Designing a Scalable Social e-Commerce Application

Eugenio Zimeo, Gianfranco Oliva, Fabio Baldi, Alfonso Caracciolo

Abstract


eCommerce is gaining a momentum due to the wide diffusion of Web 2.0 technology. Social mining, recommenders and data semantics are moving the focus of eCommerce applications towards context-awareness and personalization. However, the design of these software systems needs specific architectures to support intelligent behaviors, still ensuring important non-functional properties, such as flexibility, efficiency and scalability. This paper proposes an architectural pattern that helps designers to easily identify the subsystems that characterize intelligent enterprise systems. By decoupling transactional behavior from batch processing, the pattern avoids the interference of knowledge extraction and reasoning processes with the state and the performance of the transactional subsystem, so improving scalability. The pattern has been experimented in eCommerce by designing an intelligent and scalable virtual mall.

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