Stratified Computation of Skylines with Partially-Ordered Domains

In this paper, we study the evaluation of skyline queries with partially-ordered attributes. Because such attributes lack a total ordering, traditional index-based evaluation algorithms (e.g., NN and BBS) that are designed for totallyordered attributes can no longer prune the space as effectively. Our solution is to transform each partially-ordered attribute into a two-integer domain that allows us to exploit index-based algorithms to compute skyline queries on the transformed space. Based on this framework, we propose three novel algorithms: BBS+ is a straightforward adaptation of BBS using the framework, and SDC (Stratification by Dominance Classification) and SDC+ are optimized to handle false positives and support progressive evaluation. Both SDC and SDC+ exploit a dominance relationship to organize the data into strata. While SDC generates its strata at runtime, SDC+ partitions the data into strata offline. We also design two dominance classification strategies (MinPC and MaxPC) ...

• Type: Conference
• Year: 2005
• Where: SIGMOD
• Authors: Chee Yong Chan, Pin-Kwang Eng, Kian-Lee Tan
• Keywords: Database, Dominance Classification Strategies, Index-based Evaluation Algorithms, SIGMOD 2005, Skyline Query Evaluation