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AdvisorJaoua, Ali Mohamed
AuthorADAM, GEORGES J.
Available date2019-02-28T11:21:40Z
Publication Date2018-01
URIhttp://hdl.handle.net/10576/11366
AbstractDeep web databases provide strict search interface and limited web access with top-k results based on a pre-defined ranking function. However, top-k results may not be suitable for multi-criteria decision making because of the variety in preferences. To make the results more relevant to such a decision maker, skyline records were introduced, and as per definition these records are not dominated by any other record such that a record dominates another if it is better or as good as other for all attributes and better in at least one attribute. In this report, we introduce an algorithm for discovering skyline records from hidden databases using different multi-objective attributes on a real-world database. We predicted a new lower bound for the minimum issued number of queries to extract the skyline. This was supported by our algorithm which accomplished the above task in an efficient manner including the worst-case scenario hence proving our theory via running rigorous experiments on a hidden database given the limitations on hand.
SponsorThis contribution was made possible by NPRP grant #07- 794-1-145 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
SubjectSKYLINE SYSTEM
SubjectGOOGLE FLIGHTS
SubjectAlgorithms
TitleEFFICIENT SKYLINE SYSTEM DEVELOPMENT FOR NORMAL AND HIDDEN DATABASES: APPLICATION FOR GOOGLE FLIGHTS
TypeProfessional Masters Project
DepartmentComputing


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