Cloudet

Author:

Baciu George1,Li Chenhui2,Wang Yunzhe2,Zhang Xiujun3

Affiliation:

1. GAMA Lab, Department of Computing, Hong Kong Polytechnic University, Hung Hom, Hong Kong

2. Hong Kong Polytechnic University, Hung Hom, Hong Kong

3. Shenzhen University, Shenzhen, China

Abstract

Streaming data cognition has become a dominant problem in interactive visual analytics for event detection, meteorology, cosmology, security, and smart city applications. In order to interact with streaming data patterns in an elastic cloud environment, we present a new elastic framework for big data visual analytics in the cloud, the Cloudet. The Cloudet is a self-adaptive cloud-based platform that treats both data and compute nodes as elastic objects. The main objective is to readily achieve the scalability and elasticity of cloud computing platforms in order to process large streaming data and adapt to potential interactions between data stream features. Our main contributions include a robust cloud-based framework called the Cloudet. This is a cloud profile manager that attempts to optimize resource parameters in order to achieve expressivity, scalability, reliability, and the proper aggregation of the compute nodes and data streams into several density maps for the purpose of dynamic visualization.

Publisher

IGI Global

Subject

Artificial Intelligence,Human-Computer Interaction,Software

Cited by 11 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive Computing (Plenary Panel Report-II);2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC);2021-10-29

2. Streaming Data and Data Streams;Wiley StatsRef: Statistics Reference Online;2021-05-17

3. Cognitive Visual Analytics of Multi-Dimensional Cloud System Monitoring Data;Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing;2021

4. A Novel Approach With Dynamic Fuzzy Inference for Big Data Classification Problems;Advances in Computational Intelligence and Robotics;2020

5. Abstract Intelligence;Cognitive Analytics;2020

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