Skyline Query Processing Research Articles

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.

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.

Efficient Routing of Subspace Skyline Queries over Highly Distributed Data

Data generation increases at highly dynamic rates, making its storage, processing, and update costs at one central location excessive. The P2P paradigm emerges as a powerful model for organizing and searching large data repositories distributed over independent sources. Advanced query operators, such as skyline queries, are necessary in order to help users handle the huge amount of available data. A skyline query retrieves the set of nondominated data points in a multidimensional data set. Skyline query processing in P2P networks poses inherent challenges and demands nontraditional techniques, due to the distribution of content and the lack of global knowledge. Relying on a superpeer architecture, we propose a threshold-based algorithm, called SKYPEER and its variants, for efficient computation of skyline points in arbitrary subspaces, while reducing both computational time and volume of transmitted data. Furthermore, we address the problem of routing skyline queries over the superpeer network and we propose an efficient routing mechanism, namely SKYPEER+, which further improves the performance by reducing the number of contacted superpeers. Finally, we provide an extensive experimental evaluation showing that our approach performs efficiently and provides a viable solution when a large degree of distribution is required.

L-SkyDiv query: Effectively improve the usefulness of skylines

Skyline query processing has recently received a lot of attention in database community. Given a set of multi-dimensional objects, the skyline query finds the objects that are not dominated by others. To the best of our knowledge, the existing researches mainly focus on how to efficiently return the whole skyline set. However, as the cardinality and dimensionality of input dataset increase, the number of skylines grows exponentially, and hence this “huge” skyline set is completely useless to users. Motivated by the above fact, in this paper, we present a novel type of l-SkyDiv query, which only returns l skylines having maximum diversity, to improve the usefulness of skyline result. Also, we prove that the l-SkyDiv query belongs to the NP-Hard problem theoretically, and propose three efficient heuristic algorithms whose time complexities are polynomial to fast implement the proposed query. Furthermore, we present detailed theoretical analyses and extensive experiments, demonstrating that our algorithms are both efficient and effective.

An Efficient Method for Parallel Processing of Skyline Queries

An Efficient Method for Parallel Processing of Skyline Queries

Approximate Skyline Query Processing Algorithm in Wireless Sensor Networks

Due to the limitation of wireless sensor networks in energy resources and the fact that part of Skyline query results can satisfy the users in many applications, this paper proposes an energy efficient approximate Skyline query processing algorithm to save the energy maximally according to the different requirements of applications. The proposed algorithm can compute an approximate Skyline result set only by making partial sensor nodes transmitting their sensing data back. And it is energy efficient because each sensor node transmits its sensing data back or not only depending on the information of itself, without the comparison with other data. Accordingly, communication cost is greatly reduced and network energy is greatly saved. Extensive experiments in simulation environment indicate that the proposed algorithm can process the approximate Skyline queries in wireless sensor networks energy efficiently according to different requirements of applications.

Efficient Distributed Skyline Queries for Mobile Applications

In this paper, we consider skyline queries in a mobile and distributed environment, where data objects are distributed in some sites (database servers) which are interconnected through a high-speed wired network, and queries are issued by mobile units (laptop, cell phone, etc.) which access the data objects of database servers by wireless channels. The inherent properties of mobile computing environment such as mobility, limited wireless bandwidth, frequent disconnection, make skyline queries more complicated. We show how to efficiently perform distributed skyline queries in a mobile environment and propose a skyline query processing approach, called efficient distributed skyline based on mobile computing (EDS-MC). In EDS-MC, a distributed skyline query is decomposed into five processing phases and each phase is elaborately designed in order to reduce the network communication, network delay and query response time. We conduct extensive experiments in a simulated mobile database system, and the experimental results demonstrate the superiority of EDS-MC over other skyline query processing techniques on mobile computing.

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.

