Continuous Probabilistic Subspace Skyline Query Processing Using Grid Projections

As an important type of multidimensional preference query, the skyline query can find a superset of optimal results when there is no given linear function to combine values for all attributes of interest. Its processing has been extensively investigated in the past. While most skyline query processing algorithms are designed based on the assumption that query processing is done for all attributes in a static dataset with deterministic attribute values, some advanced work has been done recently to remove part of such a strong assumption in order to process skyline queries for real-life applications, namely, to deal with data with multi-valued attributes （known as data uncertainty）, to support skyline queries in a subspace which is a subset of attributes selected by the user, and to support continuous queries on streaming data. Naturally, there are many application scenarios where these three complex issues must be considered together. In this paper, we tackle the problem of probabilistic subspace skyline query processing over sliding windows on uncertain data streams. That is, to retrieve all objects from the most recent window of streaming data in a user-selected subspace with a skyline probability no smaller than a given threshold. Based on the subtle relationship between the full space and an arbitrary subspace, a novel approach using a regular grid indexing structure is developed for this problem. An extensive empirical study under various settings is conducted to show the effectiveness and efficiency of our PSS algorithm.     

基于高斯模型的不确定数据流Skyline查询方法

由于数据的动态性及不确定性等特征,使得不确定数据流上Skyline查询研究面临挑战.不确定对象一般采用多元概率密度函数(PDF)表示,现有的不确定数据流Skyline查询方法均采用离散型随机变量建模.然而不确定数据流中的对象可能是连续变化的,离散模型对连续性随机变量难以适用.针对连续PDF建模的不确定数据流Skyline查询进行了研究,提出了基于高斯模型的不确定数据流Skyline查询方法(SGMU),该方法包含2个过程:1)动态高斯建模算法(DGM):对滑动窗口采样并建立高斯模型,将原始的数据流转化为不确定对象PDF的参数流;2)提出了基于高斯树的查询算法(GTS)以建立空间索引结构和执行Skyline查询.实验结果表明,SGMU算法不仅能够对连续型不确定对象进行有效建模以辅助Skyline查询,而且能够有效地减少查询对象个数,提高Skyline查询效率.     

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.     

高速流环境下近似连续k代表轮廓查询算法

k代表轮廓查询是从传统轮廓查询中衍生出来的一类查询.给定多维数据集合D,轮廓查询从D中找到所有不被其他对象支配的对象,将其返回给用户,便于用户结合自身偏好选择高质量对象.然而,轮廓对象规模通常较大,用户需要从大量数据中进行选择,导致选择速度和质量无法得到保证.与传统轮廓查询相比,k代表轮廓查询从所有轮廓对象中选择“代表性”最强的k个对象返回给用户,有效地解决了传统轮廓查询存在的这一问题.给定滑动窗口W和连续查询q,q监听窗口中的数据.当窗口滑动时,查询q返回窗口中,组合支配面积最大的k个对象.现有算法的核心思想是:实时监测当前窗口中的轮廓对象集合,当轮廓对象集合更新时,算法更新k代表轮廓.然而,实时监测窗口中,轮廓集合的计算代价通常较大.此外,当轮廓集合规模较大时,从中选择k代表轮廓的计算代价是同样巨大的,导致已有算法无法在高速流环境下使用.针对上述问题,提出了ρ-近似k代表轮廓查询.为了支持该查询,提出了查询处理框架PAKRS(predict-basedapproximatekrepresentativeskyline).首先,PAKRS利用高速流的特性对当前窗口进行划分,根据划分结...     

Continuous Skyline Queries for Moving Objects

The literature on skyline algorithms has so far dealt mainly with queries of static query points over static data sets. With the increasing number of mobile service applications and users, however, the need for continuous skyline query processing has become more pressing. A continuous skyline query involves not only static dimensions, but also the dynamic one. In this paper, we examine the spatiotemporal coherence of the problem and propose a continuous skyline query processing strategy for moving query points. First, we distinguish the data points that are permanently in the skyline and use them to derive a search bound. Second, we investigate the connection between the spatial positions of data points and their dominance relationship, which provides an indication of where to find changes in the skyline and how to maintain the skyline continuously. Based on the analysis, we propose a kinetic-based data structure and an efficient skyline query processing algorithm. We concisely analyze the space and time costs of the proposed method and conduct an extensive experiment to evaluate the method. To the best of our knowledge, this is the first work on continuous skyline query processing     

