Scipediacontent: Scipediacontent moved page Draft Content 724043876 to Suri et al 2015a

2021-01-28T18:41:40Z

Scipediacontent moved page Draft Content 724043876 to Suri et al 2015a

Scipediacontent: Created page with " == Abstract == Extensive deployment of sensor networks in recent years has led to the generation of large volumes of data. One approach to processing such large volumes of d..."

2021-01-28T18:41:36Z

Created page with " == Abstract == Extensive deployment of sensor networks in recent years has led to the generation of large volumes of data. One approach to processing such large volumes of d..."

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== Abstract ==

Extensive deployment of sensor networks in recent years has led to the generation of large volumes of data. One approach to processing such large volumes of data is to rely on parallelized approaches based on architectures such as MapReduce. However, fully-automated processing without human intervention is error prone. Supporting human involvement in processing pipelines of data in a variety of contexts such as warfare, cyber security, threat monitoring, and malware analysis leads to improved decision-making. Although this kind of human-machine collaboration seems straightforward, involving a human operator into an automated processing pipeline presents some challenges. For example, due to the asynchronous nature of the human intervention, care must be taken to ensure that once a user-made correction or assertion is introduced, all necessary adjustment and reprocessing is performed. In addition, to make the best use of limited resources and processing capabilities, reprocessing of data in light of such corrections must be minimized. This paper introduces an innovative approach for human-machine integration in decisionmaking for large-scale sensor networks that rely on the popular Hadoop MapReduce framework.

== Original document ==

The different versions of the original document can be found in:

* [http://www.jeffreymbradshaw.net/publications/Casini%20et%20al.%2C%20CogSIMA%202015-07108198.pdf http://www.jeffreymbradshaw.net/publications/Casini%20et%20al.%2C%20CogSIMA%202015-07108198.pdf]

* [http://xplorestaging.ieee.org/ielx7/7102993/7107964/07108198.pdf?arnumber=7108198 http://xplorestaging.ieee.org/ielx7/7102993/7107964/07108198.pdf?arnumber=7108198],
: [http://dx.doi.org/10.1109/cogsima.2015.7108198 http://dx.doi.org/10.1109/cogsima.2015.7108198]

* [https://ieeexplore.ieee.org/document/7108198 https://ieeexplore.ieee.org/document/7108198],
: [https://dblp.uni-trier.de/db/conf/cogsima/cogsima2015.html#CasiniDSBN15 https://dblp.uni-trier.de/db/conf/cogsima/cogsima2015.html#CasiniDSBN15],
: [https://academic.microsoft.com/#/detail/1533637705 https://academic.microsoft.com/#/detail/1533637705]

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Scipediacontent: Scipediacontent moved page Draft Content 724043876 to Suri et al 2015a

Scipediacontent: Created page with " == Abstract == Extensive deployment of sensor networks in recent years has led to the generation of large volumes of data. One approach to processing such large volumes of d..."