Efficient Preprocessing of Subspace Skyline Queries in P2P Networks

PDF HTML阅读 XML下载 导出引用 引用提醒 有效预处理P2P 网络中的子空间skyline 查询 DOI: 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: Supported by the National Natural Science Foundation of China under Grant No.60303008 (国家自然科学基金); the National Basic Research Program of China under Grant No.2005CB321905 (国家重点基础研究发展计划(973)) Efficient Preprocessing of Subspace Skyline Queries in P2P Networks Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:多维空间的skyline 查询处理是近年来数据库领域的一个研究重点和热点.Vlachou 等人首次考虑如何在P2P 网络中有效进行子空间上的skyline 查询,并提出“扩展skyline 集合”的概念来减少预处理时的网络传输量.然而实验评估表明,扩展skyline 集合只能有限地减少子空间skyline 查询预处理的数据传输量.基于此,提出一种缩减处理时数据传输量的有效方法TPAOSS(three-phase algorithm for optimizing skyline scalar).TPAOSS 算法根据全空间skyline 集合与子空间skyline 集合间的语义关系分3 个阶段来传输必要的数据,其中第1 阶段发送全空间skyline 对象;第2 阶段接收种子skyline 对象;而第3 阶段基于Bloom filter 技术发送种子skyline 对象在子空间上的重复对象.为了降低第2 阶段的数据传输量,给出两种接收种子skyline 对象的有效策略.理论分析和实验评估结果表明,所给出的算法具有有效性和实用性. Abstract:Skyline query processing has recently received a lot of attention in database community. Lately, Akrivi Vlachou and D. Christos considered how to efficiently process subspace skyline queries in peer-to-peer networks,and proposed the concept of “extended skyline set” to reduce the volume of data transferred in the preprocessing phase for the first time. However, the experimental evaluation shows that this data structure is extremely limited in reducing the volume of data transferred in the preprocessing phase. Motivated by these facts, this paper proposes an efficient algorithm, i.e. TPAOSS (three-phase algorithm for optimizing skyline scalar), to reduce the volume of data transferred in the preprocessing phase. TPAOSS algorithm is based on the semantic relationship between full-space skylines and subspace skylines, and transfers the data through three phases. In the first phase, it only sends full-space skylines. In the second phase, it receives seed skylines. In the third phase, it exploits Bloom filter technology to obtain and send the replicated objects with seed skylines on the subspaces. Particularly, the paper presents two efficient strategies to reduce the volume of data transferred in the second phase. Furthermore, it presents detailed theoretical analyses and extensive experiments that demonstrate these algorithms are both efficient and effective. 参考文献 相似文献 引证文献

Efficient skyline query processing in wireless sensor networks

How to process a skyline query efficiently has received considerable attention in recent years. A skyline query identifies a set of non-dominated data records in a multidimensional dataset. Whereas most previous studies have resolved this problem in a centralized environment, this work considers it in a distributed sensor network environment. An algorithm, known as Skyline Sensor Algorithm (SkySensor), is presented to efficiently retrieve skyline results from a sensor network. A cluster-based architecture is designed in SkySensor to collect all sensor readings. A pruning method is then proposed to progressively sift out the skyline results from the sensor network. SkySensor avoids the need of collecting data from all sensors in the network, which is an extremely expensive action, when searching for the skyline results. The performance study indicates that SkySensor is highly efficient, and significantly outperforms previous methods in processing skyline queries.

An almost Linear I/O Algorithm for Skyline Query

Skyline query processing has recently received a lot of attention in database community. Even though there existed several algorithms in the field of skyline query, none of them has linear I/O complexity. In this paper, the existed algorithms have been summarized, and a new kind of external memory skyline query algorithm has been presented. Moreover, the reliability of algorithm has been validated from experiments and theory, the I/O complexity and the inner memory complexity of the algorithm is both almost linear.

Energy Efficient Skyline Query Processing in Wireless Sensor Networks

In sensor networks, many studies have been proposed to process in-network aggregation efficiently. Unlike general aggregation queries, skyline query processing compares multi-dimensional data for the result. Therefore, it is very difficult to process the skyline queries in sensor networks. It is important to filter unnecessary data for energy-efficient skyline query processing. Existing approaches get rid of unnecessary data transmission by deploying filters to whole sensors. However, network lifetime is reduced due to energy consumption for transmitting filters. In this paper, we propose a lazy filtering-based in-network skyline query processing algorithm to reduce energy consumption by transmitting filters. Our algorithm creates the skyline filter table (SFT) in the data gathering process which sends data from sensor nodes to the base station and filters out unnecessary data transmissions using it. The experimental results show that our algorithm reduces false positive by 53% and improves network lifetime by 44% on average over the existing method.

