Skyline Computation Research Articles

Skyline distance: a measure of multidimensional competence

Skyline has been widely recognized as being useful for multi-criteria decision-making applications. While most of the existing work computes skylines in various contexts, in this paper, we consider a novel problem: how far away a point is from the skyline? We propose a novel notion of skyline distance that measures the minimum cost of upgrading a point to the skyline given a cost function. Skyline distance can be regarded as a measure of multidimensional competence and can be used to rank possible choices in recommendation systems. Computing skyline distances efficiently is far from trivial and cannot be handled by any straightforward extension of the existing skyline computation methods. To tackle this problem, we systematically explore several directions. We first present a dynamic programming method. Then, we investigate the boundary of skylines and develop a sort-projection method that utilizes the skyline boundary in calculating skyline distances. Last, we develop a space partitioning method to further improve the performance. We report extensive experiment results which show that our methods are efficient and scalable.

PRISMO: predictive skyline query processing over moving objects

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.

Efficient k-dominant Skyline Computation for High Dimensional Space with Domination Power Index

Skyline queries have recently attracted a lot of attention for its intuitive query formulation. It can act as a filter to discard sub-optimal objects. However, a major drawback of skyline is that, in datasets with many dimensions, the number of skyline objects becomes large and no longer offer any interesting insights. To solve the problem, recently k -dominant skyline queries have been introduced, which can reduce the number of skyline objects by relaxing the definition of the dominance. This paper addresses the problem of k -dominant skyline objects for high dimensional dataset. We propose algorithms for k - dominant skyline computation. Our algorithms reduce the pairwise comparison between the k -dominant skyline objects and the dataset. Through extensive experiments with real and synthetic datasets, we confirm that our algorithms can efficiently compute k -dominant skyline queries.

A Filter Lining Scheme for Efficient Skyline Computation

The skyline of a multidimensional data set is the maximal subset whose elements are not dominated by other elements of the set. Skyline computation is considered to be very useful for a decision making system that deals with multidimensional data analyses. Recently, a great deal of interests has been shown to improve the performance of skyline computation algorithms. In order to speedup, the number of comparisons between data elements should be reduced. In this paper, we propose a filter lining scheme to accomplish such objectives. The scheme divides the multidimensional data space into angle-based partitions, and places a filter for each partition, and then connects them together in order to establish the final filter line. The filter line can be used to eliminate data, that are not part of the skyline, from the original data set in the preprocessing stage. The filter line is adaptively improved during the data scanning stage. In addition, skylines are computed for each remaining data partition, and are then merged to form the final skyline. Our scheme is an improvement of the previously reported simple preprocessing scheme using simple filters. The performance of the scheme is shown by experiments.

Group skyline computation

Given a multi-dimensional dataset of tuples, skyline computation returns a subset of tuples that are not dominated by any other tuples when all dimensions are considered together. Conventional skyline computation, however, is inadequate to answer various queries that need to analyze not just individual tuples of a dataset but also their combinations. In this paper, we study group skyline computation which is based on the notion of dominance relation between groups of the same number of tuples. It determines the dominance relation between two groups by comparing their aggregate values such as sums or averages of elements of individual dimensions, and identifies a set of skyline groups that are not dominated by any other groups. We investigate properties of group skyline computation and develop a group skyline algorithm GDynamic which is equivalent to a dynamic algorithm that fills a table of skyline groups. Experimental results show that GDynamic is a practical group skyline algorithm.

위치 제약 조건을 고려한 효율적인 스카이라인 계산

다차원 데이터 집합에서 서로 지배되지 않는 데이터로 구성된 부분 집합을 스카이라인이라고 한다. 스카이라인 계산은 다차원 데이터를 대상으로 한 의사결정에 유용한 연산이다. 그러나 스카이라인이 지나치게 큰 경우 이를 의사결정에 활용하기 어려울 수 있다. 본 연구에서는 사용자가 제시하는 원점의 이동, 원점으로부터의 각도와 거리 정보를 반영하여 스카이라인의 일부를 효율적으로 구하는 방법을 모색하였다. 제안한 알고리즘은 스카이라인에 속하지 않는 데이터를 신속하게 제거해가며, 사용자의 요구를 점진적으로 반영할 수 있다는 특징을 갖는다. 알고리즘의 효율성은 실험을 통해 검증하였다. The skyline of a multi-dimensional data set is a subset that consists of the data that are not dominated by other members of the set. Skyline computation can be very useful for decision making for multi-dimensional data set. However, in case that the skyline is very large, it may not be much useful for decision making. In this paper, we propose an algorithm for computing a part of the skyline considering location restrictions that the user provides, such as origin movement, degree ranges and/or distances from the origin. The algorithm eliminates noncandidate data rapidly, and returns in order the skyline points that satisfy the user's requests. We show that the algorithm is efficient by experiments.

