Abstract
K-Most Promising (K-MPP) product is a strategy for selecting a product that used in the process of determining the most demanded products by consumers. The basic computations used to perform K-MPP are two types of skyline queries: dynamic skyline and reverse skyline. K-MPP selection is done on the application layer, which is the last layer of the OSI model. One of the application layer functions is providing services according to the user's preferences. In the K-MPP implementation, there exists the situation in which the manufacturer may be less satisfied with the query results generated by the database search process (why-not question), so they want to know why the database gives query results that do not match their expectations. For example, manufacturers want to know why a particular data point (unexpected data) appears in the query result set, and why the expected product does not appear as a query result. The next problem is that traditional database systems will not be able to provide data analysis and solution to answer why-not questions preferred by users. To improve the usability of the database system, this study is aiming to answer why-not K-MPP and providing data refinement solutions by considering user feedback, so users can also find out why the result set does not meet their expectations. Moreover, it may help users to understand the result by performing analysis information and data refinement suggestion.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.