A fast search strategy to optimise path founding in big data graph computing environments

2017 ◽  
Vol 13 (2) ◽  
pp. 139 ◽  
Author(s):  
Wentian Qu ◽  
Dawei Sun
Keyword(s):  
Big Data ◽  
Search Strategy ◽  
Fast Search ◽  
Computing Environments ◽  
Data Graph ◽  
Graph Computing

Challenges and Cloud Computing Environments Towards Big Data

2014 ◽  
pp. 203-208
Author(s):  
Kiran Kumar S V N Madupu
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Technology Development ◽  
Computing Environments ◽  
Modern Technologies

Big Data has terrific influence on scientific discoveries and also value development. This paper presents approaches in data mining and modern technologies in Big Data. Difficulties of data mining as well as data mining with big data are discussed. Some technology development of data mining as well as data mining with big data are additionally presented.

scholarly journals Re-Stream: Real-time and energy-efficient resource scheduling in big data stream computing environments

2015 ◽  
Vol 319 ◽  
pp. 92-112 ◽  
Author(s):  
Dawei Sun ◽  
Guangyan Zhang ◽  
Songlin Yang ◽  
Weimin Zheng ◽  
Samee U. Khan ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Energy Efficient ◽  
Data Stream ◽  
Resource Scheduling ◽  
Stream Computing ◽  
Efficient Resource ◽  
Computing Environments ◽  
Time And Energy

An Efficient and Scalable Framework for Processing Remotely Sensed Big Data in Cloud Computing Environments

2019 ◽  
Vol 57 (7) ◽  
pp. 4294-4308 ◽  
Author(s):  
Jin Sun ◽  
Yi Zhang ◽  
Zebin Wu ◽  
Yaoqin Zhu ◽  
Xianliang Yin ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Remotely Sensed ◽  
Computing Environments

A Hierarchical Hadoop Framework to Handle Big Data in Geo-Distributed Computing Environments

2018 ◽  
Vol 11 (1) ◽  
pp. 16-47 ◽  
Author(s):  
Orazio Tomarchio ◽  
Giuseppe Di Modica ◽  
Marco Cavallo ◽  
Carmelo Polito
Keyword(s):  
Big Data ◽  
Data Centers ◽  
Poor Performance ◽  
Communication Technologies ◽  
Sources Of Information ◽  
The Poor ◽  
The Social ◽  
Computing Environments ◽  
Definition Of ◽  
Hadoop Framework

Advances in the communication technologies, along with the birth of new communication paradigms leveraging on the power of the social, has fostered the production of huge amounts of data. Old-fashioned computing paradigms are unfit to handle the dimensions of the data daily produced by the countless, worldwide distributed sources of information. So far, the MapReduce has been able to keep the promise of speeding up the computation over Big Data within a cluster. This article focuses on scenarios of worldwide distributed Big Data. While stigmatizing the poor performance of the Hadoop framework when deployed in such scenarios, it proposes the definition of a Hierarchical Hadoop Framework (H2F) to cope with the issues arising when Big Data are scattered over geographically distant data centers. The article highlights the novelty introduced by the H2F with respect to other hierarchical approaches. Tests run on a software prototype are also reported to show the increase of performance that H2F is able to achieve in geographical scenarios over a plain Hadoop approach.

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