Supporting efficient distributed skyline computation using skyline views

Skyline queries return a set of objects, or a skyline, that are not dominated by any other objects. While providing users with an intuitive query formulation, the skyline queries may incur too many results, especially, for high dimensional data. To tackle this problem, subspace skyline queries, which deals with a subset of dimensions, have been recently studied. To identify interesting skylines, users can iteratively refine multiple relevant subspaces for skyline queries. Existing work focuses primarily on supporting efficient subspace skyline computation in centralized databases. In clear contrast, this paper aims to address subspace skyline computation in distributed environments such as the Web. Toward this goal, we make use of pre-computed subspace skylines as views in databases, called skyline views. Specifically, we propose distributed subspace skyline computation which minimizes the total access cost by leveraging the skyline views. Our experimental results validate that our proposed algorithms significantly outperform state-of-the-art algorithms in extensive synthetic datasets.

A survey of skyline processing in highly distributed environments

During the last decades, data management and storage have become increasingly distributed. Advanced query operators, such as skyline queries, are necessary in order to help users to handle the huge amount of available data by identifying a set of interesting data objects. Skyline query processing in highly distributed environments poses inherent challenges and demands and requires non-traditional techniques due to the distribution of content and the lack of global knowledge. This paper surveys this interesting and still evolving research area, so that readers can easily obtain an overview of the state-of-the-art. We outline the objectives and the main principles that any distributed skyline approach has to fulfill, leading to useful guidelines for developing algorithms for distributed skyline processing. We review in detail existing approaches that are applicable for highly distributed environments, clarify the assumptions of each approach, and provide a comparative performance analysis. Moreover, we study the skyline variants each approach supports. Our analysis leads to a taxonomy of existing approaches. Finally, we present interesting research topics on distributed skyline computation that have not yet been explored.

Asymptotically efficient algorithms for skyline probabilities of uncertain data

Skyline computation is widely used in multicriteria decision making. As research in uncertain databases draws increasing attention, skyline queries with uncertain data have also been studied. Some earlier work focused on probabilistic skylines with a given threshold; Atallah and Qi [2009] studied the problem to compute skyline probabilities for all instances of uncertain objects without the use of thresholds, and proposed an algorithm with subquadratic time complexity. In this work, we propose a new algorithm for computing all skyline probabilities that is asymptotically faster: worst-case O(n √ n log n ) time and O(n) space for 2D data; O ( n 2−1/d log d −1 n ) time and O(n log d −2 n ) space for d -dimensional data. Furthermore, we study the online version of the problem: Given any query point p (unknown until the query time), return the probability that no instance in the given data set dominates p . We propose an algorithm for answering such an online query for d -dimensional data in O(n 1−1/ d log d −1 n ) time after preprocessing the data in O(n 2−1/d log d −1 ) time and space.

Multi-attribute optimization in service selection

As multiple service providers may compete to offer the same functionality with different quality of service (e.g., latency, fee, and reputation), a key issue in service computing is selecting service providers with the best user desired quality. Existing service selection approaches mostly rely on computing a predefined objective function. When multiple quality criteria are considered, users are required to express their preference over different (and sometimes conflicting) quality attributes as numeric weights. This is a rather demanding task and an imprecise specification of the weights could miss user desired services. We propose a multi-attribute optimization approach to tackle this issue. In particular, we develop a novel concept, called service skyline, and a set of service skyline computation techniques that return a set of most interesting service providers. These providers are non-dominant in all user interested quality attributes. Thus, the service skyline ensures that the user desired providers will be included. Analytical and experimental studies justify the performance of the proposed techniques. The relative small sizes of the service skylines also make it practical for service users to make selections from them.

