Infusing social data analytics into Future Internet applications for manufacturing

Autor: Dimitrios Askounis, Iosif Alvertis, Evmorfia Biliri, Michael Petychakis, Sotirios Koussouris, Fenareti Lampathaki
Rok vydání: 2014
Předmět:
Zdroj: AICCSA
Popis: Today, a new age of engagement and collaboration has emerged with the proliferation of usergenerated content in social networks and generally the Web 2.0, rendering it particularly difficult for enterprises to monitor and act upon all content following conventional data mining methodologies. In this paper, we present our approach for a Future Internet enabler (FITMAN Anlzer) that provides automated, social data analytics and aims at assisting enterprises in becoming more tuned to their customer needs and gaining insights into current and future trends to early embed them into product design. The FITMAN Anlzer implementation is domainindependent and allows any manufacturer to effectively train it based on his needs and create personalized reports to timely capture the right information. Our methodology includes trend analytics, polarity detection through machine learning, data querying through flexible reports and finally informative charts to visualize the results in order to help companies in their decision making procedures.
Databáze: OpenAIRE