Data Filtering Utilizing Window Indexing

Since its introduction in 2001, the Skyline Query has been a useful addition to database management systems (DBMS) by returning best-fit results to a user. The skyline query is also a relevant area of research in mathematics as the maximum vector problem and multi-objective optimization. In this paper, we analyze both the Partitioning and Filtering (P&F) method and a new proposed method called Iterated Window Indexing. Next, we also propose two new methods, comprising combinations of previous solutions and Window Indexing. We then show that our hybrid methods are suitable for many cases and perform up to 10 times better than P&F and up to 8 times better than the Block Nested Loop (BNL) algorithm for computing skyline points.