Microeconomic analysis using dominant relationship analysis

The concept of dominance has recently attracted much interest in the context of skyline computation. Given an N-dimensional data set S, a point p is said to dominate q if p is better than q in at least one dimension and equal to or better than it in the remaining dimensions. In this article, we propose extending the concept of dominance for business analysis from a microeconomic perspective. More specifically, we propose a new form of analysis, called Dominant Relationship Analysis (DRA), which aims to provide insight into the dominant relationships between products and potential buyers. By analyzing such relationships, companies can position their products more effectively while remaining profitable. To support DRA, we propose a novel data cube called DADA (Data Cube for Dominant Relationship Analysis), which captures the dominant relationships between products and customers. Three types of queries called Dominant Relationship Queries (DRQs) are consequently proposed for analysis purposes: (1) Linear Optimization Queries (LOQ), (2) Subspace Analysis Queries (SAQ), and (3) Comparative Dominant Queries (CDQ). We designed efficient algorithms for computation, compression and incremental maintenance of DADA as well as for answering the DRQs using DADA. We conducted extensive experiments on various real and synthetic data sets to evaluate the technique of DADA and report results demonstrating the effectiveness and efficiency of DADA and its associated query-processing strategies.

Constrained Skyline Query Processing against Distributed Data Sites

The skyline of a multidimensional point set is a subset of interesting points that are not dominated by others. In this paper, we investigate constrained skyline queries in a large-scale unstructured distributed environment, where relevant data are distributed among geographically scattered sites. We first propose a partition algorithm that divides all data sites into incomparable groups such that the skyline computations in all groups can be parallelized without changing the final result. We then develop a novel algorithm framework called PaDSkyline for parallel skyline query processing among partitioned site groups. We also employ intragroup optimization and multifiltering technique to improve the skyline query processes within each group. In particular, multiple (local) skyline points are sent together with the query as filtering points, which help identify unqualified local skyline points early on a data site. In this way, the amount of data to be transmitted via network connections is reduced, and thus, the overall query response time is shortened further. Cost models and heuristics are proposed to guide the selection of a given number of filtering points from a superset. A cost-efficient model is developed to determine how many filtering points to use for a particular data site. The results of an extensive experimental study demonstrate that our proposals are effective and efficient.

Parallel skyline computation on multicore architectures

With the advent of multicore processors, it has become imperative to write parallel programs if one wishes to exploit the next generation of processors. This paper deals with skyline computation as a case study of parallelizing database operations on multicore architectures. First we parallelize three sequential skyline algorithms, BBS, SFS, and SSkyline, to see if the design principles of sequential skyline computation also extend to parallel skyline computation. Then we develop a new parallel skyline algorithm PSkyline based on the divide-and-conquer strategy. Experimental results show that all the algorithms successfully utilize multiple cores to achieve a reasonable speedup. In particular, PSkyline achieves a speedup approximately proportional to the number of cores when it needs a parallel computation the most.

A clustering based approach for skyline diversity

Skyline query processing has recently received a lot of attention in database and data-mining communities. To the best of our knowledge, the existing researches mainly focus on considering how to efficiently return the whole skyline set. However, when the cardinality and dimensionality of input objects increase, the number of skylines grows exponentially, and hence this “huge” skyline set is completely useless to users. On the other hand, in most real applications, the objects are usually clustered, and therefore many objects have similar attribute values. Motivated by the above facts, in this paper, we present a novel type of SkyCluster query to capture the skyline diversity and improve the usefulness of skyline result. The SkyCluster query integrates K-means clustering into skyline computation, and returns K “representative” and “diverse” skyline objects to users. To process such query, a straightforward approach is to simply integrate the existing techniques developed for skyline-only and clustering-only together. But this approach is costly since both skyline computation and K-means clustering are all CPU-sensitive. We propose an efficient evaluation approach which is based on the circinal index to seamlessly integrate subspace skyline computation, K-means clustering and representatives selection. Also, we present a novel optimization heuristic to further improve the query performance. Experimental study shows that our approach is both efficient and effective.

