Brain big data processing with massively parallel computing technology: challenges and opportunities

2016 ◽  
Vol 47 (3) ◽  
pp. 405-420 ◽  
Author(s):  
Dan Chen ◽  
Yangyang Hu ◽  
Chang Cai ◽  
Ke Zeng ◽  
Xiaoli Li
Keyword(s):  
Big Data ◽  
Parallel Computing ◽  
Data Processing ◽  
Massively Parallel ◽  
Computing Technology ◽  
Big Data Processing ◽  
Challenges And Opportunities ◽  
Massively Parallel Computing

Trends and Technologies in Big Data Processing

2020 ◽  
pp. 17-42
Author(s):  
Amitava Choudhury ◽  
Kalpana Rangra
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Processing ◽  
Real Time ◽  
Computing Technology ◽  
Time Data ◽  
Big Data Processing ◽  
Real Time Data ◽  
Real Time Data Processing

Data type and amount in human society is growing at an amazing speed, which is caused by emerging new services such as cloud computing, internet of things, and location-based services. The era of big data has arrived. As data has been a fundamental resource, how to manage and utilize big data better has attracted much attention. Especially with the development of the internet of things, how to process a large amount of real-time data has become a great challenge in research and applications. Recently, cloud computing technology has attracted much attention to high performance, but how to use cloud computing technology for large-scale real-time data processing has not been studied. In this chapter, various big data processing techniques are discussed.

Challenges and Opportunities in Big Data Processing

Big Data ◽  
2016 ◽  
pp. 2074-2097 ◽  
Author(s):  
Jaroslav Pokorny ◽  
Bela Stantic
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Data Management ◽  
File Systems ◽  
Distributed File Systems ◽  
Big Data Processing ◽  
Data Management Systems ◽  
Scalable Systems ◽  
Challenges And Opportunities ◽  
Special Category

The development and extensive use of highly distributed and scalable systems to process Big Data have been widely considered. New data management architectures, e.g. distributed file systems and NoSQL databases, are used in this context. However, features of Big Data like their complexity and data analytics demands indicate that these concepts solve Big Data problems only partially. A development of so called NewSQL databases is highly relevant and even special category of Big Data Management Systems is considered. In this work we will discuss these trends and evaluate some current approaches to Big Data processing, identify the current challenges, and suggest possible research directions.

BIG DATA PROCESSING: BIG CHALLENGES AND OPPORTUNITIES

2012 ◽  
Vol 13 (03n04) ◽  
pp. 1250009 ◽  
Author(s):  
CHANGQING JI ◽  
YU LI ◽  
WENMING QIU ◽  
YINGWEI JIN ◽  
YUJIE XU ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Data Management ◽  
Data Storage ◽  
Large Scale ◽  
Distributed Applications ◽  
Big Data Processing ◽  
Cloud Data ◽  
Management Platform ◽  
Challenges And Opportunities

With the rapid growth of emerging applications like social network, semantic web, sensor networks and LBS (Location Based Service) applications, a variety of data to be processed continues to witness a quick increase. Effective management and processing of large-scale data poses an interesting but critical challenge. Recently, big data has attracted a lot of attention from academia, industry as well as government. This paper introduces several big data processing techniques from system and application aspects. First, from the view of cloud data management and big data processing mechanisms, we present the key issues of big data processing, including definition of big data, big data management platform, big data service models, distributed file system, data storage, data virtualization platform and distributed applications. Following the MapReduce parallel processing framework, we introduce some MapReduce optimization strategies reported in the literature. Finally, we discuss the open issues and challenges, and deeply explore the research directions in the future on big data processing in cloud computing environments.

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