Automated image exploitation algorithms requires both computation power and fast disk access. Ordinary hardware or a sequential processing architecture is far from performing with acceptable performance, while dealing with huge satellite imagery data. In this work, a memory centric analytics solution is proposed for performing querying, graph based operations, machine learning and stream processing effectively. A proof of concept experiment was performed in order to demonstrate the effect of memory centric operations on big data. The initial results show that, memory centric analytics has great potential for imagery exploitation.


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    Title :

    MEMCA for satellite and space data: MEMCA [Memory centric analytics] for satellite and space data


    Contributors:


    Publication date :

    2015-06-01


    Size :

    759394 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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