路网数据流的预测聚集查询新方法研究

时空数据流的聚集查询技术已经成为数据库领域的研究热点。到目前为止,还没有一种有效的全时态聚集索引适用于非欧氏空间的路网数据流聚集查询。实现路网数据流的全时态聚集查询,必须解决:(1)路网的非欧氏空间特性问题;(2)路网上移动对象的重复计数、非均匀分布以及预测聚集问题。Sketch RR-tree解决了非欧氏空间特性和重复计数问题;为解决非均匀分布问题,借鉴草图划分思想,提出动态草图索引结构DynSketch:采用AMH智能划分Sketch RR-tree,使每个划分区域内车辆均匀分布,以提高聚集查询质量;同时,基于DynSketch,结合ES预测模型,提出了路网数据流的预测聚集查询算法。     

基于最短路径的道路网络k近邻查询处理

针对基于空间道路网络的k近部查询处理,提出了分布式移动对象更新策略以有效减少服务器计算代价,利用基于内存的空间道路网络部接矩阵、最短路径矩阵结构和移动对象哈希表索引分别对道路网络无向图与移动对象进行存储管理。提出了基于最短路径度量的网络扩展搜索(SPNE)算法,以通过裁剪网络搜索空间来减少k近部查询搜索代价。实验表明,SPNE算法的性能优于传统的NE和MKNN等k近邻查询处理算法。     

Instance optimal query processing in spatial networks

The performance optimization of query processing in spatial networks focuses on minimizing network data accesses and the cost of network distance calculations. This paper proposes algorithms for network k-NN queries, range queries, closest-pair queries and multi-source skyline queries based on a novel processing framework, namely, incremental lower bound constraint. By giving high processing priority to the query associated data points and utilizing the incremental nature of the lower bound, the performance of our algorithms is better optimized in contrast to the corresponding algorithms based on known framework incremental Euclidean restriction and incremental network expansion. More importantly, the proposed algorithms are proven to be instance optimal among classes of algorithms. Through experiments on real road network datasets, the superiority of the proposed algorithms is demonstrated.     

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.     

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.     

The Farthest Spatial Skyline Queries

Pareto-optimal objects are favored as each of such objects has at least one competitive edge against all other objects, or “not dominated”. Recently, in the database literature, skyline queries have gained attention as an effective way to identify such pareto-optimal objects. In particular, this paper studies the pareto-optimal objects in perspective of facility or business locations. More specifically, given data points P and query points Q in two-dimensional space, our goal is to retrieve data points that are farther from at least one query point than all the other data points. Such queries are helpful in identifying spatial locations far away from undesirable locations, e.g., unpleasant facilities or business competitors. To solve this problem, we first study a baseline Algorithm TFSS and propose an efficient progressive Algorithm BBFS, which significantly outperforms TFSS by exploiting spatial locality. We also develop an efficient approximation algorithm to trade accuracy for efficiency. We validate our proposed algorithms using extensive evaluations over synthetic and real datasets.     

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.     

Dynamic monitoring of optimal locations in road network databases

Optimal location (OL) queries are a type of spatial queries that are particularly useful for the strategic planning of resources. Given a set of existing facilities and a set of clients, an OL query asks for a location to build a new facility that optimizes a certain cost metric (defined based on the distances between the clients and the facilities). Several techniques have been proposed to address OL queries, assuming that all clients and facilities reside in an \(L_p\) space. In practice, however, movements between spatial locations are usually confined by the underlying road network, and hence, the actual distance between two locations can differ significantly from their \(L_p\) distance. Motivated by the deficiency of the existing techniques, this paper presents a comprehensive study on OL queries in road networks. We propose a unified framework that addresses three variants of OL queries that find important applications in practice, and we instantiate the framework with several novel query processing algorithms. We further extend our framework to efficiently monitor the OLs when locations for facilities and/or clients have been updated. Our dynamic update methods lead to efficient answering of continuous optimal location queries. We demonstrate the efficiency of our solutions through extensive experiments with large real data.     

