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Predictive intelligence of reliable analytics in distributed computing environments
2020
Applied intelligence (Boston)
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computing environment. ...
Lack of knowledge in the underlying data distribution in distributed large-scale data can be an obstacle when issuing analytics & predictive modelling queries. ...
and storing query results among computing nodes in a distributed computing environment (including processed data or even raw data for analytics tasks). ...
doi:10.1007/s10489-020-01712-5
fatcat:r3h3ghaduzc45nwphf3km4ymxa
Tactical big data analytics
2014
Performance Evaluation Review
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We discuss tactical challenges of the Big Data analytics regarding the underlying data, application space, and computing environment, and present a comprehensive solution framework motivated by the relevant ...
For these two use cases, we introduce Big Data analytics and cloud computing solutions in a coherent framework that supports tactical data, application, and computing needs. ...
(IAI), in various DoD sponsored projects 1 , addresses some of the challenges pertaining to joint analytics, distributed storage, and content distribution in challenging (e.g., bandwidth limited) environments ...
doi:10.1145/2627534.2627561
fatcat:tl2tv7ekdvd25j3rkxq5ytuqlu
Reliable Fleet Analytics for Edge IoT Solutions
[article]
2021
arXiv
pre-print
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Artificial Intelligence of Things (AIoT) is the combination of Artificial Intelligence (AI) technologies and the IoT infrastructure to provide robust and efficient operations and decision making. ...
For the machine learning experiments, we forecast multivariate time series for predicting air quality in the respective rooms by using the models deployed in respective edge devices. ...
Thus, we introduce a robust and reliable fleet analytics framework that can be used in production environments. ...
arXiv:2101.04414v1
fatcat:2v6fe2ugbjbilcyiymy32kiziy
Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS
2021
Computational Intelligence and Neuroscience
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Intelligent management of IoT is required to maintain connectivity, improve Quality of Service (QoS), and reduce energy consumption in real time within dynamic environments. ...
The heterogeneity and complexity of the IoT in terms of dynamism and uncertainty complicate this landscape dramatically and introduce vulnerabilities. ...
the machines smarter for prediction and data analytics, leading to smarter decision-making in smart Computational Intelligence and Neuroscience environments. ...
doi:10.1155/2021/6805151
pmid:34589123
pmcid:PMC8476267
fatcat:2rl2s6qkxbcabpwpjwcac4z6oe
Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021)
2022
Forecasting
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Big data technology and predictive analytics exhibit advanced potential for business intelligence (BI), especially for decision-making. ...
Reasons for hotspots bursting in 2021 are explored. Finally, the research direction is predicted, and the advice is delivered to future researchers. ...
Conflicts of Interest: The authors declare no conflict of interest. Forecasting 2022, 4 ...
doi:10.3390/forecast4040042
fatcat:mg44yccqqbcthp5xtlml7vggpm
Sensor-driven Learning of Time-Dependent Parameters for Prescriptive Analytics
2020
IEEE Access
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of time-dependent parameters for prescriptive analytics models deployed in streaming computational environments. ...
The use of time-dependent parameters in prescriptive analytics models provide a more reliable and realistic representation of the complex and dynamic environment and the associated decision making process ...
In this paper, we propose an approach for sensor-driven learning of time-dependent parameters for prescriptive analytics models deployed in streaming computational environment. ...
doi:10.1109/access.2020.2994933
fatcat:qxggkp4fpnbcdc4w4whcjrvai4
The Application of Downhole Vibration Factor in Drilling Tool Reliability Big Data Analytics—A Review
2018
ASCE-ASME J of Risk & Uncertainty in Engineering SystemsPart B: Mechanical Engineering
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Specifically, this paper explores the application of vibration factor in reliability big data analytics covering tool lifetime/failure prediction, prognostics/diagnostics, condition monitoring (CM), and ...
Consequently, as one of the key factors to affect drilling tool reliability, the downhole vibration factor plays an essential role in the reliability analytics based on FRBD. ...
of reliability big data analytics topics. ...
doi:10.1115/1.4040407
fatcat:fxrpxpzp3jaljgcqgv42g2jba4
Smart Grid Architectural Designs
[chapter]
2012
Smart Grid
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COMPUTATIONAL INTELLIGENCE Computational intelligence is the term used to describe the advanced analytical tools needed to optimize the bulk power network. ...
