Data Filtering Utilizing Window Indexing

Author

Loper, Scott ; Makki, S. Kami

Author_Institution

Dept. of Comput. Sci., Eastern Michigan Univ., Ypsilanti, MI, USA

fYear

2010

fDate

20-23 April 2010

Firstpage

121

Lastpage

126

Abstract

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.

Keywords

database indexing; information filtering; iterative methods; optimisation; query processing; vectors; block nested loop algorithm; data filtering; database management systems; iterated window indexing; maximum vector problem; multiobjective optimization; partitioning and filtering method; skyline query; Algorithm design and analysis; Application software; Computer science; Conferences; Database systems; Indexing; Information filtering; Information filters; Mathematics; Partitioning algorithms; Filtering; Indexing; Partitioning; Skyline; Spatial; Vector;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Information Networking and Applications Workshops (WAINA), 2010 IEEE 24th International Conference on

Conference_Location

Perth, WA

Print_ISBN

978-1-4244-6701-3

Type

conf

DOI

10.1109/WAINA.2010.59

Filename

5480843