Monitoring distributed fragmented skylines

Distributed skyline computation is important for a wide range of domains, from distributed and web-based systems to ISP-network monitoring and distributed databases. The problem is particularly challenging in dynamic distributed settings, where the goal is to efficiently monitor a continuous skyline query over a collection of distributed streams. All existing work relies on the assumption of a single point of reference for object attributes/dimensions: objects may be vertically or horizontally partitioned, but the accurate value of each dimension for each object is always maintained by a single site. This assumption is unrealistic for several distributed applications, where object information is fragmented over a set of distributed streams (each monitored by a different site) and needs to be aggregated (e.g., averaged) across several sites. Furthermore, it is frequently useful to define skyline dimensions through complex functions over the aggregated objects, which raises further challenges for dealing with distribution and object fragmentation. We present the first known distributed algorithms for continuous monitoring of skylines over complex functions of fragmented multi-dimensional objects. Our algorithms rely on decomposition of the skyline monitoring problem to a select set of distributed threshold-crossing queries, which can be monitored locally at each site. We propose several optimizations, including: (a) a technique for adaptively determining the most efficient monitoring strategy for each object, (b) an approximate monitoring technique, and (c) a strategy that reduces communication overhead by grouping together threshold-crossing queries. Furthermore, we discuss how our proposed algorithms can be used to address other continuous query types. A thorough experimental study with synthetic and real-life data sets verifies the effectiveness of our schemes and demonstrates order-of-magnitude improvements in communication costs compared to the only alternative centralized solution.     

Efficient mining of skyline objects in subspaces over data streams

Given a set of k-dimensional objects, the skyline query finds the objects that are not dominated by others. In practice, different users may be interested in different dimensions of the data, and issue queries on any subset of k dimensions in stream environments. This paper focuses on supporting concurrent and unpredictable subspace skyline queries over data streams. Simply to compute and store the skyline objects of every subspace in stream environments will incur expensive update cost. To balance the query cost and update cost, we only maintain the full space skyline in this paper. We first propose an efficient maintenance algorithm and several novel pruning techniques. Then, an efficient and scalable two-phase algorithm is proposed to process the skyline queries in different subspaces based on the full space skyline. Furthermore, we present the theoretical analyses and extensive experiments that demonstrate our method is both efficient and effective.     

Continuous reverse k nearest neighbor monitoring on moving objects in road networks

Continuous reverse k nearest neighbor (CRkNN) monitoring in road networks has recently received increasing attentions. However, there is still a lack of efficient CRkNN algorithms in road networks up to now. In road networks, moving query objects and data objects are restricted by the connectivity of the road network and both the object–query distance and object–object distance updates affect the result of CRkNN queries. In this paper, we present a novel algorithm for continuous and incremental evaluation of CRkNN queries in road networks. Our method is based on a novel data structure called dual layer multiway tree (DLM tree) we proposed to represent the whole monitoring region of a CRkNN query q. We propose several lemmas to reduce the monitoring region of q and the number of candidate objects as much as possible. Moreover, by associating a variable NN_count with each candidate object, we can simplify the monitoring of candidate objects. There are a large number of objects roaming in a road network and many of them are irrelevant to a specific CRkNN query of a query object q. To minimize the processing extension, for a road in the network, we give an IQL list and an IQCL list to specify the set of query objects and data objects whose location updates should be maintained for CRkNN processing of query objects. Our CRkNN method consists of two phase: the initial result generating phase and incremental maintenance phase. In each phase, algorithms with high performance are proposed to make our CRkNN method more efficient. Extensive simulation experiments are conducted and the result shows that our proposed approach is efficient and scalable in processing CRkNN queries in road networks.     

Aggregate nearest neighbor queries in road networks

Aggregate nearest neighbor queries return the object that minimizes an aggregate distance function with respect to a set of query points. Consider, for example, several users at specific locations (query points) that want to find the restaurant (data point), which leads to the minimum sum of distances that they have to travel in order to meet. We study the processing of such queries for the case where the position and accessibility of spatial objects are constrained by spatial (e.g., road) networks. We consider alternative aggregate functions and techniques that utilize Euclidean distance bounds, spatial access methods, and/or network distance materialization structures. Our algorithms are experimentally evaluated with synthetic and real data. The results show that their relative performance depends on the problem characteristics.     

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.     

