STREAM
Scalable Autonomic Streaming Middleware for Real-time Processing of Massive Data Flows
| Coordinatore | UNIVERSIDAD POLITECNICA DE MADRID
Organization address
address: CALLE RAMIRO DE MAEZTU 7 contact info |
| Nazionalità Coordinatore | Spain [ES] |
| Totale costo | 3˙956˙379 € |
| EC contributo | 2˙609˙597 € |
| Programma | FP7-ICT
Specific Programme "Cooperation": Information and communication technologies |
| Code Call | FP7-ICT-2007-1 |
| Funding Scheme | CP |
| Anno di inizio | 2008 |
| Periodo (anno-mese-giorno) | 2008-02-01 - 2011-04-30 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
UNIVERSIDAD POLITECNICA DE MADRID
Organization address
address: CALLE RAMIRO DE MAEZTU 7 contact info |
ES (MADRID) | coordinator | 0.00 |
| 2 |
EPSILON S.R.L.
Organization address
address: VIA FRANCESCO FRACANZANO contact info |
IT (NAPOLI) | participant | 0.00 |
| 3 |
EXODUS A.E.
Organization address
address: ODOS FARANTATON 6-10 contact info |
EL (ATHINA) | participant | 0.00 |
| 4 |
FOUNDATION FOR RESEARCH AND TECHNOLOGY HELLAS
Organization address
address: N PLASTIRA STR contact info |
EL (HERAKLION) | participant | 0.00 |
| 5 |
TECHNISCHE UNIVERSITAET DRESDEN
Organization address
address: HELMHOLTZSTRASSE contact info |
DE (DRESDEN) | participant | 0.00 |
| 6 |
TELEFONICA INVESTIGACION Y DESARROLLO SA
Organization address
address: CALLE EMILIO VARGAS contact info |
ES (MADRID) | participant | 0.00 |
Mappa
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Obiettivo del progetto (Objective)
A growing number of applications require the ability to analyze massive amounts of streaming data in real time. Examples of such applications are: market data processing, anti-spam and anti-virus filters for e-mail, network security systems for incoming IP traffic in organisation-wide networks, automatic trading, fraud detection for cellular telephony to analyze and correlate phone calls, fraud detection for credit cards, and e-services for verifying SLAs. Typically, such applications require strong analysis and processing capabilities, i.e., data mining, to discover facts of interest. Data analysis happens today on clusters of workstations using specialized middleware and applications. Although solutions for real-time processing of information flows already exist, current platforms and infrastructures phase three main limitations: (a) scalability, (b) autonomy, and (c) performance. STREAM aims at scaling system size by an order of magnitude, to 100s of nodes, achieving real-time processing of information flows, and providing unsupervised and autonomous operation. This will allow for much broader deployment of such products and services to new areas that need to manipulate large information flows in a cost-effective manner, and in particular, the Telecom, Financial, and E-services sectors.