Abstract
Event detection in social networks and, more in general, for large-scale Social Analytics, requires continuous monitoring and processing of huge volumes of data. In this paper we explored effective ways to leverage the opportunities coming from innovations and evolutions in computational power, storage, and infrastructures, with particular focus on modern architectures. In particular, we position a specific core technology and cloud platform adopted in our research with respect to micro-services. Furthermore, we demonstrate the application of some of its capabilities for performing data-intensive computations and implementing services in a real case of social analytics. A prototype of this system was experimented in the contest of a specific kind of social event, an art exhibition of sculptures, where the system collected and analyzed in real-time the tweets issued in an entire region, including exhibition sites, and continuously updated analytical dashboards placed in one of the exhibition rooms.
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Chianese, A., Benedusi, P., Piccialli, F. (2017). Designing a Service Oriented System for social analytics. In: Xhafa, F., Barolli, L., Amato, F. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2016. Lecture Notes on Data Engineering and Communications Technologies, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-49109-7_67
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DOI: https://doi.org/10.1007/978-3-319-49109-7_67
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