A model for processing skyline queries over a database with missing data
Skyline queries provide a flexible query operator that returns data items (skylines) which are not being dominated by other data items in all dimensions (attributes) of the database. Most of the existing skyline techniques determine the skylines by assuming that the values of dimensions for every...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Design for Scientific Renaissance
2015
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/44860/1/A_Model_for_Processing_Skyline_Queries_over_a_database_with_missing_data.pdf http://irep.iium.edu.my/44860/ http://www.sign-ific-ance.co.uk/index.php/JACSTR/article/view/1169/1107 |
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| Summary: | Skyline queries provide a flexible query operator that returns data items (skylines) which are not being dominated
by other data items in all dimensions (attributes) of the database. Most of the existing skyline techniques
determine the skylines by assuming that the values of dimensions for every data item are available (complete).
However, this assumption is not always true particularly for multidimensional database as some values may be
missing. The incompleteness of data leads to the loss of the transitivity property of skyline technique and results
into failure in test dominance as some data items are incomparable to each other. Furthermore, incompleteness of
data influences negatively on the process of finding skylines, leading to high overhead, due to exhaustive pairwise
comparisons between the data items. This paper proposed a model to process skyline queries for incomplete data
with the aim of avoiding the issue of cyclic dominance in deriving skylines. The proposed model for identifying
skylines for incomplete data consists of four components, namely: Data Clustering Builder, Group Constructor
and Local Skylines Identifier, k-dom Skyline Generator, and Incomplete Skylines Identifier. Including these
processes in the proposed model has optimized the process of identifying skylines in incomplete database by
reducing the necessary number of pairwise comparison through eliminating the dominated data items as early as
possible before applying the skyline technique. |
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