一种处理Skyline查询的有效方法

skyline查询是近年来数据库领域的一个研究重点和热点.当系统中存在多个不同维空间上的skyline查询时,现有的工作均直接从底层关系表中获取这些skyline查询的结果集.显然,当底层关系表的基数较大且skyline查询的个数较多时,现有方法的处理效率极其低下.基于此,提出一种使用预存储的n个skyline集合{PR\\-1,…,PR\\-n}来回答用户提交的m个不同维空间上的skyline查询{SQ\\-1,…,SQ\\-m}的有效方法EAPSQ(efficient algorithm for processing skyline queries).算法充分考虑预存储的skyline集合的编码机制,采用经济学中边际贡献(contribution margin)的概念,使得m个用户提交的skyline查询在n个预存储的skyline集合间的分配达到最佳状态,从而显著提高了处理用户m个skyline查询的效率.实验评估表明,EAPSQ算法具有有效性和实用性.     

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 branch-and-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 processing of top-k dominating queries in distributed environments

Due to the recent massive data generation, preference queries are becoming an increasingly important for users because such queries retrieve only a small number of preferable data objects from a huge multi-dimensional dataset. A top-k dominating query, which retrieves the k data objects dominating the highest number of data objects in a given dataset, is particularly important in supporting multi-criteria decision making because this query can find interesting data objects in an intuitive way exploiting the advantages of top-k and skyline queries. Although efficient algorithms for top-k dominating queries have been studied over centralized databases, there are no studies which deal with top-k dominating queries in distributed environments. The recent data management is becoming increasingly distributed, so it is necessary to support processing of top-k dominating queries in distributed environments. In this paper, we address, for the first time, the challenging problem of processing top-k dominating queries in distributed networks and propose a method for efficient top-k dominating data retrieval, which avoids redundant communication cost and latency. Furthermore, we also propose an approximate version of our proposed method, which further reduces communication cost. Extensive experiments on both synthetic and real data have demonstrated the efficiency and effectiveness of our proposed methods.     

Finding superior skyline points for multidimensional recommendation applications

In a typical Web recommendation system, objects are often described by many attributes. It also needs to serve many users with a diversified range of preferences. In other words, it must be capable to efficiently support high dimensional preference queries that allow the user to explore the data space effectively without imposing specific preference weightings for each dimension. The skyline query, which can produce a set of objects guaranteed to contain all top ranked objects for any linear attribute preference combination, has been proposed to support this type of recommendation applications. However, it suffers from the problem known as ‘dimensionality curse’ as the size of skyline query result set can grow exponentially with the number of dimensions. Therefore, when the dimensionality is high, a large percentage of objects can become skyline points. This problem makes such a recommendation system less usable for users. In this paper, we propose a stronger type of skyline query, called core skyline query, that adopts a new quality measure called vertical dominance to return only an interesting subset of the traditional skyline points. An efficient query processing method is proposed to find core skyline points using a novel indexing structure called Linked Multiple B’-trees (LMB). Our approach can find such superior skyline points progressively without the need of computing the entire set of skyline points first.     

一种并行处理Skyline查询的有效方法

Skyline查询是近年来数据库领域的一个研究重点和热点, 这主要是因为Skyline查询在许多领域有着广泛的应用. 现有的工作大都集中于单处理机环境, 然而, 由于Skyline查询是CPU敏感的, 因此,在实际应用中, 现有的方法具有很大的局限性. 基于此, 提出一种有效降低处理Skyline查询时间开销的并行算法PAPSQ (Parallel algorithm for processing skyline queries). 算法有机结合多维数据对象的自身特性和通用多处理机系统的实施优点, 以Skyline查询搜索偏序格为底层结构, 利用多维数据对象的同胚评估值和偏序格加权技术来有效提高并行处理Skyline查询的效率. 实验评估表明, PAPSQ算法具有有效性和实用性.     

