A Survey of Large Scale Data Management Approaches in Cloud Environments

By Sherif Sakr, Anna Yun-Wen Liu, D. Batista, M. Alomari. IEEE Communications Surveys and Tutorials, 2011

472 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/7dfd49a28b3eff74e29fd11aeecd1e9802c5c95a

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In Computer Networks and Communications

In Computer Vision and Pattern Recognition

In Information Systems

Citations in the corpus

Heterogeneity in Mobile Cloud Computing: Taxonomy and Open Challenges (2014, background): “Integrating huge distributed data and providing virtually unified storage for mobile users is becoming more complicated with the ever increasing heterogeneity in MCC [19].”
Data management in cloud environments: NoSQL and NewSQL data stores (2013, methodology): “Several surveys have addressed the NoSQL domain [10-14]; nevertheless, this survey is different because it focuses on the comparison of available NoSQL and NewSQL solutions over a number of dimensions.”
Cloud-Based Augmentation for Mobile Devices: Motivation, Taxonomies, and Open Challenges (2013, background): “Live migration of VMs between distributed cloud-based servers (especially for distant servers) is a crucial requir ment in successfully adopting CMA solutions in MCC, considering wireless network bandwidth and intermittency, and mobility limitations.”
Sensing as a service model for smart cities supported by Internet of Things (2013, unknown): “As we discussed in Section 3, ESPs can help the sensor data consumers to orchestrate existing services into different data processing [34] and analysis work flows [35].”
Big Data Processing in Cloud Computing Environments (2012, unknown): “Amazon Simple Storage Service (S3)[8] is”
A Survey on Optical Interconnects for Data Centers (2012, background): “The applications that are hosted in the data center servers (e.g. cloud computing applications, search engines, etc.) are data-intensive and require high interaction between the servers in the data center [1].”
Data-intensive applications, challenges, techniques and technologies: A survey on Big Data (2014, methodology): “Higher level Big Data technologies include distributed file systems [148,32], distributed computational systems [136], massively parallel-processing (MPP) systems [185,181], data mining based on grid computing [34], cloud-based storage and computing resources [154], as well as granular computing and”
Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph (2015, background): “Thus, it is significant to meet their bulk storage requirements in big data management stage for experimental data bases, array storage for large-scale scientific computations, and large output files (Sakr et al., 2011).”
A Survey of Mobile Cloud Computing Application Models (2014, background): “Therefore, ad-hoc mobile cloud based systems/application models [18] and associated issues, such as mobility of cloud infrastructure and geo-distribution of service nodes [19], [20], are beyond the scope of this survey.”