Intelligent Grid Distribution Subsystem Component The distribution system is the fi nal stage in the transmission of power to end users. ...
doi:10.1002/9781118156117.ch1
fatcat:kb355x5y7fdx5afbcohmbziwtm
AI-driven resource management strategies for cloud computing systems, services, and applications
2024
World Journal of Advanced Engineering Technology and Sciences
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However, efficient resource management in cloud computing systems remains a major challenge due to the scalability, heterogeneity, and dynamic nature of these environments. ...
Algorithms for machine learning, reinforcement learning, predictive analytics, natural language processing, and genetic algorithms. ...
An overview and comparison of artificial intelligence-based resource management systems in cloud computing environments can be found in Table 1 . ...
doi:10.30574/wjaets.2024.11.2.0137
fatcat:vtq4f4uvsrdqvepcopee4ltxsi
Remote Big Data Management Tools, Sensing and Computing Technologies, and Visual Perception and Environment Mapping Algorithms in the Internet of Robotic Things
2022
Electronics
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and whether smart connected objects, situational awareness algorithms, and edge computing technologies configure IoRT systems and cloud robotics in relation to distributed task coordination through visual ...
, sensing and computing technologies, and visual perception and environment mapping algorithms. ...
Sensor accuracy is instrumental in IoT-enabled robotic swarms in terms of performance and reliability. ...
doi:10.3390/electronics12010022
fatcat:74fn3hdotncxxh4agsxcycbp7m
Real-Time High-Load Infrastructure Transaction Status Output Prediction Using Operational Intelligence and Big Data Technologies
2020
Electronics
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Transaction tracing in a distributed environment has been enhanced using machine learning and mathematical modelling. ...
Metrics and indicators for determining infrastructure load are given in the paper to obtain Operational intelligence and Site reliability insights. ...
Therefore, Operational Intelligence, Machine Learning and Big Data Analytics is an evolving trend in the era of cloud and quantum computing and has the potential to lead to high-quality data project implementation ...
doi:10.3390/electronics9040668
fatcat:vwmlmlqkhvejpbdxmdtwqw4rqq
Reliable capacity provisioning for distributed cloud/edge/fog computing applications
2017
2017 European Conference on Networks and Communications (EuCNC)
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. %20Data%20Science%20overview.pdf
Reliable Capacity Provisioning for Distributed Cloud/Edge/Fog Computing Applications -5/8
Reliable Capacity Provisioning for Distributed Cloud/Edge/Fog Computing ...
, data science and analytics, and intelligent automation. ...
doi:10.1109/eucnc.2017.7980667
dblp:conf/eucnc/OstbergBCEAFKDM17
fatcat:6hmjvvp3fveqvi73my6djbnpeu
PREDICTION AND EVALUATION OF THE MOLDS STATE USING THE TOOLS OF ARTICICIAL INTELLIGENCE
2014
Acta Metallurgica Slovaca - Conference
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The Department of Automation and Computing in Metallurgy, VSB-TU Ostrava has long dealt with the challenge of life of technical systems. ...
of monitoring and localization of crystallizer's desks, crystallizer lifetime prediction and prediction of blank defects. ...
Acknowledgments Authors are grateful for the support of experimental works by project the specific university research of Ministry of Education, Youth and Sports of the Czech Republic No. SP2013/49. ...
doi:10.12776/amsc.v4i0.222
fatcat:rwpgjop3ejbcnjvaovbmd3omcm
Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics
[article]
2020
arXiv
pre-print
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cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. ...
This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed ...
, predictive, and prescriptive risk analytics in edge computing
Table 2 : 2 Dynamic and self-adapting predictive cyber risk analytics based on different levels of cyber risk intelligence `] , a = Expected ...
arXiv:2005.12150v1
fatcat:2aexajsa3ze2fbnskqry7sayd4
Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems
[article]
2017
arXiv
pre-print
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With a higher capability of passengers' mobile devices and intra-vehicle processors, such a distributed edge computing architecture can leverage deep learning techniques for reliable mobile sensing in ...
However, realizing the true potential of ITSs requires ultra-low latency and reliable data analytics solutions that can combine, in real-time, a heterogeneous mix of data stemming from the ITS network ...
The prediction of driver behavior will reduce the computational overhead due to the reduction in the sensor readings of self-driving vehicles in the proximity of drivercontrolled vehicles. ...
arXiv:1712.04135v1
fatcat:pakkfrovvzcvnlzfkoeqmsxyla
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