Efficient k-dominant skyline query over incomplete data using MapReduce

Skyline queries are extensively incorporated in various real-life applications by filtering uninteresting data objects. Sometimes, a skyline query may return so many results because it cannot control the retrieval conditions especially for highdimensional datasets. As an extension of skyline query, the kdominant skyline query reduces the control of the dimension by controlling the value of the parameter kto achieve the purpose of reducing the retrieval objects. In addition, with the continuous promotion of Bigdata applications, the data we acquired may not have the entire content that people wanted for some practically reasons of delivery failure, no power of battery, accidental loss, so that the data might be incomplete with missing values in some attributes. Obviously, the k-dominant skyline query algorithms of incomplete data depend on the user definition in some degree and the results cannot be shared. Meanwhile, the existing algorithms are unsuitable for directly used to the incomplete big data. Based on the above situations, this paper mainly studies k-dominant skyline query problem over incomplete dataset and combines this problem with the distributed structure like MapReduce environment. First, we propose an index structure over incomplete data, named incomplete data index based on dominate hierarchical tree (ID-DHT). Applying the bucket strategy, the incomplete data is divided into different buckets according to the dimensions of missing attributes. Second, we also put forward query algorithm for incomplete data in MapReduce environment, named MapReduce incomplete data based on dominant hierarchical tree algorithm (MR-ID-DHTA). The data in the bucket is allocated to the subspace according to the dominant condition by Map function. Reduce function controls the data according to the key value and returns the k-dominant skyline query result. The effective experiments demonstrate the validity and usability of our index structure and the algorithm.     

Skyline查询处理数据立方体代数

维空间的Skyline查询处理技术是近年来数据库技术领域的一个研究重点和热点.目前所有的研究工作都是直接在原始数据表上执行关系查询代数操作来获得最终的结果集,然而,随着原始数据表的数据量和维目标个数的增大,这些研究工作将不再适用.基于此,首次研究Skyline集合上的查询代数操作,使得Skyline查询处理的输入数据来自于小规模的Skyline结果集,而非海量的原始数据表.并且,首次给出一个集成多维对象集合和该对象集合上的Skyline结果集的形式化模型,该模型适合目前Skyline查询计算的应用,并在该模型的实例上研究Skyline集合的查询代数操作.同时,给出查询代数体系的代价评估模型.实验表明,给出的数据模型和查询代数体系具有有效性和实用性.     

SOLE: scalable on-line execution of continuous queries on spatio-temporal data streams

This paper presents the scalable on-line execution (SOLE) algorithm for continuous and on-line evaluation of concurrent continuous spatio-temporal queries over data streams. Incoming spatio-temporal data streams are processed in-memory against a set of outstanding continuous queries. The SOLE algorithm utilizes the scarce memory resource efficiently by keeping track of only the significant objects. In-memory stored objects are expired (i.e., dropped) from memory once they become insignificant. SOLE is a scalable algorithm where all the continuous outstanding queries share the same buffer pool. In addition, SOLE is presented as a spatio-temporal join between two input streams, a stream of spatio-temporal objects and a stream of spatio-temporal queries. To cope with intervals of high arrival rates of objects and/or queries, SOLE utilizes a load-shedding approach where some of the stored objects are dropped from memory. SOLE is implemented as a pipelined query operator that can be combined with traditional query operators in a query execution plan to support a wide variety of continuous queries. Performance experiments based on a real implementation of SOLE inside a prototype of a data stream management system show the scalability and efficiency of SOLE in highly dynamic environments. This work was supported in part by the National Science Foundation under Grants IIS-0093116, IIS-0209120, and 0010044-CCR.     

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 monitoring of skyline queries over distributed data streams

Data management and data mining over distributed data streams have received considerable attention within the database community recently. This paper is the first work to address skyline queries over distributed data streams, where streams derive from multiple horizontally split data sources. Skyline query returns a set of interesting objects which are not dominated by any other objects within the base dataset. Previous work is concentrated on skyline computations over static data or centralized data streams. We present an efficient and an effective algorithm called BOCS to handle this issue under a more challenging environment of distributed streams. BOCS consists of an efficient centralized algorithm GridSky and an associated communication protocol. Based on the strategy of progressive refinement in BOCS, the skyline is incrementally computed by two phases. In the first phase, local skylines on remote sites are maintained by GridSky. At each time, only skyline increments on remote sites are sent to the coordinator. In the second phase, a global skyline is obtained by integrating remote increments with the latest global skyline. A theoretical analysis shows that BOCS is communication-optimal among all algorithms which use a share-nothing strategy. Extensive experiments demonstrate that our proposals are efficient, scalable, and stable.     

