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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Dynamic Composable Analytics on Consumer Behaviour
verfasst von : Jin-Quan Goh, Fang-Fang Chua
Erschienen in: Computational Science and Its Applications – ICCSA 2018
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Abstract
Large enterprises and companies often use different tools and systems that distributed across their company branches to operate daily business operations. The collected data and logs have significant potential of providing useful information and insights for the company; however, staffs may spend massive time and effort to process the raw data into useful information as raw data is scattered and distributed across different platforms. This study proposes a framework called Dynamic Composable Analytic Framework (DCAF), which is able to accept and compose raw data from different systems or tools, and performs analytics on the composed data to identify or predict the consumer behavior. The proposed framework is able to perform data receiver, data composition, data massaging and data analytic job with minor human interaction. DCAF provides contribution as an end-to-end solution for converting raw data to predicted customer behavior information and thus improving the customer analytics efficiency.