big data
2016-05-09 18:46:19 0 举报AI智能生成
大数据是指规模庞大、复杂多样且难以通过传统方法进行捕捉、管理和处理的数据集合。这些数据通常包括结构化数据和非结构化数据，它们以高速率生成并积累。大数据的特点可以用“3V”来描述：大容量、高速度和多样性。此外，还有两个重要的特性，即价值和真实性。大数据具有巨大的潜力，可以用于各种领域，如商业智能、科学研究、医疗保健等。通过对大数据的分析，人们可以获得有关趋势、模式和关联的深入洞察，从而做出更明智的决策。然而，有效地处理和分析大数据也带来了挑战，需要使用先进的技术和工具来应对数据的复杂性和规模。因此，大数据已成为当今信息时代的重要组成部分，对各行各业产生了深远的影响。
作者其他创作
大纲/内容
Design and construction of reference architecture
design of the reference architecture
data sources
mobility
in situ
streaming
structure
structured
unstructured
semi-structured
data extraction
data loading and pre-processing
data processing
data analysis
data loading and transformation
interfacing and visualization
visualization application
dashboarding application
end user application
data storage
construction of the reference architecture
Facebook
LinkedIn
Twitter
Netflix
BlockMon
Network Measurement
FIU-Miner
Review of big data technologies
Classification of big data technologies and commercial products/services
data collection and storage
data analysis
benchmarking
novel technology frameworks
virtualization
cloud-based solutions
commercial products and services
Survey of related work
stream processing
Remote Direct Memory Access (RDMA)
Processing Elements (PE)
Processing Nodes (PN)
directed acyclic graph (DAG)
Service Level Agreement (SLA)
graph models
Resource Description Framework (RDF)
XGDBench
OrientDB
business intelligence and visualization
Extract-Transform-Load (ETL)
computational methods for improving performance of visual analytics
big data benchmarking
BigBench
LinkBench
BigDataBench
virtualization and cloud-based solutions
Full virtualization
Paravirtualization
OS-level virtualization
Native virtualization
full- and paravirtualization models
paravirtualization and OS-based virtualization
new technology frameworks
ASTERIX
Flink/Stratosphere
commercial services
SaaS
IaaS
Analytics infrastructure
DaaS
Material and methods
goals
design technology independent reference architecture
classify related technologies and products/services
research questions
What elements comprise reference architecture for big data systems?
How to classify technologies and products/services of big data system?
Theoretical background
big data research
short survey of state-of-the-art in architectures
comprehensive survey of big data
survey focused on big data opportunities and challenges
framework for big data mining
reference architecture for big data systems
multi-dimensional classification space and five types of reference architecture
service-oriented reference architecture
big data cases
Discussion
online layer
merge layer
data layer
index layer
service layer
concentration on selected technologies
reference architecture should be evaluated with a real big data use case
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