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.     

Processing skyline queries in incomplete distributed databases

Due to its great benefits over many database applications, skyline queries have received formidable concern in the last decades. Skyline queries attempt to assist users by identifying the set of data items which represents the best results that meet the conditions of a given query. Most of the existing skyline techniques concentrate on identifying skylines over a single relation. However, in distributed databases, the process of skyline queries required accessing multiple relations which might be located at different sites. Consequently, data items from these multiple relations need to be joined and thus transferring these data items from one site to another is unavoidable. Moreover, the previous techniques also assume that the values of dimensions for every data item are presented (complete) which is not always true as some values may be missing. In this paper, we proposed an approach for processing skyline queries in incomplete distributed databases. The approach derives skylines from multiple relations where dominated data items are removed before joining the relations to reduce the processing time and the network cost. The experimental results illustrate that our proposed approach outperforms the previous approaches in terms of processing time and network cost.     

时空数据库中移动对象查询的选择性估算

目前,用来估算查询选择性的最有效的技术是直方图技术。通过特定的改进,直方图也可应用到时空查询优化中。TPR 树在对未来预测的时空索引中显示了很好的优越性。结合TPR 树索引结构,构建满足时空查询的直方图,估算时空查询的选择性。     

An efficient query optimization strategy for spatio-temporal queries in video databases

The interest for multimedia database management systems has grown rapidly due to the need for the storage of huge volumes of multimedia data in computer systems. An important building block of a multimedia database system is the query processor, and a query optimizer embedded to the query processor is needed to answer user queries efficiently. Query optimization problem has been widely studied for conventional database systems; however it is a new research area for multimedia database systems. Due to the differences in query processing strategies, query optimization techniques used in multimedia database systems are different from those used in traditional databases. In this paper, a query optimization strategy is proposed for processing spatio-temporal queries in video database systems. The proposed strategy includes reordering algorithms to be applied on query execution tree. The performance results obtained by testing the reordering algorithms on different query sets are also presented.     

Efficient skyline query processing in SpatialHadoop

This paper studies the problem of computing the skyline of a vast-sized spatial dataset in SpatialHadoop, an extension of Hadoop that supports spatial operations efficiently. The problem is particularly interesting due to advent of Big Spatial Data that are generated by modern applications run on mobile devices, and also because of the importance of the skyline operator for decision-making and supporting business intelligence. To this end, we present a scalable and efficient framework for skyline query processing that operates on top of SpatialHadoop, and can be parameterized by individual techniques related to filtering of candidate points as well as merging of local skyline sets. Then, we introduce two novel algorithms that follow the pattern of the framework and boost the performance of skyline query processing. Our algorithms employ specific optimizations based on effective filtering and efficient merging, the combination of which is responsible for improved efficiency. We compare our solution against the state-of-the-art skyline algorithm in SpatialHadoop. The results show that our techniques are more efficient and outperform the competitor significantly, especially in the case of large skyline output size.     

Voronoi-based range and continuous range query processing in mobile databases

With the wide availability of mobile devices (smart phones, iPhones, etc.), mobile location-based queries are increasingly in demand. One of the most frequent queries is range search which returns objects of interest within a pre-defined area. Most of the existing methods are based on the road network expansion method – expanding all nodes (intersections and objects) and computing the distance of each node to the query point. Since road networks are extremely complex, node expansion approaches are inefficient. In this paper, we propose a method, Voronoi Range Search (VRS) based on the Voronoi diagram, to process range search queries efficiently and accurately by partitioning the road networks to some special polygons. Then we further propose Voronoi Continuous Range (VCR) to satisfy the requirement for continuous range search queries (moving queries) based on VRS. Our empirical experiments show that VRS and VCR surpass all their rivals for both static and moving queries.     

A continuous reverse skyline query processing scheme for multimedia data sharing in mobile environments

Multimedia Tools and Applications - Recently, various query processing schemes in mobile environments have been studied. Particularly, a reverse skyline query that is the variation of a skyline...     