Efficient skycube computation using point and domain-based filtering

Skyline queries have been increasingly used in multi-criteria decision making and data mining applications. They retrieve a set of interesting points from a potentially large set of data points. A point is said to be interesting if it is not dominated by any other point. Skyline cube (skycube) consists of skylines of all possible non-empty subsets of a given set of dimensions. In this paper, we propose two algorithms for computing skycube using bitmaps that are derivable from indexes. The Point-based skycube algorithm is an improvement over the existing Bitmap algorithm, extended to compute skycube. The Point-based algorithm processes one point at a time to check for skylines in all subspaces. The Domain-based skycube algorithm views points as value combinations and probes entire search space for potential skyline points. It significantly reduces bitmap access for low cardinality dimensions. Our experimental study shows that the two algorithms strictly dominate, or at least comparable to, the current skycube algorithm in most of the cases, suggesting that such an approach could be a useful addition to the set of skyline query processing techniques.

A fast and progressive algorithm for skyline queries with totally- and partially-ordered domains

We devise a skyline algorithm that can efficiently mitigate the enormous overhead of processing millions of tuples on totally- and partially-ordered domains (henceforth, TODs and PODs). With massive datasets, existing techniques spend a significant amount of time on a dominance comparison because of both a large number of skyline points and the unprogressive method of skyline computing with PODs. (If data has high dimensionality, the situation is undoubtedly aggravated.) The progressiveness property turns out to be the key feature for solving all remaining problems. This article presents a FAST-SKY algorithm that deals successfully with these two obstacles and improves skyline query processing time strikingly, even with high-dimensional data. Progressive skyline evaluation with PODs is guaranteed by new index structures and topological sorting order. A stratification technique is adopted to index data on PODs, and we propose two new index structures: stratified R-trees (SR-trees) for low-dimensional data and stratified MinMax treaps (SM-treaps) for high-dimensional data. A fast dominance comparison is achieved by using a reporting query instead of a dominance query, and a dimensionality reduction technique. Experimental results suggest that in general cases (anti-correlated and uniform distributions) FAST-SKY is orders of magnitude faster than existing algorithms.

Z-SKY: an efficient skyline query processing framework based on Z-order

Given a set of data points in a multidimensional space, a skyline query retrieves those data points that are not dominated by any other point in the same dataset. Observing that the properties of Z-order space filling curves (or Z-order curves) perfectly match with the dominance relationships among data points in a geometrical data space, we, in this paper, develop and present a novel and efficient processing framework to evaluate skyline queries and their variants, and to support skyline result updates based on Z-order curves. This framework consists of ZBtree, i.e., an index structure to organize a source dataset and skyline candidates, and a suite of algorithms, namely, (1) ZSearch, which processes skyline queries, (2) ZInsert, ZDelete and ZUpdate, which incrementally maintain skyline results in presence of source dataset updates, (3) ZBand, which answers skyband queries, (4) ZRank, which returns top-ranked skyline points, (5) k-ZSearch, which evaluates k-dominant skyline queries, and (6) ZSubspace, which supports skyline queries on a subset of dimensions. While derived upon coherent ideas and concepts, our approaches are shown to outperform the state-of-the-art algorithms that are specialized to address particular skyline problems, especially when a large number of skyline points are resulted, via comprehensive experiments.

Efficient Processing of Metric Skyline Queries

Skyline query is of great importance in many applications, such as multi-criteria 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 (i.e. their attribute vectors are fixed), whereas several recent work 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 (i.e. not limited to the Euclidean space). We illustrate an efficient and effective pruning mechanism to answer metric skyline queries through a metric index. Most importantly, we formalize the query performance of the metric skyline query in terms of the pruning power, by a cost model, in light of which we construct an optimized metric index aiming to maximize the pruning power of metric skyline queries. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed pruning techniques as well as the constructed index in answering metric skyline queries.

