The Farthest Spatial Skyline Queries

MSSQ: Manhattan Spatial Skyline Queries

Given a set of query points, a dynamic skyline query reports all data points that are not dominated by other data points according to the distances between data points and query points. In this paper, we study d ynamic s kyline queries in a large g raph (DSG-query for short). Although dynamic skylines have been studied in Euclidean space [14], road network [5], and metric space [3,6], there is no previous work on dynamic skylines over large graphs. We employ a filter-and-refine framework to speed up the query processing that can answer DSG-query efficiently. We propose a novel pruning rule based on graph properties to derive the candidates for DSG-query, that are guaranteed not to introduce false negatives. In the refinement step, with a carefully-designed index structure, we compute short path distances between vertices in O(H), where H is the number of maximal hops between any two vertices. Extensive experiments demonstrate that our methods outperform existing algorithms by orders of magnitude.

This paper aims at answering the so called why-not questions in reverse skyline queries. A reverse skyline query retrieves all data points whose dynamic skylines contain the query point. We outline the benefit and the semantics of answering why-not questions in reverse skyline queries. In connection with this, we show how to modify the why-not point and the query point to include the why-not point in the reverse skyline of the query point. We then show, how a query point can be positioned safely anywhere within a region (i.e., called safe region) without losing any of the existing reverse skyline points. We also show how to answer why-not questions considering the safe region of the query point. Our approach efficiently combines both query point and data point modification techniques to produce meaningful answers. Experimental results also demonstrate that our approach can produce high quality explanations for why-not questions in reverse skyline queries.

The range-based skyline (r-skyline) query on road networks retrieves the skyline objects of taking each point within a road region as a query point, in terms of objects' spatial and non-spatial attributes. In this paper, we systematically carry out the study of why-not questions on the r-skyline query in the road network (abbreviated as the why-not RSQ problem). We present three modification strategies, including modifying the query range, modifying the why-not point, and modifying both of them, for the why-not RSQ problem. In particular, a suite of newly presented effective concepts/techniques are leveraged, such as the concepts of skyline scope and skyline dominance region, non-spatial attribute modification pruning, and G-tree index. Extensive experiments using both real and synthetic data sets demonstrate the performance of our proposed algorithms.

Skyline computation for frequent queries in update intensive environment

As more data-intensive applications emerge, advanced retrieval semantics, such as ranking and skylines, have attracted attention. Geographic information systems are such an application with massive spatial data. Our goal is to efficiently support skyline queries over massive spatial data. To achieve this goal, we first observe that the best known algorithm VS 2, despite its claim, may fail to deliver correct results. In contrast, we present a simple and efficient algorithm that computes the correct results. To validate the effectiveness and efficiency of our algorithm, we provide an extensive empirical comparison of our algorithm and VS 2 in several aspects.

MapReduce skyline query processing with partitioning and distributed dominance tests

This paper presents a system framework to support continuous nearest surrounder (NS) queries in moving object environments. NS query finds the nearest objects at individual distinct angles from a query point. This query distinguishes itself from other conventional spatial queries such as range queries and nearest neighbor queries by considering both distance and angular aspects of objects with respect to a query point. One of NS query applications is to monitor the nearest objects around an observation point. In our framework, a centralized server is dedicated to collect object location updates, to determine affected NS queries of each object location update, to compute the incremental result change of affected queries and to deliver result updates to corresponding interested users/applications that initiate the queries. In particular, we propose algorithms namely, safe region formation and partial query evaluation, that can significantly improve the system performance. Through simulations, we validate our proposed algorithms over a wide range of settings

Skyline queries are useful in decision making applications. Skyline queries in highly mobile distributed environments have attracted many attentions recently due to the development of mobile internet device. The properties of distributed computing make skyline queries more complicated especially in any subspace. Conventional skyline algorithms do not support subspace skyline queries in distributed environment. In this paper, we focus on how to perform distributed skyline queries in any subspace according to user preference. So we propose a system model in a mobile and distributed environment. An efficient parallel algorithm for processing the Subspace Skyline Query (SSQ) using MapReduce is applied to the system model. This algorithm can report skyline points in any subspace. Meanwhile, a pruning strategy is also proposed in order to reduce the network communication and minimize the response time. We conduct experiments on real and synthetic data. Experimental results indicate that our SSQ algorithm is much more efficient. Furthermore, the pruning strategy can further improve the performance of the algorithm.

