Preference-based queries often referred to as skyline queries play an important role in cooperative query processing. However, their prohibitive result sizes pose a severe challenge to the paradigm‟s practical applicability. In this paper we discuss the incremental re-computation of skylines based on additional information elicited from the user. Extending the traditional case of totally ordere...

Business intelligence applications ranging from stock market tickers to strategic supply chain adaptation systems require the efficient support of multi-criteria decision support (MCDS) queries. Skyline queries are a popular class of MCDS queries that have received a lot of attention recently. However, a vast majority of skyline algorithms focus entirely on the input being a single data set. In...

Skyline queries are a well-known technique for explorative retrieval, multi-objective optimization problems, and personalization tasks in databases. They are widely acclaimed for their intuitive query formulation mechanisms. However, when operating on incomplete datasets, skyline query processing is severely hampered and often has to resort to error-prone heuristics. Unfortunately, incomplete d...

The emergence of real-time decision-making applications in domains like high-frequency trading, emergency management and service level analysis in communication networks, has led to the definition of new classes of queries. Skyline queries are a notable example. Their results consist of all the tuples whose attribute vector is not dominated (in the Pareto sense) by one of any other tuple. Becau...

Skyline queries are a well-studied problem for multidimensional data, wherein points are returned to the user iff no other point is preferable across all attributes. This leaves only the points most likely to appeal to an arbitrary user. However, some dominated points may still be interesting, and the skyline offers little support for helping the user understand why some interesting points are ...

Skyline queries identify skyline points, the minimal set of data points that dominate all other in a large dataset. The main challenge with is executing query shortest possible time. To address and solve performance issues, we propose decision tree-based method known as comparator (DC). This minimizes unnecessary dominance tests (i.e., pairwise comparisons) by constructing tree based on testing...