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Using Local Inference in Massively Distributed Systems

Using Local Inference in Massively Distributed Systems

Objective

As the scale of today¿s networked techno-social systems continues to increase, the analysis of their global phenomena becomes increasingly difficult, due to the continuous production of streams of data scattered among distributed, possibly resource-constrained nodes, and requiring reliable resolution in (near) real-time.We will explore a novel approach for realising sophisticated, large-scale distributed data-stream analysis systems, relying on processing local data in situ. Our key insight is that, for a wide range of distributed data analysis tasks, we can employ novel geometric techniques for intelligently decomposing the monitoring of complex holistic conditions and functions into safe, local constraints that can be tracked independently at each node (without communication), while guaranteeing correctness for the global-monitoring operation. While some solutions exist for the limited case of linear functions of the data, it is hard to deal with general, non-linear functions: in this case, a node¿s local function value essentially tells us absolutely nothing about the global function value. Our fundamental idea is to design novel algorithmic tools that monitor the input domain of the global function rather than its range. Each node can then be assigned a safe zone (SZ) for its local values that can offer guarantees for the value of the global function over the entire collection of nodes. This represents a dramatic shift in conventional thinking and the state-of-the-art. We aim to reduce the amount of communication and data collection across nodes to a minimum, requiring nodes to communicate only when their local constraints are violated. Privacy protection, in the case when transmitted data contain sensitive information, is also revolutionized in our view. We investigate real-life scenarios from network health monitoring, large-scale analysis of human mobility and traffic phenomena, internet-scale distributed querying, and monitoring sensor networks.
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Coordinator Contact

Michael MAY (Dr.)

Coordinator

FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V.

Address

Hansastrasse 27c
80686 Munchen

Germany

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 456 420

Administrative Contact

Elke Rupp (Ms.)

Participants (5)

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POLYTECHNEIO KRITIS

Greece

EU Contribution

€ 361 800

UNIVERSITY OF HAIFA

Israel

EU Contribution

€ 328 440

TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY

Israel

EU Contribution

€ 395 200

CONSIGLIO NAZIONALE DELLE RICERCHE

Italy

EU Contribution

€ 229 408

UNIVERSITA DI PISA

Italy

EU Contribution

€ 120 000

Project information

Grant agreement ID: 255951

Status

Closed project

  • Start date

    1 October 2010

  • End date

    30 September 2013

Funded under:

FP7-ICT

  • Overall budget:

    € 2 495 742

  • EU contribution

    € 1 891 268

Coordinated by:

FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V.

Germany