VSkyline

A dominance test, which decides the dominance relationship between tuples, is a core operation in skyline computation. Optimizing dominance tests can thus improve the performance of all existing skyline algorithms. Towards this goal, this paper proposes a vectorization of dominance tests in SIMD architectures. Specifically, our vectorization can perform the dominance test of multiple consecutive dimensions in parallel, thereby achieving a speedup of SIMD parallelism degree in theory. However, achieving such performance gain is non-trivial due to complex control dependencies within the dominance test. To address this problem, we devise an efficient vectorization, called VSkyline, which performs the dominance test with SIMD instructions by determining incomparability in a block of four dimensional values. Experimental results using a performance monitor show that VSkyline considerably reduces the numbers of both executed instructions and branch mispredictions.

ZINC

We present a new indexing method named ZINC (for Z-order Indexing with Nested Code) that supports efficient skyline computation for data with both totally and partially ordered attribute domains. The key innovation in ZINC is based on combining the strengths of the ZB-tree, which is the state-of-the-art index method for computing skylines involving totally ordered domains, with a novel, nested coding scheme that succinctly maps partial orders into total orders. An extensive performance evaluation demonstrates that ZINC significantly outperforms the state-of-the-art TSS indexing scheme for skyline queries.

An efficient skyline framework for matchmaking applications

In this article, we present a skyline-based matchmaking framework. The current method of carrying out the matchmaking procedure identifies items based on users’ specifications. We rethink matchmaking procedures in such a way that they can find items that can satisfy a specific computing demand from a user and recommend a collection of better candidates among the identified items. This endows a user with the right of choice on deciding the best-possible items. We approach the recommendation from the perspective of skyline computation and present an efficient skyline algorithm that gathers interesting item candidates efficiently. To devise an efficient sequential skyline algorithm, we adopt (i) lattice-based indexing using a lattice composition technique and (ii) an optimized dominance-check algorithm. Moreover, we parallelize the algorithm using breadth-first-search (BFS). Our extensive experimental results show that our algorithm outperforms current state-of-the-art algorithms, and the speedup factor of the parallelized algorithm is near-linear.

An Efficient Grid Method for Continuous Skyline Computation over Dynamic Data Set

Skyline queries are an important new search capability for multi-dimensional databases. Most of the previous works have focused on processing skyline queries over static data set. However, most of the real applications deal with the dynamic data set. Since dynamic data set constantly changes as time passes, the continuous skyline computation over dynamic data set becomes ever more complicated. In this paper, we propose a multiple layer grids method for continuous skyline computation (MLGCS) that maintains multiple layer grids to manage the dynamic data set. The proposed method divides the work space into multiple layer grids and creates the skyline influence region in the grid of each layer. In the continuous environment, the continuous skyline queries are only handled when the updating data points are in the skyline influence region of each layer grid. Experiments based on various data distributions show that our proposed method outperforms the existing methods.

Continuous k-Dominant Skyline Computation by Using Divide and Conquer Strategy

Skyline objects in a database are objects that are not dominated by any other object in the database. Skyline queries retrieve a set of skyline objects so that the user can choose promising objects from them and make further inquiries. Therefore, such skyline queries are important for several database applications. However, a skyline query often retrieves too many objects to analyze intensively especially for high-dimensional dataset. Recently, k-dominant skyline queries have been introduced, which can reduce the number of retrieved objects by relaxing the definition of the dominance. On the other hand, the maintenance of k-dominant skyline objects under continuous updates is much more difficult compared to conventional skyline objects. This paper addresses the problem of efficient maintenance of k-dominant skyline objects of frequently updated database. We propose an algorithm based on divide and conquer strategy for maintaining k-dominant skyline objects. Intensive experiments using real and synthetic datasets demonstrated that our method is efficient and scalable.

Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation

Skyline computation in databases has been a hot topic in the literature because of its interesting applications. The basic idea is to find non-dominated values within a database. The task is mainly a multi-objective optimization process as described in this paper. This motivated for our approach that employs a multi-objective genetic algorithm based clustering approach to find the pareto-optimal front which allows us to locate skylines within a given data. To tackle large data, we simply split the data into manageable subsets and concentrate our analysis on the subsets instead of the whole data at once. The proposed approach produced interesting results as demonstrated by the outcome from the conducted experiments.