Reverse Skyline Computation over Sliding Windows

Reverse skyline queries have been used in many real-world applications such as business planning, market analysis, and environmental monitoring. In this paper, we investigated how to efficiently evaluate continuous reverse skyline queries over sliding windows. We first theoretically analyzed the inherent properties of reverse skyline on data streams and proposed a novel pruning technique to reduce the number of data points preserved for processing continuous reverse skyline queries. Then, an efficient approach, called Semidominance Based Reverse Skyline (SDRS), was proposed to process continuous reverse skyline queries. Moreover, an extension was also proposed to handle n-of-N and (n1,n2)-of-N reverse skyline queries. Our extensive experimental studies have demonstrated the efficiency as well as effectiveness of the proposed approach with various experimental settings

Direct Neighbor Search

In this paper we study a novel query type, called direct neighbor query. Two objects in a dataset are direct neighbors (DNs) if a window selection may exclusively retrieve these two objects. Given a source object, a DN search computes all of its direct neighbors in the dataset. The DNs define a new type of affinity that differs from existing formulations (e.g., nearest neighbors, nearest surrounders, reverse nearest neighbors, etc) and finds application in domains where user interests are expressed in the form of windows, i.e., multi-attribute range selections. Drawing on key properties of the DN relationship, we develop an I/O optimal processing algorithm for data indexed with a spatial access method. In addition to plain DN search, we also study its K-DN and all-DN variants. The former relaxes the DN condition – two objects are K-DNs if a window query may retrieve them and only up to K − 1 other objects – whereas the all-DN variant computes the DNs of every object in the dataset. Using real, large-scale data

Efficient Processing of Metric Skyline Queries

Skyline query is of great importance in many applications, such as multicriteria decision making and business planning. In particular, a skyline point is a data object in the database whose attribute vector is not dominated by that of any other objects. Previous methods to retrieve skyline points usually assume static data objects in the database (that is, their attribute vectors are fixed), whereas several recent works focus on skyline queries with dynamic attributes. In this paper, we propose a novel variant of skyline queries, namely metric skyline, whose dynamic attributes are defined in the metric space (that is, not limited to the euclidean space). We illustrate an efficient and effective pruning mechanism to answer metric skyline queries (MSQs) through a metric index. Most importantly, we formalize the query performance of the MSQ in terms of the pruning power (PP), by a cost model, in light of which we construct an optimized metric index aiming to maximize PP of MSQs. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed pruning techniques as well as the constructed index in answering MSQs