Trajectory databases: Data models,uncertainty and complete query languages

Moving objects produce trajectories. We describe a data model for trajectories and trajectory samples and an efficient way of modeling uncertainty via beads for trajectory samples. We study transformations of the ambient space for which important physical properties of trajectories, such as speed, are invariant. We also determine which transformations preserve beads. We give conceptually easy first-order complete query languages and computationally complete query languages for trajectory databases, which allow to talk directly about speed and uncertainty in terms of beads. The queries expressible in these languages are invariant under speed- and bead-preserving transformations.     

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.     

Fast query processing in deductive databases

A special-purpose algorithm, that analyzes the structure of a recursion and exploits its properties in query processing in a deductive database is presented. This method is applied to linear rules, a large and common class of recursion. The structural approach to rule processing (SARP) prototype system that implements the algorithm is described     

Skyline-join query processing in distributed databases

The skyline-join operator, as an important variant of skylines, plays an important role in multi-criteria decision making problems. However, as the data scale increases, previous methods of skyline-join queries cannot be applied to new applications. Therefore, in this paper, it is the first attempt to propose a scalable method to process skyline-join queries in distributed databases. First, a tailored distributed framework is presented to facilitate the computation of skyline-join queries. Second, the distributed skyline-join query algorithm (DSJQ) is designed to process skyline-join queries. DSJQ contains two phases. In the first phase, two filtering strategies are used to filter out unpromising tuples from the original tables. The remaining tuples are transmitted to the corresponding data nodes according a partition function, which can guarantee that the tuples with the same join value are transferred to the same node. In the second phase, we design a scheduling plan based on rotations to calculate the final skyline-join result. The scheduling plan can ensure that calculations are equally assigned to all the data nodes, and the calculations on each data node can be processed in parallel without creating a bottleneck node. Finally, the effectiveness of DSJQ is evaluated through a series of experiments.     

Constraint-based query evaluation in deductive databases

Constraints play an important role in the efficient query evaluation in deductive databases. Constraint-based query evaluation in deductive databases is investigated, with emphasis on linear recursions with function symbols. Constraints are grouped into three classes: rule constraints, integrity constraints, and query constraints. Techniques are developed for the maximal use of different kinds of constraints in rule compilation and query evaluation. The study on the roles of different classes of constraints in set-oriented evaluation of linear recursions shows the following: rule constraints should be integrated with their corresponding deduction rules in the compilation of recursions; integrity constraints, including finiteness constraints and monotonicity constraints, should be used in the analysis of finite evaluability and termination for specific queries; and query constraints, which are often useful in search space reduction and termination, should be transformed, when necessary, and should be pushed into the compiled chains as deeply as possible for efficient evaluation. The constraint-based query-processing technique integrates query-independent compilation and chain-based query evaluation methods and demonstrates its great promise in deductive query evaluation     

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.     

一种分布式环境中的二分式多层网格skyline算法

skyline计算在数据挖掘、多标准决策和数据库可视化等领域有着非常重要的作用,这些年已经得到了广泛的关注,以往对于skyline查询的研究大多集中在处理集中的数据集上,即集中式skyline查询,已经得到了很多的研究成果。然而,实际情况是：相关数据几乎分散在几个不同的服务器上,因此在分布式环境中的skyline查询计算需要从各个服务器收集大量的数据;现有的在分布式环境中的skyline查询方法有两个主要问题：一是skyline查询的处理时间较慢;二是在网络中服务器之间传输了很多不必要的重叠数据。提出了一种二分式多层网格法（DMLG）,可以有效地处理在分布式环境中的skyline查询。该方法利用网格的方法,借鉴二分法,最大限度地减少了不必要的重叠数据传输,基于不同的数据集的实验表明,这种方法优于现有的方法。     

脏数据库的查询方法研究

对于给定的约束,多个数据源分别是一致的,但是在它们集成时可能是脏的.已经存在的技术能够通过特别的方法识别出数据集成环境下的脏数据,但是不能进行有效处理.分析查询对应的连接图是否为有向连接图,判断查询是否可重写,并且给出了元组概率计算和基本查询重写方法.使用TPC-H基准的数据和查询比较脏数据多粒度的执行性能,实验显示方法是可行的.     

