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With the rapid development of knowledge engineering and cyberinfrastructure, a lot of data processing and knowledge reasoning capabilities become online accessible, shareable and interoperable.
Artificial neural network are the structures consisted of interconnected adaptive simple processing elements that can perform massive parallel computation for data processing and knowledge representation.
Artificial neural networks (ANN) mimic the function of the human brain and are capable of performing massively parallel computations for data processing and knowledge representation.
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After this, a simple use case based on Bristol open data by identifying needs of information processing and knowledge generation for decision making is presented in Section "A use case: analytics using Bristol open data".
Moreover, increasing data-intensification poses new demands for more efficient organizational data processing and effective knowledge utilization capabilities.
That is, when future companies become more software-intensive, they will need more efficient internal data processing and effective knowledge utilization capabilities (incorporating data science).
Similarly, cluster deduplication and redundancy elimination algorithms that remove duplicated data for efficient data processing and useful knowledge discovery are primarily post-data collection methods [12, 13, 14, 15].
The model proposed in [20] comprised of four interconnecting phases: data collection phase, data storage phase, data processing and analysis, and knowledge creation.
Although this data deluge has an enormous potential to transform our lives, knowledge creation and translation need software applications that scale with increasing data processing and analysis requirements.
Communications merges into data processing and data processing into banking.
Device connectivity or data processing and analysis?
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