基于事件的位置不确定移动对象连续概率Skyline查询

Skyline查询是基于位置服务(Location based service, LBS)的一项重要操作,其目的是发现数据集中不被其他点支配的点的集合.移动对象在运动过 程中,其位置信息具有不确定性,导致各数据点间的支配关系不稳定,从而影响Skyline操作.本文针对以位置不确定移动对象为查 询点的Skyline查询进行研究,首先,定义了查询点移动时各对象间支配概率,提出了支配概率和Skyline概率的微元计算方法.在此基 础上,提出一种面向不确定移动对象进行连续概率Skyline查询的有效算法U_CPSC.该算法首先快速计算初始时刻的p-Skyline集合; 然后,定义了两类可能引起p-Skyline变动的事件,通过对这些事件的跟踪计算快速更新p-Skyline集合,无需在移动对象的每一运动 时刻去遍历整个数据集,实现了对p-Skyline的连续更新操作,大大减少了算法的查找和计算开销,提高了运算效率;最后,提出一 种静态算法U_SPSC,与U_CPSC进行了对比试验,实验结果证明了算法的有效性.     

Selecting skyline stars over uncertain databases: Semantics and refining methods in the evidence theory setting

In recent years, a great attention has been paid to skyline computation over uncertain data. In this paper, we study how to conduct advanced skyline analysis over uncertain databases where uncertainty is modeled thanks to the evidence theory (a.k.a., belief functions theory). We particularly tackle an important issue, namely the skyline stars (denoted by SKY²) over the evidential data. This kind of skyline aims at retrieving the best evidential skyline objects (or the stars). Efficient algorithms have been developed to compute the SKY². Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approaches that considerably refine the huge skyline. In addition, the conducted experiments have shown that our algorithms significantly outperform the basic skyline algorithms in terms of CPU and memory costs.     

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.     

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.     

Personalized top-k skyline queries in high-dimensional space

As data of an unprecedented scale are becoming accessible, it becomes more and more important to help each user identify the ideal results of a manageable size. As such a mechanism, skyline queries have recently attracted a lot of attention for its intuitive query formulation. This intuitiveness, however, has a side effect of retrieving too many results, especially for high-dimensional data. This paper is to support personalized skyline queries as identifying “truly interesting” objects based on user-specific preference and retrieval size k. In particular, we abstract personalized skyline ranking as a dynamic search over skyline subspaces guided by user-specific preference. We then develop a novel algorithm navigating on a compressed structure itself, to reduce the storage overhead. Furthermore, we also develop novel techniques to interleave cube construction with navigation for some scenarios without a priori structure. Finally, we extend the proposed techniques for user-specific preferences including equivalence preference. Our extensive evaluation results validate the effectiveness and efficiency of the proposed algorithms on both real-life and synthetic data.     

道路网中的移动对象连续范围查询

研究了采用网络距离的道路网上移动对象连续多范围查询处理技术。设计了道路网、移动对象和查询数据在内存中存储的数据模型。基于该数据模型提出了两种道路网上的移动对象连续多范围查询处理算法。其中,增量式范围查询算法(incremental range query algorithm,IRQA)通过使用扩张树和影响列表结构减少查询的重新计算;组范围查询算法(group range query algorithm,GRQA)利用同一路径上多查询的结果具有相关性这一特点减少查询的重新计算。实验结果表明GRQA算法在查询分布比较集中时性能较优,IRQA算法在查询均匀分布时性能较优,此外,两种算法均优于重新计算所有查询结果的原始算法。     

Ranking continuous nearest neighbors for uncertain trajectories

This article addresses the problem of performing Nearest Neighbor (NN) queries on uncertain trajectories. The answer to an NN query for certain trajectories is time parameterized due to the continuous nature of the motion. As a consequence of uncertainty, there may be several objects that have a non-zero probability of being a nearest neighbor to a given querying object, and the continuous nature further complicates the semantics of the answer. We capture the impact that the uncertainty of the trajectories has on the semantics of the answer to continuous NN queries and we propose a tree structure for representing the answers, along with efficient algorithms to compute them. We also address the issue of performing NN queries when the motion of the objects is restricted to road networks. Finally, we formally define and show how to efficiently execute several variants of continuous NN queries. Our experiments demonstrate that the proposed algorithms yield significant performance improvements when compared with the corresponding naïve approaches.