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Utilizing semantic big data for realizing a national-scale infrastructure vulnerability analysis system
2016
Proceedings of the International Workshop on Semantic Big Data - SBD '16
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We present a vision of national-scale Infrastructure Vulnerability Analysis System (IVAS) leveraging Semantic Big Data (SBD) tools, Big Data, and Geographical Information Systems (GIS) tools. ...
visualization of analytic output to generate meaningful insights. ...
Modeling and performing simulation on these networks for vulnerability and cascading failure analysis are nicely aligned with the capabilities of emerging SBD technologies. ...
doi:10.1145/2928294.2928295
dblp:conf/sigmod/LeeCDS16
fatcat:biolcgbtojcfja72ryoiqcj5ku
Artificial intelligence and machine learning in dynamic cyber risk analytics at the edge
2020
SN Applied Sciences
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With the application of social science's grounded theory, the framework details a new process for a prototype of AI-enabled dynamic cyber risk analytics at the edge. ...
We explore the potential and practical challenges in the use of artificial intelligence (AI) in cyber risk analytics, for improving organisational resilience and understanding cyber risk. ...
Literature review on artificial intelligence, CPS and predictive cyber risk analytics CPSs and IoT produce a vast amount of data, and the analysis of such big data requires advanced analytical tools. ...
doi:10.1007/s42452-020-03559-4
fatcat:utrws565jngpdozzw2ghbt3j6q
Review: Big Data Techniques of Google, Amazon, Facebook and Twitter
2018
Journal of Communications
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Big data provides skilled analytics and instantly that deciphers failures and problems that may occur in near future or in current situations. ...
Further, big data strategize futuristic business moves with user performance analytics. ...
doi:10.12720/jcm.13.2.94-100
fatcat:lqdvekwl7vasnjqisf7phmmz5e
Smart Flood Resilience: Harnessing Community-Scale Big Data for Predictive Flood Risk Monitoring, Rapid Impact Assessment, and Situational Awareness
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2111.06461v1 fatcat:chmhag5flzailk2gqq62tmzn2e
The smart flood resilience framework focuses on four core capabilities that could be augmented by the use of heterogeneous community-scale big data and analytics techniques: (1) predictive flood risk mapping ...
The objective of this study is to propose and demonstrate a smart flood resilience framework that leverages heterogeneous community-scale big data and infrastructure sensor data to enhance predictive risk ...
The authors would also like to acknowledge INRIX, Inc. and SafeGraph for providing data. ...
arXiv:2111.06461v2
fatcat:2ugdb6geivdsjp5ypzkspvldey
Risk Forecasting in the Light of Big Data
2021
Journal of Risk Analysis and Crisis Response (JRACR)
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The shift from past data analysis to the prediction of future events is a central claim of big data analytics. ...
TYPES OF ANALYTICAL METHODS FOR BIG DATA RISK FORECASTING The ability to conduct predictive analysis based on large volumes of data is one of the interesting opportunities arising from the spread of large ...
doi:10.2991/jracr.k.201230.001
fatcat:torwized35crblnmbzrn644r2m
Systemic risk management and investment analysis with financial network analytics: research opportunities and challenges
2015
Financial Innovation
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such big data are also needed. ...
Moreover, with the advance of big data related technologies, and the availability of huge amounts of financial and economic network data, advanced computing technologies and data analytics that can comprehend ...
Moreover, the era of big data has bring the stakeholders huge amounts of data about various financial networks and thus great opportunities for studying such networks. ...
doi:10.1186/s40854-015-0001-x
fatcat:e4lhqph7dbhw5ogimc7s5hslve
Emergence of Scale-Free Blackout Sizes in Power Grids
2020
Physical Review Letters
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We model power grids as graphs with heavy-tailed sinks, which represent demand from cities, and study cascading failures on such graphs. ...
Our results are based on a new mathematical framework combining the physics of power flow with rare event analysis for heavy-tailed distributions, and are validated using various synthetic networks and ...
We thank Sem Borst for useful discussions, and the Isaac Newton Institute for support and hospitality during the program "Mathematics of Energy Systems." ...
doi:10.1103/physrevlett.125.058301
pmid:32794856
fatcat:f3vvrccnzvf27micdiwc47wvye
How Big Is Too Big? Critical Shocks for Systemic Failure Cascades
2013
Journal of statistical physics
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For three different threshold distributions P(\theta), we derive analytical results for the size of the cascade, X(t), which is regarded as a measure of systemic risk, and the time when it stops. ...
