Different sources of information generate every day huge amount of data. For example, let us consider social networks: here the number of active users is impressive; they process and publish information in different formats and data are heterogeneous in their topics and in the published media (text, video, images, audio, etc.). In this work, we present a general framework for event detection in processing of heterogeneous data from social networks. The framework we propose, implements some techniques that users can exploit for malicious events detection on Twitter. © Springer International Publishing AG 2017.

An architecture for processing of heterogeneous sources

Amato, F.
;
Cozzolino, G.;Mazzeo, A.;Romano, S.
2017

Abstract

Different sources of information generate every day huge amount of data. For example, let us consider social networks: here the number of active users is impressive; they process and publish information in different formats and data are heterogeneous in their topics and in the published media (text, video, images, audio, etc.). In this work, we present a general framework for event detection in processing of heterogeneous data from social networks. The framework we propose, implements some techniques that users can exploit for malicious events detection on Twitter. © Springer International Publishing AG 2017.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/821900
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