Efficient Data Synchronization on Mobile Devices in Big Data

Author(s):  
Ewa Niewiadomska-Szynkiewicz ◽  
Michał P. Karpowicz

Progress in life, physical sciences and technology depends on efficient data-mining and modern computing technologies. The rapid growth of data-intensive domains requires a continuous development of new solutions for network infrastructure, servers and storage in order to address Big Datarelated problems. Development of software frameworks, include smart calculation, communication management, data decomposition and allocation algorithms is clearly one of the major technological challenges we are faced with. Reduction in energy consumption is another challenge arising in connection with the development of efficient HPC infrastructures. This paper addresses the vital problem of energy-efficient high performance distributed and parallel computing. An overview of recent technologies for Big Data processing is presented. The attention is focused on the most popular middleware and software platforms. Various energy-saving approaches are presented and discussed as well.


Author(s):  
Haoliang Wang ◽  
Wei Liu ◽  
Tolga Soyata

The amount of data acquired, stored, and processed annually over the Internet has exceeded the processing capabilities of modern computer systems, including supercomputers with multiple-Petaflop processing power, giving rise to the term Big Data. Continuous research efforts to implement systems to cope with this insurmountable amount of data are underway. The authors introduce the ongoing research in three different facets: 1) in the Acquisition front, they introduce a concept that has come to the forefront in the past few years: Internet-of-Things (IoT), which will be one of the major sources for Big Data generation in the following decades. The authors provide a brief survey of IoT to understand the concept and the ongoing research in this field. 2) In the Cloud Storage and Processing front, they provide a survey of techniques to efficiently store the acquired Big Data in the cloud, index it, and get it ready for processing. While IoT relates primarily to sensor nodes and thin devices, the authors study this storage and processing aspect of Big Data within the framework of Cloud Computing. 3) In the Mobile Access front, they perform a survey of existing infrastructures to access the Big Data efficiently via mobile devices. This survey also includes intermediate devices, such as a Cloudlet, to accelerate the Big Data collection from IoT and access to Big Data for applications that require response times that are close to real-time.


Author(s):  
Suriya Murugan ◽  
Sumithra M. G.

Cognitive radio has emerged as a promising candidate solution to improve spectrum utilization in next generation wireless networks. Spectrum sensing is one of the main challenges encountered by cognitive radio and the application of big data is a powerful way to solve various problems. However, for the increasingly tense spectrum resources, the prediction of cognitive radio based on big data is an inevitable trend. The signal data from various sources is analyzed using the big data cognitive radio framework and efficient data analytics can be performed using different types of machine learning techniques. This chapter analyses the process of spectrum sensing in cognitive radio, the challenges to process spectrum data and need for dynamic machine learning algorithms in decision making process.


2019 ◽  
Vol 30 (12) ◽  
pp. 2677-2691 ◽  
Author(s):  
Qiufen Xia ◽  
Zichuan Xu ◽  
Weifa Liang ◽  
Shui Yu ◽  
Song Guo ◽  
...  

Author(s):  
Libin Tang ◽  
Harish Subramony ◽  
Weian Chen ◽  
Jimin Ha ◽  
Hassnaa Moustafa ◽  
...  

Author(s):  
Brian Tierney ◽  
Ezra Kissel ◽  
Martin Swany ◽  
Eric Pouyoul

2005 ◽  
Vol 10 (4) ◽  
pp. 475-486 ◽  
Author(s):  
Huaping Shen ◽  
Mohan Kumar ◽  
Sajal K. Das ◽  
Zhijun Wang

2014 ◽  
Vol 926-930 ◽  
pp. 3621-3624 ◽  
Author(s):  
Er Nuan Wang

With the coming of the era of big data, various types of databases are emerging, the data synchronization between heterogeneous databases is becoming more important, Goldengate has a very good performance, and it is used widely now. Goldengate can be used for data synchronization from Oracle to Oracle and from Oracle to other databases.


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