Skyline Query Processing

This paper gives a survey on current Skyline queries techniques. It first introduces the background in which Skyline queries appear. Then it presents in-memory algorithms in Skyline query problem. Facing to the situation of large data sets, it further presents the techniques about Skyline query processing by two cases, with or without indices respectively. Evaluations of Skyline query methods are discussed after that. This paper also introduces the novel query model-SKYCUBE which is applied to process multi-Skyline queries in various subspaces and related research based on it. Additionally, it introduces the efficient algorithm to solve Skyline queries in various applicant environment and the extension of the Skyline query processing. Finally, this paper proposes several directions for further research on the topic of Skyline query processing.

Skyframe: a framework for skyline query processing in peer-to-peer systems

This paper looks at the processing of skyline queries on peer-to-peer (P2P) networks. We propose Skyframe, a framework for efficient skyline query processing in P2P systems, which addresses the challenges of quick response time, low network communication cost and query load balancing among peers. Skyframe consists of two querying methods: one is optimized for network communication while the other focuses on query response time. These methods are different in the way in which the query search space is defined. In particular, the first method uses a high dominating point that has a large dominating region to prune the search space to achieve a low cost in network communication. On the other hand, the second method relaxes the search space in order to allow parallel query processing to speed up query response. Skyframe achieves query load balancing by both query load conscious data space splitting/merging during the join/departure of nodes and dynamic load migration. We further show how to apply Skyframe to both the P2P systems supporting multi-dimensional indexing and the P2P systems supporting single-dimensional indexing. Finally, we have conducted extensive experiments on both real and synthetic data sets over two existing P2P systems: CAN (Ratnasamy in A scalable content-addressable network. In: Proceedings of SIGCOMM Conference, pp. 161---172, 2001) and BATON (Jagadish et al. in A balanced tree structure for peer-to-peer networks. In: Proceedings of VLDB Conference, pp. 661---672, 2005) to evaluate the effectiveness and scalability of Skyframe.

Reverse skyline search in uncertain databases

Reverse skyline queries over uncertain databases have many important applications such as sensor data monitoring and business planning. Due to the wide existence of uncertainty in many real-world data, answering reverse skyline queries accurately and efficiently over uncertain data has become increasingly important. In this article, we formalize the probabilistic reverse skyline query over uncertain data, in both monochromatic and bichromatic cases, and propose effective pruning methods, namely spatial pruning and probabilistic pruning , to reduce the search space of the reverse skyline query processing. Moreover, efficient query procedures have been presented seamlessly integrating the proposed pruning methods. Furthermore, a novel query type, namely Probabilistic Reverse Furthest Skyline (PRFS) query, is proposed and tackled under “the larger, the better” dominance semantics of skyline. Variants of probabilistic reverse skyline have been proposed and tackled, including those that return objects with top- k highest probabilities and that retrieve top- k reverse skylines. Extensive experiments demonstrated the efficiency and effectiveness of our approaches with various experimental settings.

In-Network Processing for Skyline Queries in Sensor Networks

Wireless sensor networks can be used in various fields, e.g., military and civil applications. The technique of saving energy to prolong the life of sensor nodes is one of main challenges to resource-constrained sensor networks. Therefore, in-network aggregation of data has been proposed in resource-constrained environments for energy efficiency. Most previous works on in-network aggregation only support a one-dimensional data (e.g., MIN and MAX). To support a multi-dimensional data, the skyline query is used. The skyline query returns a set of points that are not dominated by any other point on all dimensions. The majority of previous skyline query processing methods (e.g., BNL and BBS) work on centralized storage. Centralized query processing methods do not have merits in terms of energy efficiency in high event rate environments. In this paper, we propose new algorithm of in-network processing for the skyline queries. The proposed algorithm reduces the communication cost and evenly distributes load. The experimental results show the advantages of our algorithm over in-network aggregation in terms of improving energy efficiency.