If the shock hits only one agent initially and causes it to fail, this can induce a cascade of failures among neighoring agents. ...
The complex network approach was also used to describe cascading processes in power grids and in Internet services [4, 5] , and was also applied to data storage services [6] . ...
doi:10.1007/s10955-013-0723-y
fatcat:z4hvqzuj6bgdjno4td7zm63wve
Critical infrastructure automated immuno-response system (CIAIRS)
2016
2016 International Conference on Control, Decision and Information Technologies (CoDIT)
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data and the use of the analytics for predictive, descriptive, diagnosis and prescriptive analysis. ...
These areas include big data and the use of analytics for predictive, descriptive, diagnosis and prescriptive analysis. Next, the chapter describes the publish-subscribe design pattern. ...
doi:10.1109/codit.2016.7593542
dblp:conf/codit/BadriFH16
fatcat:t4l4fqbv3fdudl4na7vk5o5jfe
A Machine Learning Approach for Big Data in Oil and Gas Pipelines
2015
2015 3rd International Conference on Future Internet of Things and Cloud
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Thus, the collected raw data is so big that it makes the pipeline probing process difficult, exhausting and error-prone. ...
Machine learning approaches such as neural networks have made it possible to effectively manage the complexity pertaining to big data and learn their intrinsic properties. ...
To alleviate the difficulties accompanying the analysis process of such big data, the data dimensionality is first reduced through suitable feature extraction techniques. ...
doi:10.1109/ficloud.2015.54
dblp:conf/ficloud/MohamedHT15
fatcat:h6vrgyghfjdo5kdsvq2kuhjuwu
Exploratory Models in a time of Big Data
2016
Interdisciplinary Science Reviews
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We then present major changes introduced by Big Data and computer modelling to the traditional modelling paradigm. ...
This paper aims to trigger discourse about the emergence of a new type of social scientific model -Exploratory Models -models which draw on Big Data, computer modelling methods and interdisciplinary research ...
Moreover, these models use Big Data to become epistemologically similar to experiments and to allow higher flexibility in data control and analysis. ...
doi:10.1080/03080188.2016.1257196
fatcat:bydrvjtxcjay3jby4jbtclfr7q
Technical Challenges for Big Data in Biomedicine and Health: Data Sources, Infrastructure, and Analytics
2014
IMIA Yearbook of Medical Informatics
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Methods: We discuss sources of big datasets, survey infrastructures for big data storage and big data processing, and describe the main challenges that arise when analyzing big data. ...
Objectives: To review technical and methodological challenges for big data research in biomedicine and health. ...
Acknowledgements Niels Peek and John Holmes chair the IMIA working group on Data Mining and Big Data Analytics. ...
doi:10.15265/iy-2014-0018
pmid:25123720
pmcid:PMC4287098
fatcat:pcq6bb26srdmfly2szkdfvqzry
RCMP: Enabling Efficient Recomputation Based Failure Resilience for Big Data Analytics
2014
2014 IEEE 28th International Parallel and Distributed Processing Symposium
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Data replication, the main failure resilience strategy used for big data analytics jobs, can be unnecessarily inefficient. ...
In this paper we show how job recomputation can be made a first-order failure resilience strategy for big data analytics. The need for data replication can thus be significantly reduced. ...
INTRODUCTION Data replication is the main failure resilience strategy used for big data analytics jobs today. ...
doi:10.1109/ipdps.2014.102
dblp:conf/ipps/DinuN14
fatcat:75uevz3py5ey7blqal6pphufwq
Disaster Management in Smart Cities
2021
Smart Cities
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This data availability opens new research opportunities in the study of the interdependency between those critical infrastructures and cascading effects solutions and focuses on the smart city as a network ...
A data-driven approach is considered, using artificial intelligence and methods to minimize cascading effects and the destruction of failing critical infrastructures and their components (at a city level ...
Data analytics allows the generation of a big picture, and information can be used for decision support. ...
doi:10.3390/smartcities4020042
fatcat:bisk5r6i7vfljlju63wacqxe7q
Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends
2014
BioData Mining
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The objective of this paper is to summarize the state-of-the-art efforts in clinical big data analytics and highlight what might be needed to enhance the outcomes of clinical big data analytics tools. ...
Data are stored in the HDFS and made available to the slave nodes for computation. ...
This is followed by our perspective and use cases on how to leverage clinical big data for novel analytics. ...
doi:10.1186/1756-0381-7-22
pmid:25383096
pmcid:PMC4224309
fatcat:zpis7kklerh2vna5le2gtxc5vi
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