Abstract-Effective design of online shopping websites is the need of the hour as design plays a crucial role in the success of online shopping businesses. Recently, the use of Quality Function Deployment (QFD) has been reported for the design of online shopping websites. QFD is a customer driven process that encompasses voluminous data gathered from customers through several techniques like personal interview, focus groups, surveys etc. This massive, unsorted and unstructured data is required to be transformed into a limited number of structured information to represent the actual Customer Needs (CNs) which are then utilized in subsequent stages of QFD process. This can be achieved through brainstorming using techniques like Affinity Process. However, integrating the Affinity Process within QFD is tedious and time consuming and cannot be dealt with manually. This generates a pressing need for a software tool to serve the purpose. Moreover, the researches carried out so far have focused on QFD application, post the generation of CNs. Also, the available QFD softwares lack the option to generate CNs from collected data. Thus, the paper aims to develop a novel software tool that integrates Affinity Process with QFD to generate customers' needs for effective design of online shopping websites. The software system is developed using Visual Basic Dot Net (VB.Net) that integrates a MS-Access database.
Over the past few years, wireless sensor networks (WSN) have emerged as one of the most exciting fields in Computer Science research. A Wireless Sensor Network (WSN) is a set of sensors that are integrated with a physical environment. These sensors are small in size, and capable of sensing physical phenomenon and processing them. The main aim of deploying applications based on WSNs is to make use of the data sensed by the sensors to raise the real-time decisions. The main limitations of WSNs are characteristics of sensor nodes and nature of sensor data generated by networks. Due to these limitations, traditional data mining techniques are not suitable to WSNs. As the data generated by WSNs is highly resource-constrained, huge in volume, fast changing, it is very challenging to design suitable data mining techniques for WSNs. This inspires to explore a novel and appropriate data mining technique capable of extracting knowledge from huge volume and variety of continuously arriving data from WSNs. In this paper different existing data mining techniques adopted for WSNs are examined with different classification, evaluation approaches, Finally, some research challenges related to adopting data mining techniques in WSNs are also pointed out.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.