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Big Data in cloud data centers

Big Data in cloud data centers

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Big Data refers to a collection of massive volume of data that cannot be processed by conventional data processing tools and technologies. In recent years, the data production sources are enlarged noticeably, such as high-end streaming devices, wireless sensor networks, satellite, wearable Internet of Things devices. These data generation sources generate massive amount of data in continuous manner. Nowadays, Big Data analytics plays a significant role in various environments it includes business monitoring, healthcare applications, production development, research and development, share market prediction, business process, industrial applications, social network analysis, weather analysis and environmental monitoring. A data center is a facility composed of networked computers and storage that businesses or other organizations use to process, analyze, store and distribute huge volume of data. In recent years, cloud data centers have been used to store and process the Big Data. This chapter reviews various architectures to store and process the Big Data in cloud data centers. In addition, this chapter also describesthe challenges and applications of Big Data analytics in cloud data centers.

Chapter Contents:

  • 8.1 Introduction
  • 8.2 Needs for the architecture patterns and data sources for Big Data storage in cloud data centers
  • 8.3 Applications of Big Data analytics with cloud data centers
  • 8.3.1 Disease diagnosis
  • 8.3.2 Government organizations
  • 8.3.3 Social networking
  • 8.3.4 Computing platforms
  • 8.3.5 Environmental and natural resources
  • 8.4 State-of-the-art Big Data architectures for cloud data centers
  • 8.4.1 Lambda architecture
  • 8.4.1.1 Batch layer
  • 8.4.1.2 Speed layer
  • 8.4.1.3 Serving layer
  • 8.4.2 NIST Big Data Reference Architecture (NBDRA)
  • 8.4.2.1 System Orchestrator
  • 8.4.2.2 Data provider
  • 8.4.2.3 Data consumer
  • 8.4.2.4 Big DataApplication Provider
  • 8.4.2.5 Big Data framework provider
  • 8.4.3 Big Data Architecture for Remote Sensing
  • 8.4.3.1 Remote sensing Big Data Acquisition Unit
  • 8.4.3.2 Data processing unit
  • 8.4.3.3 Data analysis and decision unit
  • 8.4.4 The Service-On Line-Index-Data (SOLID) architecture
  • 8.4.4.1 Content tier
  • 8.4.4.2 Data layer and index layer
  • 8.4.4.3 Online layer, merge tier and service tier
  • 8.4.5 Semantic-based Architecture for Heterogeneous Multimedia Retrieval
  • 8.4.5.1 Multimedia semantic input
  • 8.4.5.2 Ontology semantic representation
  • 8.4.5.3 NoSQL-base Semantic Storage
  • 8.4.5.4 MapReducebased Heterogeneous Multimedia Retrieval
  • 8.4.6 LargeScale Security Monitoring Architecture
  • 8.4.6.1 Data presentation
  • 8.4.6.2 Data correlation
  • 8.4.7 Modular software architecture
  • 8.4.8 MongoDB-based Healthcare Data Management Architecture
  • 8.4.9 Scalable and Distributed Architecture for Sensor Data Collection, Storage and Analysis
  • 8.4.9.1 Data Harvesting Subsystem
  • 8.4.9.2 Data Storage Subsystem
  • 8.4.9.3 Data Analysis Subsystem
  • 8.4.10 Distributed parallel architecture for "Big Data"
  • 8.4.10.1 Different layers
  • 8.5 Challenges and potential solutions for Big Data analytics in cloud data centers
  • 8.6 Conclusion
  • References

Inspec keywords: computer centres; Big Data; cloud computing

Other keywords: cloud data centers; data generation sources; research and development; networked computers; social network analysis; share market prediction; data production sources; Big Data analytics; business process; environmental monitoring; weather analysis; production development; data processing tools; business monitoring; industrial applications; data storage; healthcare applications; data analysis

Subjects: Information networks; Data handling techniques; Internet software

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