scholarly journals Location, Location, Location: Data-Intensive Distributed Computing in the Cloud

2013 ◽  
Author(s):  
Michael Luckeneder ◽  
Adam Barker
Keyword(s):  
Distributed Computing ◽  
Data Intensive ◽  
Location Data

scholarly journals The home-range concept: are traditional estimators still relevant with modern telemetry technology?

2010 ◽  
Vol 365 (1550) ◽  
pp. 2221-2231 ◽  
Author(s):  
John G. Kie ◽  
Jason Matthiopoulos ◽  
John Fieberg ◽  
Roger A. Powell ◽  
Francesca Cagnacci ◽  
...  
Keyword(s):  
Home Range ◽  
New Technologies ◽  
Animal Movement ◽  
Space Use ◽  
Ease Of Use ◽  
Home Ranges ◽  
Data Intensive ◽  
Use Patterns ◽  
Location Data ◽  
Utilization Distributions

Recent advances in animal tracking and telemetry technology have allowed the collection of location data at an ever-increasing rate and accuracy, and these advances have been accompanied by the development of new methods of data analysis for portraying space use, home ranges and utilization distributions. New statistical approaches include data-intensive techniques such as kriging and nonlinear generalized regression models for habitat use. In addition, mechanistic home-range models, derived from models of animal movement behaviour, promise to offer new insights into how home ranges emerge as the result of specific patterns of movements by individuals in response to their environment. Traditional methods such as kernel density estimators are likely to remain popular because of their ease of use. Large datasets make it possible to apply these methods over relatively short periods of time such as weeks or months, and these estimates may be analysed using mixed effects models, offering another approach to studying temporal variation in space-use patterns. Although new technologies open new avenues in ecological research, our knowledge of why animals use space in the ways we observe will only advance by researchers using these new technologies and asking new and innovative questions about the empirical patterns they observe.

A data intensive distributed computing architecture for “Grid” applications

2000 ◽  
Vol 16 (5) ◽  
pp. 473-481 ◽  
Author(s):  
Brian Tierney ◽  
William Johnston ◽  
Jason Lee ◽  
Mary Thompson
Keyword(s):  
Distributed Computing ◽  
Computing Architecture ◽  
Data Intensive ◽  
Grid Applications

scholarly journals Nakamoto Consensus to Accelerate Supervised Classification Algorithms for Multiparty Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhen Zhang ◽  
Bing Guo ◽  
Yan Shen ◽  
Chengjie Li ◽  
Xinhua Suo ◽  
...  
Keyword(s):  
Distributed Computing ◽  
Supervised Classification ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Classification Algorithms ◽  
Distributed Data ◽  
Data Intensive ◽  
Private Data ◽  
Cooperation Mechanism ◽  
Mathematical Formulas

Bitcoin mining consumes tremendous amounts of electricity to solve the hash problem. At the same time, large-scale applications of artificial intelligence (AI) require efficient and secure computing. There are many computing devices in use, and the hardware resources are highly heterogeneous. This means a cooperation mechanism is needed to realize cooperation among computing devices, and a good calculation structure is required in the case of data dispersion. In this paper, we propose an architecture where devices (also called nodes) can reach a consensus on task results using off-chain smart contracts and private data. The proposed distributed computing architecture can accelerate computing-intensive and data-intensive supervised classification algorithms with limited resources. This architecture can significantly increase privacy protection and prevent leakage of distributed data. Our proposed architecture can support heterogeneous data, making computing on each device more efficient. We used mathematical formulas to prove the correctness and robustness of our system and deduced the condition to stop a given task. In the experiments, we transformed Bitcoin hash collision into distributed computing on several nodes and evaluated the training and prediction accuracy for handwritten digit images (MNIST). The experimental results demonstrate the effectiveness of the proposed method.

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