Topologically sorted skylines for partially ordered domains

Abstract — The vast majority of work on skyline queries considers totally ordered domains, whereas in many applications some attributes are partially ordered, as for instance, domains of set values, hierarchies, intervals and preferences. The only work addressing this issue has limited progressiveness and pruning ability, and it is only applicable to static skylines. This paper overcomes these problems with the following contributions. (i) We introduce a generic framework, termed TSS, for handling partially ordered domains using topological sorting. (ii) We propose a novel dominance check that eliminates false hits/misses, further enhancing progressiveness and pruning ability. (iii) We extend our methodology to dynamic skylines with respect to an input query. In this case, the dominance relationships change according to the query specification, and their computation is rather complex. We perform an extensive experimental evaluation demonstrating that TSS is up to 9 times and up to 2 orders of magnitude faster than existing methods in the static and the dynamic case, respectively. I

Representing and reasoning with qualitative preferences for compositional systems

Many applications call for techniques for representing and reasoning about preferences, i.e., relative desirability over a set of alternatives. Preferences over the alternatives are typically derived from preferences with respect to the various attributes of the alternatives (e.g., a student\u27s preference for one course over another may be influenced by his preference for the topic, the time of the day when the course is offered, etc.). Such preferences are often qualitative and conditional. When the alternatives are expressed as tuples of valuations of the relevant attributes, preferences between alternatives can often be expressed in the form of (a) preferences over the values of each attribute, and (b) relative importance of certain attributes over others. An important problem in reasoning with multi-attribute qualitative preferences is dominance testing, i.e., to find if one alternative (assignment to all attributes) is preferred over another. This problem is hard (PSPACE-complete) in general for well known qualitative conditional preference languages such as TCP-nets. We provide two practical approaches to dominance testing. First, we study a restricted unconditional preference language, and provide a dominance relation that can be computed in polynomial time by evaluating the satisfiability of an appropriately constructed logic formula. Second, we show how to reduce dominance testing for TCP-nets to reachability analysis in an induced preference graph. We provide an encoding of TCP-nets in the form of a Kripke structure for CTL. We show how to compute dominance using NuSMV, a model checker for CTL. We address the problem of identifying a preferred outcome in a setting where the outcomes or alternatives to be compared are composite in nature (i.e., collections of components that satisfy certain functional requirements). We define a dominance relation that allows us to compare collections of objects in terms of preferences over attributes of the objects that make up the collection, and show that the dominance relation is a strict partial order under certain conditions. We provide algorithms that use this dominance relation to identify only (sound), all (complete), or at least one (weakly complete) of the most preferred collections. We establish some key properties of the dominance relation and analyze the quality of solutions produced by the algorithms. We present results of simulation experiments aimed at comparing the algorithms, and report interesting conjectures and results that were derived from our analysis. Finally, we show how the above formalism and algorithms can be used in preference-based service composition, substitution, and adaptation

スカイライン問合わせを利用した大規模データベースの情報選別

Conventional SQL queries take exact input and produce complete result set. However, with massive increase in data volume in different applications, the large result sets returned by traditional SQL queries are not well suited for the users to take effective decisions. Therefore, there is an increasing interest in queries like top-k queries and skyline queries those produce a more concise result set. Top-k queries rely on the scores of the objects to evaluate the usefulness of the objects. In this type of queries, users require to define their own scoring function by combining their interests. Based on the user defined scoring function, the system sorts the objects by their scores and outputs the top-k objects in the ranking list as the result. However, defining a scoring function by the users is a major draw of the top-k queries as in the large data sets where there are many conflicting criteria exist, it is very difficult for the users to define the scoring functions by themselves.……広島大学(Hiroshima University)博士(工学)Engineeringdoctora