Range queries in OLAP data cubes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Proceedings of the 17th International Conference on Data Engineering
Efficient Progressive Skyline Computation
Proceedings of the 27th International Conference on Very Large Data Bases
Techniques for Design and Implementation of Efficient Spatial Access Methods
VLDB '88 Proceedings of the 14th International Conference on Very Large Data Bases
An optimal and progressive algorithm for skyline queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Stabbing the Sky: Efficient Skyline Computation over Sliding Windows
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Efficient computation of the skyline cube
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Catching the best views of skyline: a semantic approach based on decisive subspaces
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Finding k-dominant skylines in high dimensional space
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
DADA: a data cube for dominant relationship analysis
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Foundations of preferences in database systems
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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Recent research on skyline queries has attracted much interest in the database and data mining community. The concept of dominant relationship analysis has commonly used in the context of skyline computation, due to its importance in many applications. Current methods have only considered so-called min/max hard attributes like price and quality which a user wants to minimize or maximize. However, objects can also have temporal attribute which can be used to represent relevant constraints on the query results. In this paper, we introduce novel skyline query types taking into account not only min/max hard attributes but also temporal attribute and the relationships between these different attribute types. We find the interrelated connection between the time-evolving attributes and the dominant relationship. Based on this discovery, we define the novel dominant relationship based on temporal aggregation and use it to analyze the problem of positioning a product in a competitive market while the time frame is required. We propose a new and efficient method to process temporal aggregation dominant relationship queries using corner transformation. Our experimental evaluation using a real dataset and various synthetic datasets demonstrates that the new query types are indeed meaningful and the proposed algorithms are efficient and scalable.