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Knowledge extraction from massive data is becoming more and more urgent.
Learning from these massive data is expected to bring significant opportunities and transformative potential for various sectors.
One of the challenges of large scale computing is that storing and analyzing massive data is becoming more and more difficult.
Learning from these massive data is expected to bring significant science and engineering advances which can facilitate the development of more intelligent systems.
As we are now entering the era of data deluge, how to efficiently manage these massive data is becoming a great challenge, especially for the exponentially growing unstructured data, which is far more than structured and semi-structured data.
While the potential of these massive data is undoubtedly significant, fully making sense of them requires new ways of thinking and novel learning techniques to address the various challenges.
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With the wide spread of Web of Things (WoT) technology, massive data are generated by huge amounts of distributed sensors and different applications.
In case of massive data are stored in cloud and data exchanges rapidly, the high traffic processing ability of the MUCON protocol can satisfy the access control demands of users in cloud computing environment perfectly.
I believe Taser is poised to revolutionize the public safety space with enhanced transparency from body-worn camera technology while leveraging the cloud to manage the massive data being generated by wearable cameras and sensors.
In the Data Preprocessing module, large-scale raw reads are filtered one by one, where the massive data are not loaded into computer memory at once, causing no memory pressure at all to any modern computer.
Volume represents how massive the data is, i.e., the number of transaction records.
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