A big data platform for large scale event processing

Gulisano, Vincenzo Massimiliano; Jiménez-Peris, Ricardo; Patiño-Martínez, M.; Soriente, Claudio y Valduriez, Patrick (2011). A big data platform for large scale event processing. "Ercim News" (n. 89); pp. 32-33. ISSN 0926-4981.


Título: A big data platform for large scale event processing
  • Gulisano, Vincenzo Massimiliano
  • Jiménez-Peris, Ricardo
  • Patiño-Martínez, M.
  • Soriente, Claudio
  • Valduriez, Patrick
Tipo de Documento: Artículo
Título de Revista/Publicación: Ercim News
Fecha: 2011
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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To date, big data applications have focused on the store-and-process paradigm. In this paper we describe an initiative to deal with big data applications for continuous streams of events. In many emerging applications, the volume of data being streamed is so large that the traditional ‘store-then-process’ paradigm is either not suitable or too inefficient. Moreover, soft-real time requirements might severely limit the engineering solutions. Many scenarios fit this description. In network security for cloud data centres, for instance, very high volumes of IP packets and events from sensors at firewalls, network switches and routers and servers need to be analyzed and should detect attacks in minimal time, in order to limit the effect of the malicious activity over the IT infrastructure. Similarly, in the fraud department of a credit card company, payment requests should be processed online and need to be processed as quickly as possible in order to provide meaningful results in real-time. An ideal system would detect fraud during the authorization process that lasts hundreds of milliseconds and deny the payment authorization, minimizing the damage to the user and the credit card company.

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ID de Registro: 13648
Identificador DC: http://oa.upm.es/13648/
Identificador OAI: oai:oa.upm.es:13648
Depositado por: Memoria Investigacion
Depositado el: 25 Feb 2013 08:36
Ultima Modificación: 21 Abr 2016 12:58
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