 27 publications

(10 citation statements)

References 1 publication

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“…The last specification of big-data analytics is to efficiently become familiar with the rapidly progressing workload notations. Big-data analytics frameworks differ from traditional healthcare processing systems with respect to how they process big data [22] . In the current health care system, data is processed using traditional tools installed in a single stand-alone system like a desktop computer.…”

Section: Big Data Analytics Architecture For Health Informaticsmentioning

confidence: 99%

“…Hadoop can no longer be considered to be a monolithic single project, but rather an approach to data processing that radically differs from the traditional relational database model [23] . A more practical definition of the Hadoop ecosystem and framework is the following: open source tools, libraries, and methodologies for "big data" analysis in which a number of data sets are collected from different sources, i.e., Internet images, audios, videos, and sensor records as both structured and unstructured data to be processed [22] . shows the physical layout architecture of Hadoop which consists of MapReduce, HBase, and HDFS.…”

Section: Hadoop's Tools and Techniques For Big Datamentioning

confidence: 99%

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Big data analytics for healthcare industry: impact, applications, and tools

Kumar

Singh

2019

Big Data Min. Anal.

168

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In recent years, huge amounts of structured, unstructured, and semi-structured data have been generated by various institutions around the world and, collectively, this heterogeneous data is referred to as big data. The health industry sector has been confronted by the need to manage the big data being produced by various sources, which are well known for producing high volumes of heterogeneous data. Various big-data analytics tools and techniques have been developed for handling these massive amounts of data, in the healthcare sector. In this paper, we discuss the impact of big data in healthcare, and various tools available in the Hadoop ecosystem for handling it. We also explore the conceptual architecture of big data analytics for healthcare which involves the data gathering history of different branches, the genome database, electronic health records, text/imagery, and clinical decisions support system.

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Section: Big Data Analytics Architecture For Health Informaticsmentioning

confidence: 99%

Section: Hadoop's Tools and Techniques For Big Datamentioning

confidence: 99%

Big data analytics for healthcare industry: impact, applications, and tools

Kumar

Singh

2019

Big Data Min. Anal.

168

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“…Hadoop is an open-source software for reliable, scalable, distributed computing on commodity hardware [27,56]. Hadoop is one of the most widely used distributed processing frameworks developed to address the challenges of big data.…”

Section: Distributed Processing Of Geospatial Datamentioning

confidence: 99%

Multi-dimensional geospatial data mining in a distributed environment using MapReduce

Alkathiri

Abdul²,

Potdar³

2019

J Big Data

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Data mining and machine learning techniques for processing raster data consider a single spectral band of data at a time. The individual results are combined to obtain the final output. The essence of related multi-spectral information is lost when the bands are considered independently. The proposed platform is based on Apache Hadoop ecosystem and supports performing analysis on large amounts of multispectral raster data using MapReduce. A novel technique of transforming the spectral space to the geometrical space is also proposed. The technique allows to consider multiple bands coherently. The results of clustering 10 6 pixels for multiband imagery with widely used GIS software have been tested and other machine learning methods are planned to be incorporated in the platform. The platform is scalable to support tens of spectral bands. The results from our platform were found to be better and are also available faster due to application of distributed processing.

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“…It will get copied to next datablock from the source datablock of the pipeline. The data will be propagated in the same fashion till the last datablock in the pipeline (Uzunkaya et al, 2015;Jach et al, 2015). The length of the pipeline is equivalent to replication factor which we have configured in the hdfs-site.xml file.…”

Section: ) Write Operationmentioning

confidence: 99%

Hadoop distributed file system mechanism for processing of large datasets across computers cluster using programming techniques

Edwards

Brain

Joly

et al. 2019

irjmis

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In this paper, we have proved that the HDFS I/O operations performance is getting increased by integrating the set associativity in the cache design and changing the pipeline topology using fully connected digraph network topology. In read operation, since there is huge number of locations (words) at cache compared to direct mapping the chances of miss ratio is very low, hence reducing the swapping of the data between main memory and cache memory. This is increasing the memory I/O operations performance. In Write operation instead of using the sequential pipeline we need to construct the fully connected graph using the data blocks listed from the NameNode metadata. In sequential pipeline, the data is getting copied to source node in the pipeline. Source node will copy the data to next data block in the pipeline. The same copy process will continue until the last data block in the pipeline. The acknowledgment process has to follow the same process from last block to source block. The time required to transfer the data to all the data blocks in the pipeline and the acknowledgment process is almost 2n times to data copy time from one data block to another data block (if the replication factor is n).

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Hadoop Ecosystem and Its Analysis on Tweets

Cited by 27 publications

References 1 publication

Big data analytics for healthcare industry: impact, applications, and tools

Big data analytics for healthcare industry: impact, applications, and tools

Multi-dimensional geospatial data mining in a distributed environment using MapReduce

Hadoop distributed file system mechanism for processing of large datasets across computers cluster using programming techniques

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