加密数据库快速关键词查询技术

为保证敏感信息的数据安全,用户通常会将其加密后存储到云端数据库,这为数据库管理及后续使用增加了难度。提出一种安全查询方案,在不暴露敏感信息的情况下可获得符合查询条件的结果集。使用伪随机函数和Bloom过滤器,对敏感信息的关键词集合进行预处理,在数据库中生成相应的索引数据结构,支持不固定数量的关键词查询与高效的数据更新。查询时,客户端计算出关键词相应的陷门并将其发送给服务器,服务器使用陷门执行查询,将多关键词计算出的陷门进行串接,可将多关键词查询问题转换成单关键词查询问题,并且不提高时间复杂度。此外,有效的陷门只能由拥有密钥的用户产生,陷门不会泄露任何敏感信息,故该方案不依赖完全可信的数据库服务提供商。与现有的采用特殊双层结构的加密方式相比,提高了查询效率,解决了加密数据库处理用户查询请求时的敏感信息泄露问题,且允许用户对敏感信息采用不同的加密方式,具有很强的兼容性。使用TPC-H的数据库测试方案和测试数据进行实验,实验结果证明了算法具有较高的执行效率。     

Efficient processing of probabilistic group subspace skyline queries in uncertain databases

Due to the pervasive data uncertainty in many real applications, efficient and effective query answering on uncertain data has recently gained much attention from the database community. In this paper, we propose a novel and important query in the context of uncertain databases, namely probabilistic group subspace skyline (PGSS) query, which is useful in applications like sensor data analysis. Specifically, a PGSS query retrieves those uncertain objects that are, with high confidence, not dynamically dominated by other objects, with respect to a group of query points in ad-hoc subspaces. In order to enable fast PGSS query answering, we propose effective pruning methods to reduce the PGSS search space, which are seamlessly integrated into an efficient PGSS query procedure. Furthermore, to achieve low query cost, we provide a cost model, in light of which uncertain data are pre-processed and indexed. Extensive experiments have been conducted to demonstrate the efficiency and effectiveness of our proposed approaches.     

Object-based directional query processing in spatial databases

Direction-based spatial relationships are critical in many domains, including geographic information systems (GIS) and image interpretation. They are also frequently used as selection conditions in spatial queries. In this paper, we explore the processing of object-based direction queries and propose a new open shape-based strategy (OSS). OSS models the direction region as an open shape and converts the processing of the direction predicates into the processing of topological operations between open shapes and closed geometry objects. The proposed strategy OSS makes it unnecessary to know the boundary of the embedding world and also eliminates the computation related to the world boundary. OSS reduces both I/O and CPU costs by greatly improving the filtering effectiveness. Our experimental evaluation shows that OSS consistently outperforms classical range query strategies (RQS) while the degree of performance improvement varies by several parameters. Experimental results also demonstrate that OSS is more scalable than RQS for large data sets.     

Indexing range sum queries in spatio-temporal databases

Although spatio-temporal databases have received considerable attention recently, there has been little work on processing range sum queries on the historical records of moving objects despite their importance. Since the direct access to a huge amount of data to answer range sum queries incurs prohibitive computation cost, materialization techniques based on existing index structures are suggested. A simple but effective solution is to apply the materialization technique to the MVR-tree known as the most efficient structure for window queries with spatio-temporal conditions. Aggregate structures based on other index structures such as the HR-tree and the 3DR-tree do not provide satisfactory query performance. In this paper, we propose a new index structure called the Adaptively Partitioned Aggregate R-Tree (APART) and query processing algorithms to efficiently process range sum queries in many situations. Our experimental results show that the performance of the APART is typically 1.3 times better than that of its competitor for a wide range of scenarios.