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期刊名称: IEEE Access
Volume:2    Page:652-687
ISSN:2169-3536

Toward Scalable Systems for Big Data Analytics: A Technology Tutorial期刊论文

作者: Han Hu Yonggang Wen Tat-Seng Chua Xuelong Li
DOI:10.1109/ACCESS.2014.2332453

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页码: 652-687
被引频次: 295
出版者: IEEE,IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
期刊名称: IEEE Access
ISSN: 2169-3536
语言: English
摘要: Recent technological advancements have led to a deluge of data from distinctive domains (e.g., health care and scientific sensors, user-generated data, Internet and financial companies, and supply chain systems) over the past two decades. The term big data was coined to capture the meaning of this emerging trend. In addition to its sheer volume, big data also exhibits other unique characteristics as compared with traditional data. For instance, big data is commonly unstructured and require more real-time analysis. This development calls for new system architectures for data acquisition, transmission, storage, and large-scale data processing mechanisms. In this paper, we present a literature survey and system tutorial for big data analytics platforms, aiming to provide an overall picture for nonexpert readers and instill a do-it-yourself spirit for advanced audiences to customize their own big-data solutions. First, we present the definition of big data and discuss big data challenges. Next, we present a systematic framework to decompose big data systems into four sequential modules, namely data generation, data acquisition, data storage, and data analytics. These four modules form a big data value chain. Following that, we present a detailed survey of numerous approaches and mechanisms from research and industry communities. In addition, we present the prevalent Hadoop framework for addressing big data challenges. Finally, we outline several evaluation benchmarks and potential research directions for big data systems.
相关主题: Supply chain management, Scalability, Data acquisition, Medical services, Sensor phenomena and characterization, Big data, Sensor systems, Real-time systems, Information analysis, Cloud computing, Big data analytics, Data analytics, Data storage, Hadoop, data analytics, INDUSTRY, PERFORMANCE, COMPUTER SCIENCE, INFORMATION SYSTEMS, FEATURE GENERATION, NETWORKS, ENERGY-EFFICIENT, TELECOMMUNICATIONS, MAPREDUCE, OF-THE-ART, CHALLENGES, ENGINEERING, ELECTRICAL & ELECTRONIC, MAP-REDUCE, data storage, FRAMEWORK, data acquisition, cloud computing,

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