Skyline query is important in the circumstances that require the support of decision making. The existing work on skyline queries is based mainly on the assumption that the datasets are static. Querying skylines over moving objects, however, is also important and requires more attention. In this paper, we propose a framework, namely PRISMO, for processing predictive skyline queries over moving objects that not only contain spatio-temporal information, but also include non-spatial dimensions, such as other dynamic and static attributes. We present two schemes, RBBS (branch-and-bound skyline with rescanning and repacking) and TPBBS (time-parameterized branchand-bound skyline), each with two alternative methods, to handle predictive skyline computation. The basic TPBBS is further extended to TPBBSE (TPBBS with expansion) to enhance the performance of memory space consumption and CPU time. Our schemes are flexible and thus can process point, range, and subspace predictive skyline queries. Extensive experiments show that our proposed schemes can handle predictive skyline queries effectively, and that TPBBS significantly outperforms RBBS.

In many decision-making applications, the skyline query is frequently used to find a set of dominating data points (called skyline points) in a multi-dimensional dataset. In a high-dimensional space skyline points no longer offer any interesting insights as there are too many of them. In this paper, we introduce a novel metric, called skyline frequency that compares and ranks the interestingness of data points based on how often they are returned in the skyline when different number of dimensions (i.e., subspaces) are considered. Intuitively, a point with a high skyline frequency is more interesting as it can be dominated on fewer combinations of the dimensions. Thus, the problem becomes one of finding top-k frequent skyline points. But the algorithms thus far proposed for skyline computation typically do not scale well with dimensionality. Moreover, frequent skyline computation requires that skylines be computed for each of an exponential number of subsets of the dimensions. We present efficient approximate algorithms to address these twin difficulties. Our extensive performance study shows that our approximate algorithm can run fast and compute the correct result on large data sets in high-dimensional spaces.

Over the past years, the skyline query has already attracted wide attention in database community. In order to meet different preferences for users, the skycube computation is proposed to compute skylines, or cuboids, on all possible non-empty dimension subsets. The key issue of computing skycube is how to share computation among multiple related cuboids, which classified into sharing strict space dominance and sharing space incomparability. However, state-of-the-art algorithm only leverages sharing strict space dominance to compute skycube. This paper aims to design a more efficient skycube algorithm that shares computation among multiple related cuboids. We first propose a set of rules named identical partitioning (IP) for constructing a novel structure VSkyTree. Moreover, we present the reusing principle, which utilizes both sharing strict space dominance and sharing space incomparability by reusing VSkyTree on parent cuboids to compute child cuboids. Then, in top-down fashion, we design an efficient skycube computation algorithm RSkycube based on the reusing principle. Our experimental results indicate that our algorithm RSkycube significantly outperforms state-of-the-art skycube computation algorithm on both synthetic and real datasets.

Skyline queries are currently the most notable type of multi-criteria search algorithm. A skyline query returns all of the data points in a given a dataset that are not dominated by other data points. However, this type of query is limited by the fact that the number of results cannot be controlled. In some cases, this can result in an excessive number of results, whereas other cases result in an insufficient number of results. In this study, we propose a scheme referred to as m-representative skyline queries to provide control over the number of results that are returned. We also developed a naive algorithm and a sorted algorithm to provide additional control over the search process. Experiment results demonstrate the efficacy of the proposed approach.

In this paper, we present our research on similarity search problems. Similarity search problems define the distances between data points and a given query point Q, efficiently and effectively selecting data points which are closest to Q. It can be applied in various data mining fields. In many applications, information similar to multiple queries is required. In this paper, we explore the meaning of K nearest neighbors from a new perspective, define the distance between a data point and a query point set, and propose an algorithm to find nearest neighbors to multiple queries with possibly different degrees of importance. Our approach works for both full similarities and partial similarities in subsets of dimensions.

A spatial k-NN query returns k nearest points in a point dataset to a given query point. To measure the distance between two points, most of the literature focuses on the Euclidean distance or the network distance. For many applications, such as wildlife movement, it is necessary to consider the surface distance, which is computed from the shortest path along a terrain surface. In this paper, we investigate the problem of efficient surface k-NN (sk-NN) query processing. This is an important yet highly challenging problem because the underlying environment data can be very large and the computational cost of finding the shortest path on a surface can be very high. To minimize the amount of surface data to be used and the cost of surface distance computation, a multi-resolution surface distance model is proposed in this paper to take advantage of monotonic distance changes when the distances are computed at different resolution levels. Based on this innovative model, sk-NN queries can be processed efficiently by accessing and processing surface data at a just-enough resolution level within a just-enough search region. Our extensive performance evaluations using real world datasets confirm the efficiency of our proposed model.