Range Skyline Queries Research Articles

Dynamic planar range skyline queries in log logarithmic expected time

The skyline of a set P of points consists of the “best” points with respect to minimization or maximization of the attribute values. A point p dominates another point q if p is as good as q in all dimensions and it is strictly better than q in at least one dimension. In this work, we focus on the 2-d space and provide expected performance guarantees for dynamic (insertions and deletions) 3-sided range skyline queries. We assume that the x and y coordinates of the points are drawn from a class of distributions and present the ML-tree (Modified Layered Range-tree), which attains O(log2⁡Nlog⁡log⁡N) expected update time and O(tlog⁡log⁡N) time with high probability for finding planar skyline points in a 3-sided query rectangle q=[a,b]×[d,+∞) in the RAM model, where N is the cardinality of P and t is the answer size.

Design and Analysis of Spatial Skyline Queries Indexing

Dwelling in the information age permits nearly everybody needs to recover countless information and choices to gather from to fulfill their necessities. In distinctive cases, the quantity of information accessible and the speed of change may cover the ideal and required explanation. Spatial-textual queries provide the most acclaimed nearest points concerning a conveyed site and a keyword set. Current practice regularly thought on the most capable technique to expertly get the top-k resultset reestablished a spatial-scholarly query. A capable Spatial Range Skyline Query (SRSQ) algorithm is proposed which initially performsa spatial keyword query (SKQ) that relies upon an IRtree that documents the information. Skyline centers picked are not simply established on their partitions to a lot of inquiries and more subject to their significance to a social occasion of query keywords. Additionally, besides proposed range skyline (RS) methods based on R-tree multi-dimensional space including secondary- memory pruning tools for operating field skyline queries is accomplished. The advanced scheme is dynamic and I/O optimum. Ultimately, methodology presents a modern assessment that demonstrates the proficiency.

Distributed Continuous Range-Skyline Query Monitoring Over the Internet of Mobile Things

A Range-Skyline Query (RSQ) is the combination of range query and skyline query. It is one of the practical query types in multi-criteria decision services, which may include the spatial and non-spatial information as well as make the resulting information more useful than skyline search when the location is concerned. Furthermore, Continuous Range-Skyline Query (CRSQ) is an extension of Range-Skyline Query (RSQ) that the system continuously reports the skyline results to a query within a given search range. This work focuses on the RSQ and CRSQ within a specific range on Internet of Mobile Things (IoMT) applications. Many server-client approaches for CRSQ have been proposed but are sensitive to the number of moving objects. We propose an effective and non-centralized approach, Distributed Continuous Range-Skyline Query process (DCRSQ process), for supporting RSQ and CRSQ in mobile environments. By considering the mobility, the proposed approach can predict the time when an object falls in the query range and ignore more irrelevant information when deriving the results, thus saving the computation overhead. The proposed approach, DCRSQ process, is analyzed on cost and validated with extensive simulated experiments. The results show that DCRSQ process outperforms the existing approaches in different scenarios and aspects.

PRS: efficient range skyline computation on massive data via presorting

In many applications, range skyline query is an important operation to find the interesting tuples in a potentially huge data space. Given selection condition, range skyline query returns tuples both satisfying the specified selection condition and not dominated by other satisfying tuples. It is found that most of the existing skyline algorithms do not consider the selection condition. This paper proposes a novel table-scan-based Presorted-table-based Range Skyline (PRS) algorithm to efficiently compute range skyline results on massive data. PRS first presorts the table for early termination. The early termination checking is proposed in this paper, along with the theoretical analysis of scan depth. The selection checking and dominance checking are devised in this paper to skip the unsatisfying or dominated tuples directly. The theoretical analysis proves that the overwhelming majority of candidates can be skipped. The extensive experimental results, conducted on synthetic and real-life data sets, show that PRS outperforms the existing algorithms significantly.