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.     

基于TPU-tree的Skyline分支定界查询算法(英文)

提出了一种新的限定性skyline查询理念,并给出了高效的处理技术。分支定界方法是当前skyline查询处理效率较高的技术之一,在一种不确定移动对象的索引策略TPU-tree之上,基于分支定界方法提出了B2CPS可限定性skyline查询处理算法。实验结果表明,提出的基于TPU-tree的B2CPS算法可以很大程度地提高限定性skyline查询的效率,在移动对象频繁更新的情况下亦能保持较高的查询性能,因此具有较好的实用价值。     

Multi-dimensional top-k dominating queries

The top-k dominating query returns k data objects which dominate the highest number of objects in a dataset. This query is an important tool for decision support since it provides data analysts an intuitive way for finding significant objects. In addition, it combines the advantages of top-k and skyline queries without sharing their disadvantages: (i) the output size can be controlled, (ii) no ranking functions need to be specified by users, and (iii) the result is independent of the scales at different dimensions. Despite their importance, top-k dominating queries have not received adequate attention from the research community. This paper is an extensive study on the evaluation of top-k dominating queries. First, we propose a set of algorithms that apply on indexed multi-dimensional data. Second, we investigate query evaluation on data that are not indexed. Finally, we study a relaxed variant of the query which considers dominance in dimensional subspaces. Experiments using synthetic and real datasets demonstrate that our algorithms significantly outperform a previous skyline-based approach. We also illustrate the applicability of this multi-dimensional analysis query by studying the meaningfulness of its results on real data.     

The subspace global skyline query processing over dynamic databases

The global skyline, as an important variant of the skyline, has been widely applied in multi-criteria decision making, business planning and data mining. In this paper, we extend our early work and propose the maintenance methods to process the subspace global skyline (SGS) queries in dynamic databases. In the previous work, we proposed the index structure RB-tree, which can effectively manage the data to accelerate the subspace global skyline calculation. Also, the basic single SGS algorithm based on RB-tree (SSRB) and the optimized single SGS algorithm (OSSRB) were proposed to process a single SGS query. In addition, the multiple SGS algorithm (MSRB) was proposed to calculate multiple SGS queries by sharing the scan spaces of different queries. In this paper, we design some data structures and propose the maintenance approaches of SSRB, OSSRB and MSRB to cope with updates that happen to data sets. Thus our extended algorithms can be adopted for dynamic data sets. Finally, the experimental results show that the proposed algorithms OSSRB and MSRB have good performance to process SGS queries and they can be easily maintained with dynamic datasets.     

Understanding the meaning of a shifted sky: a general framework on extending skyline query

Skyline queries are often used on data sets in multi-dimensional space for many decision-making applications. Traditionally, an object p is said to dominate another object q if, for all dimensions, it is no worse than q and is better on at least one dimension. Therefore, the skyline of a data set consists of all objects not dominated by any other object. To better cater to application requirements such as controlling the size of the skyline or handling data sets that are not well-structured, various works have been proposed to extend the definition of skyline based on variants of the dominance relationship. In view of the proliferation of variants, in this paper, a generalized framework is proposed to guide the extension of skyline query from conventional definition to different variants. Our framework explicitly and carefully examines the various properties that should be preserved in a variant of the dominance relationship so that: (1) maintaining original advantages, while extending adaptivity to application semantics, and (2) keeping computational complexity almost unaffected. We prove that traditional dominance is the only relationship satisfying all desirable properties, and present some new dominance relationships by relaxing some of the properties. These relationships are general enough for us to design new top-k skyline queries that return robust results of a controllable size. We analyze the existing skyline algorithms based on their minimum requirements on dominance properties. We also extend our analysis to data sets with missing values, and present extensive experimental results on the combinations of new dominance relationships and skyline algorithms.