Social media and networks are used by millions of people to share with their friends across the world: tastes, opinions, ideas, etc. The volume and the speed at which these data are produced make it a challenging task to discover meaningful patterns in the data. Nevertheless, very interesting business goals could be achieved collecting these data and performing analytics on social media data streams, such as: addressing marketing strategies, targeting advertisements, and so forth. We emphasize that there is a need to investigate and define suitable knowledge mining approaches to go beyond explicitly available metadata by analyzing unstructured data to provide intelligent analytics services. Specifically, in this paper we provide first results on applying OLAP analysis to multidimensional Tweet streams.

OLAP analysis of multidimensional tweet streams for supporting advanced analytics

CUZZOCREA, Alfredo Massimiliano;
2016

Abstract

Social media and networks are used by millions of people to share with their friends across the world: tastes, opinions, ideas, etc. The volume and the speed at which these data are produced make it a challenging task to discover meaningful patterns in the data. Nevertheless, very interesting business goals could be achieved collecting these data and performing analytics on social media data streams, such as: addressing marketing strategies, targeting advertisements, and so forth. We emphasize that there is a need to investigate and define suitable knowledge mining approaches to go beyond explicitly available metadata by analyzing unstructured data to provide intelligent analytics services. Specifically, in this paper we provide first results on applying OLAP analysis to multidimensional Tweet streams.
9781450337397
9781450337397
http://dl.acm.org/citation.cfm?doid=2851613.2851662
File in questo prodotto:
File Dimensione Formato  
ACM Cuzzocrea 2016.pdf

non disponibili

Tipologia: Documento in Versione Editoriale
Licenza: Digital Rights Management non definito
Dimensione 1.36 MB
Formato Adobe PDF
1.36 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2898322
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 50
  • ???jsp.display-item.citation.isi??? ND
social impact