Efficiently performing consistency checks for multi-dimensional preference trade-offs

C. Lofi, Wolf-Tilo Balke, Ulrich Güntzer
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引用次数: 6

Abstract

Skyline queries have recently received a lot of attention due to their intuitive query capabilities. Following the concept of Pareto optimality all dasiabestpsila database objects are returned to the user. However, this often results in unmanageable large result set sizes hampering the success of this innovative paradigm. As an effective remedy for this problem, trade-offs provide a natural concept for dealing with incomparable choices. Such trade-offs, however, are not reflected by the Pareto paradigm. Thus, incorporating them into the userspsila preference orders and adjusting skyline results accordingly needs special algorithms beyond traditional skylining. For the actual integration of trade-offs into skylines, the problem of ensuring the consistency of arbitrary trade-off sets poses a demanding challenge. Consistency is a crucial aspect when dealing with multi-dimensional trade-offs spanning over several attributes. If the consistency should be violated, cyclic preferences may occur in the result set. But such cyclic preferences cannot be resolved by information systems in a sensible way. Often, this problem is circumvented by restricting the trade-offspsila expressiveness, e.g. by altogether ignoring some classes of possibly inconsistent trade-offs. In this paper, we will present a new algorithm capable of efficiently verifying the consistency of any arbitrary set of trade-offs. After motivating its basic concepts and introducing the algorithm itself, we will also show that it exhibits superior average-case performance. The benefits of our approach promise to pave the way towards personalized and cooperative information systems.
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有效地执行多维偏好权衡的一致性检查
由于其直观的查询功能,Skyline查询最近受到了很多关注。根据帕累托最优性的概念,所有数据库对象都返回给用户。然而,这通常会导致难以管理的大型结果集,从而阻碍这种创新范例的成功。作为对这个问题的有效补救,权衡为处理不可比较的选择提供了一个自然的概念。然而,这种权衡并没有反映在帕累托范式中。因此,将它们纳入用户的偏好顺序并相应地调整天际线结果需要超越传统天际线的特殊算法。对于实际的天际线的权衡整合,保证任意权衡集的一致性问题提出了一个艰巨的挑战。在处理跨越多个属性的多维权衡时,一致性是一个至关重要的方面。如果要违反一致性,则可能会在结果集中出现循环首选项。但信息系统无法以一种明智的方式解决这种循环偏好。通常,可以通过限制权衡的表达性来规避这个问题,例如,完全忽略一些可能不一致的权衡类。在本文中,我们将提出一种新的算法,能够有效地验证任何任意权衡集的一致性。在介绍了它的基本概念和算法本身之后,我们还将展示它具有优越的平均情况性能。我们的方法带来的好处有望为个性化和合作性信息系统铺平道路。
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