Efficient Reverse Skyline Algorithm for Discovering Top K-dominant Products

Recent boom in internet growth and the advancement in internet security have led to rapid growth in Ecommerce and related services. In this context, capturing the preferences of customers plays an important role in decisions about the design and launch of new products in the market. The science that primarily deals with the support of such decisions is the Operational Research. Since many of the research problems of Operational Research have to do with the analysis of large volumes of data, therefore there has been a keen interest in data management methods to solve these problems. In this work we develop new algorithms for two problems related to the analysis of large volumes of consumer preferences, with practical applications in market research. The first problem we consider is to find the potential buyers of a product (potential customer’s identification). We formulated this problem as a reverse query skyline and propose a new algorithm called ERS. Secondly, Practical applications often require simultaneous processing of multiple queries. To resolve this problem, we formulated a new type of query, which is referred to as a query to find the k dominant candidates (k-dominant query). Our experimental evaluation validates the efficiency of the proposed algorithm which outperforms BRS by a huge margin.