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Big Data & Storage

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InfoBeyond advocates secure and effective big data sharing to achieve a win-win business goal as various data resources are managed by different owners.

Discovering meaningful information from large quantities of data in clouds is challenging. The centralized data warehouse approaches are unable to cope with the nature of the distributed data due to the large data transmission. On the other hand, the existing mediation data fusion approaches cannot be directly applied to the clouds as they are unable to provide solution to scenarios having inconsistent data formats, distributed reasoning, or large-scale cluster collaboration. To address the existing issues, InfoBeyond advocates efficient and accurate information fusion in clouds using N-gram, Semantic Machine Learning, and Map-Reduce Algorithms as illustrated in the below figure.

Our system provides an efficient and accurate method for understanding the user query with near real-time processing. Particularly, it offers several unique capabilities:

  • Semantic analysis: It semantically corrects and expands the query for accurate information retrieval based on the user-defined contextual knowledge information.
  • System scalability: Further, it offers a scalable machine learning approach for data mining on heterogeneous distributed data sources. Data models across the cloud nodes can be consistently maintained via the semantic mapping function.
  • System efficiency: Furthermore, our system leverages the Map-Reduce programming structure to enable efficient information fusion and distributed reasoning. The fusion engine promotes information trustworthiness and in-depth collaborations among the cloud nodes for improving fusion precision and reliability.

  


 
 
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