A Temporal Dominant Relationship Analysis Method

Abstract

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.

This research is partly supported by the National Science Foundation of China (60673138, 60603046), Key Program of Science Technology Research of MOE (106006), and Program for New Century Excellent Talents in University.