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EMOMETRIC : An IOT Integrated Big Data Analytic System for Real Time Retail Customer ’ s Emotion Tracking and Analysis
| Content Provider | Semantic Scholar |
|---|---|
| Author | Tallapragada, V. V. Satyanarayana Rao, Narsing A. Kanapala, Satish |
| Copyright Year | 2017 |
| Abstract | Business Intelligence is an important aspect of any organized commerce. With the growing competition and global outlook of the businesses, metric driven business insight has become extremely important for the corporations to analyse, understand and plan their business in better ways. Understanding customer's behaviour and their sentiments has been an active area of research over a long time. However, due to practical challenges associated with such adaptations, there hasn't been significant work towards customer behaviour and sentiment tracking in physical retail stores. In this work, we have addressed this significant challenge of the retail sector and proposed EMOMETRIC, which is an intelligent trolley that can track customer's emotion and provide a customer behavioural insight through IoT integrated data intelligence running on Apache Spark Cluster. The proposed system uses model based face and emotion tracking under real use case conditions. Results shows that the proposed technique has an overall accuracy of 95% in comparison to the current state of art. The proposed technique also adapts QoS enabled secure MQTT protocol to collect the data by the big data No-sql storage system. It is also observed that the proposed technique is not only fast and accurate but also illumination and pose invariant. This work can be used as a framework to offer emotion as a service through SAAS platform. Keyword: Business Intelligence, Customer Behaviour, Emotion, Apache Spark, MQTT Protocol, No-sql, Big Data. 674 V. V. Satyanarayana Tallapragada, N. Ananda Rao and Satish Kanapala |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ripublication.com/ijcir17/ijcirv13n5_03.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
