QALD-3: Multilingual question answering over linked data
Elena Cabrio, Philipp Cimiano, et al.
CLEF 2013
The amount of data stored by banks is rapidly increasing and provides the opportunity for banks to conduct predictive analytics and enhance its businesses. However, data scientists are facing large challenges, handling the massive amount of data efficiently and generating insights with real business value. In this paper, the Intelligent Customer Analytics for Recognition and Exploration (iCARE) framework is presented to analyze banking customer behaviors from banking big data, through analytical modeling methodologies and techniques designed for a key business scenario. Combining IBM software platforms and big data processing power with customized data analytical models, the iCARE solution provides deeper customer insights to satisfy a bank's specific business need and data environment. The advantages of the iCARE framework have been confirmed in a real case study of a bank in southeast China. In this case, iCARE helps generate insights for active customers based on their transaction behavior, using close to 20 terabytes of data.
Elena Cabrio, Philipp Cimiano, et al.
CLEF 2013
B. Wagle
EJOR
Indranil R. Bardhan, Sugato Bagchi, et al.
JMIS
J.P. Locquet, J. Perret, et al.
SPIE Optical Science, Engineering, and Instrumentation 1998