DATA-DRIVEN PRODUCT RETURNS PREDICTION: A CLOUD-BASED ENSEMBLE SELECTION APPROACH

The number of product returns represents a considerable cost factor in e-commerce, especially in the apparel sector. The application of advanced information technologies and predictive analytics, enabling to capture and analyze massive amounts of user data, pave the way for a more efficient management of product returns and reverse logistics. However, we identify a lack of data-driven approaches in this area, especially regarding product returns prediction. In this paper, we present an ensemble selection approach for predicting product returns in the apparel sector. Computational experiments indicate that our approach produces satisfying results in terms of prediction quality. We further explore the correlation between sample sizes and computational times. Thereby, we demonstrate that the run-time increases exponentially when using more data records. To address heavy run-time overheads resulting from high processing and memory requirements of classifiers, we present a framework to embed ensemble selection processes into a highly scalable cloud environment. The framework explains the provisioning of cloud resources and parallelization of tasks according to ensemble selection processes. It further builds a basis for considering data streams, data splitting, and a dynamic adoption of changing customer behavior over time, which has not been considered in related work so far. The envisioned forecasting support system aids retailers in reducing product returns and increasing profit margins

Cloud Computing

Cloud computing was a cloud technology pioneered by Amazon for a long time due to its software technology that is based on the online shopping platform. After Google, Microsoft also follow up, and this technology, in fact, already exists in our lives, and applications continue to expand, become an integral part of life. With the rapid development of the Internet and the demand for high-speed computing of mobile devices, the simplest cloud computing technology has been widely used in online services, such as “search engine, webmail,” and so on. Users can get a lot of information by simply entering a simple instruction. Further cloud computing is not only for data search and analysis function, but also can be used in the biological sciences, such as: analysis of cancer cells, analysis of DNA structure, gene mapping sequencing; in the future more Smart phone, GPS and other mobile devices through the cloud computing to develop more application service

A cloud brokerage approach for solving the resource management problem in multi-cloud environments

Cloud computing is increasingly becoming a mainstream technology-delivery model from which companies and research aim to gain value. As different cloud providers offer cloud services in various forms, there is a huge potential of optimizing the selection of those services to better fulfill user-, i.e., consumer- and application-related requirements. Recently, multi-cloud environments have been introduced thus making it possible to execute applications not only on single-provider resources, but also by using resources from multiple cloud providers. Due to the growing complexity in cloud marketplaces, a cloud brokerage mechanism, interacting on behalf of the consumers with various cloud providers, can be used to provide decision support for consumers. In this paper, we address the Cloud Resource Management Problem in multi-cloud environments that is a recent optimization problem aimed at reducing the monetary cost and the execution time of consumer applications using Infrastructure as a Service of multiple cloud providers. Due to the fact that consumers require real-time and high-quality solutions to economically automate cloud resource management and corresponding deployment processes, we propose an efficient Biased Random-Key Genetic Algorithm. The computational experiments over a large benchmark suite generated based on real cloud market resources indicate that the performance of our approach outperforms the approaches proposed in the literature