Building an integrated CBR-Big Data Oriented Architecture based on SEASALT

Kareem Amin

In: Emmanuel Müller , Ralf Krestel , Davide Mottin (Hrsg.). LWDA 2016 - Lernen, Wissen, Daten, Analysen - Workshop Proceedings. GI-Workshop-Tage "Lernen, Wissen, Daten, Analysen" (LWDA-2016) September 12-14 Potsdam Germany Seiten 257-264 Hasso-Plattner-Institut 2016.


The growth of intensive data-driven decision-making is now being recognized broadly. In this paper I propose a CBR – Big Data oriented architecture based on the SEASALT architecture. SEASALT will be enhanced to be compliant with Big Data frameworks. I will use the state-of-the-art/best practices approaches for managing Big Data and CBR. I will go through the process starting from gathering data stage till building a CBR system that is able to answer streams of questions and come up with accurate retrieved results in a reasonable time.

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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence