数据流上连续动态skyline查询研究

skyline查询能够从大规模数据集上计算满足多个标准的最优点.数据流上的skyline计算是数据流上最基本的查询操作之一,对于很多在线应用具有非常重要的意义,尤其在移动计算环境、网络监控、通信网络以及传感器网络等领域.不同于大部分传统的skyline研究,主要研究数据流上约束skvline和动态skyline计算问题.采用网格索引存储元组,提出了GBDS算法用于计算和维护动态skvline.通过为每个查询定义影响区域,使得在元组到达和失效时需要处理的元组个数最小化.理论分析和实验结果证明了提出方法的有效性.     

An Efficient Approach of Processing Multiple Continuous Queries

As stream data is being more frequently collected and analyzed, stream processing systems are faced with more design challenges. One challenge is to perform continuous window aggregation, which involves intensive computation. When there are a large number of aggregation queries, the system may suffer from scalability problems. The queries are usually similar and only differ in window specifications. In this paper, we propose collaborative aggregation which promotes aggregate sharing among the windows so that repeated aggregate operations can be avoided. Different from the previous approaches in which the aggregate sharing is restricted by the window pace, we generalize the aggregation over multiple values as a series of reductions. Therefore, the results generated by each reduction step can be shared. The sharing process is formalized in the feed semantics and we present the compose-and-declare framework to determine the data sharing logic at a very low cost. Experimental results show that our approach offers an order of magnitude performance improvement to the state-of-the-art results and has a small memory footprint.     

A Hybrid Planning Approach for Accompanying Information-gathering in Plan Execution Monitoring

Journal of Intelligent & Robotic Systems - In this work, we present a new mathematic model for the flight of a bird-scale flapping-wing aerial vehicle, in which the impacts of the wing inertia...     

一种连续属性动态分类规则数据挖掘方法

连续属性的处理是当前分类规则中一个热点研究问题。以往的算法往往是建立在离散化过程的基础上进行的,然而,该类方法不但会破坏数据中信息的精度,同时也使得概念的转换十分困难。文章在分析了以往算法的基础上,提出了利用包含度和蕴含度的方法进行连续属性的分类规则学习,并对该种方法的属性约简问题进行了讨论。可以看出,通过该文的研究较好地解决了数据精度和动态概念挖掘的问题,利用包含度和蕴含度的方法是一个十分有价值的研究方向。     

一种混合的动态DDM实现方法

介绍了HLA中数据分发管理DDM的基本内容和过程,分析了目前两种经典的DDM实现方法;在此基础上综合了现有方法的优点,提出了一种混合的动态的DDM实现方法,提高了区域匹配的精度,降低了网络资源的消耗,对DDM方法进行了改进。     

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     

一种通用的可伸缩增量连续查询处理框架

连续查询(continuous queries,CQ)是时空数据库中重要的查询类型.针对基于TPR树索引和R树索引的大量并发连续查询处理,提出了一种可伸缩的增量连续查询处理(scalable processing of incremental continuous queries,SICQ)框架,通过引入搜索区域进行预裁剪以减少查询更新所需要的索引节点访问代价,并引入了增量结果表保存候选对象、批量地更新查询结果集.SICQ框架能够高效处理大量并发的连续查询,具有良好的可伸缩性.基于SICQ框架提出了一种增量更新的SICQ查询处理算法,能够基于上次查询结果增量地更新查询,支持查询集合中加入或删除查询和对象数据集的插入、删除等动态更新操作.实验结果与分析表明,基于SICQ算法的SICQ框架可以很好地支持大量并发的连续查询处理,具有良好的实用价值.     

A Dynamic Discretization Approach for Constructing Decision Trees with a Continuous Label

In traditional decision (classification) tree algorithms, the label is assumed to be a categorical (class) variable. When the label is a continuous variable in the data, two possible approaches based on existing decision tree algorithms can be used to handle the situations. The first uses a data discretization method in the preprocessing stage to convert the continuous label into a class label defined by a finite set of nonoverlapping intervals and then applies a decision tree algorithm. The second simply applies a regression tree algorithm, using the continuous label directly. These approaches have their own drawbacks. We propose an algorithm that dynamically discretizes the continuous label at each node during the tree induction process. Extensive experiments show that the proposed method outperforms the preprocessing approach, the regression tree approach, and several nontree-based algorithms.     

Hybrid Estimation of State and Input for Linear Continuous Time-varying Systems:A Game Theory Approach

1IntroductionWhen estimated signal includes both state and unknown input of the system,estimation problemis referred to as state and input hybrid estimation(in the following,only hybrid estimation will beused for brevity).Hybrid estimation is originated f…     

移动对象的连续最近邻查询算法

介绍了一种索引结构———TPR树和静态环境中基本的最近邻查询算法,并提出了影响时间这一概念,将其运用到最近邻查询算法中,可以完成移动对象的连续最近邻查询。     

Filtering Data Streams for Entity-Based Continuous Queries

The idea of allowing query users to relax their correctness requirements in order to improve performance of a data stream management system (e.g., location-based services and sensor networks) has been recently studied. By exploiting the maximum error (or tolerance) allowed in query answers, algorithms for reducing the use of system resources have been developed. In most of these works, however, query tolerance is expressed as a numerical value, which may be difficult to specify. We observe that in many situations, users may not be concerned with the actual value of an answer, but rather which object satisfies a query (e.g., "who is my nearest neighbor?”). In particular, an entity-based query returns only the names of objects that satisfy the query. For these queries, it is possible to specify a tolerance that is "nonvalue-based.” In this paper, we study fraction-based tolerance, a type of nonvalue-based tolerance, where a user specifies the maximum fractions of a query answer that can be false positives and false negatives. We develop fraction-based tolerance for two major classes of entity-based queries: 1) nonrank-based query (e.g., range queries) and 2) rank-based query (e.g., k-nearest-neighbor queries). These definitions provide users with an alternative to specify the maximum tolerance allowed in their answers. We further investigate how these definitions can be exploited in a distributed stream environment. We design adaptive filter algorithms that allow updates be dropped conditionally at the data stream sources without affecting the overall query correctness. Extensive experimental results show that our protocols reduce the use of network and energy resources significantly.     

Efficient Maintenance of Continuous Queries for Trajectories

We address the problem of optimizing the maintenance of continuous queries in Moving Objects Databases, when a set of pending continuous queries need to be reevaluated as a result of bulk updates to the trajectories of moving objects. Such bulk updates may happen when traffic abnormalities, e.g., accidents or road works, affect a subset of trajectories in the corresponding regions, throughout the duration of these abnormalities. The updates to the trajectories may in turn affect the correctness of the answer sets for the pending continuous queries in much larger geographic areas. We present a comprehensive set of techniques, both static and dynamic, for improving the performance of reevaluating the continuous queries in response to the bulk updates. The static techniques correspond to specifying the values for the various semantic dimensions of trigger execution. The dynamic techniques include an in-memory shared reevaluation algorithm, extending query indexing to queries described by trajectories and query reevaluation ordering based on space-filling curves. We have completely implemented our system prototype on top of an existing Object-Relational Database Management System, Oracle 9i, and conducted extensive experimental evaluations using realistic data sets to demonstrate the validity of our approach.

A Hybrid Bayesian-Frequentist Approach to SLAM

A hybrid Bayesian/ frequentist approach is presented for the Simultaneous Localization and Mapping Problem (SLAM). A frequentist approach is proposed for mapping a dense environment when the robotic pose is known and then extended to the case when the pose is uncertain. The SLAM problem is then solved in two steps: 1) the robot is localized with respect to a sparse set of landmarks in the map using a Bayes filter and a belief on the robot pose is formed, and 2) this belief on the robot pose is used to map the rest of the map using the frequentist estimator. The frequentist part of the hybrid methodology is shown to have complexity linear (constant time complexity under the assumption of bounded noise) in the map components, is robust to the data association problem and is provably consistent. The complexity of the Bayesian part is kept under control owing to the sparseness of the features, which also improves the robustness of the technique to the issue of data association. The hybrid method is tested on standard datasets on the RADISH repository.     

RUSBoost: A Hybrid Approach to Alleviating Class Imbalance

Class imbalance is a problem that is common to many application domains. When examples of one class in a training data set vastly outnumber examples of the other class(es), traditional data mining algorithms tend to create suboptimal classification models. Several techniques have been used to alleviate the problem of class imbalance, including data sampling and boosting. In this paper, we present a new hybrid sampling/boosting algorithm, called RUSBoost, for learning from skewed training data. This algorithm provides a simpler and faster alternative to SMOTEBoost, which is another algorithm that combines boosting and data sampling. This paper evaluates the performances of RUSBoost and SMOTEBoost, as well as their individual components (random undersampling, synthetic minority oversampling technique, and AdaBoost). We conduct experiments using 15 data sets from various application domains, four base learners, and four evaluation metrics. RUSBoost and SMOTEBoost both outperform the other procedures, and RUSBoost performs comparably to (and often better than) SMOTEBoost while being a simpler and faster technique. Given these experimental results, we highly recommend RUSBoost as an attractive alternative for improving the classification performance of learners built using imbalanced data.     

连续系统鲁樟约束方差估计的一种新方法

本文提出一种新的性能鲁棒滤波增益的设计方法，即设计滤波增益，使得当系统噪声强度不确定时，系统每个状态的误差方差稳态值都不大于预先指定值，并举例说明这种设计方法的直接性与简单性．     

一种复合型组播数据认证方法

组播数据的认证是安全组通信中的重点和难点问题之一,但目前没有一种有效方法能够真正适合所有组播应用领域的需求,如散列树的通信开销太大,而散列链不能应对网络传输中的数据丢包.因此基于这两种方法提出了一种复合型组播数据认证方法HAMA(hybrid approach to multicast authentication).通过性能分析与数据对比表明,HAMA的通信开销低于散列树,发生数据丢包时的认证概率高于散列链,且提供不可抵赖认证.所以HAMA是一种安全、有效、实用的组播数据认证方法,尤其适用于远程软件更新等非实时组播应用.     

IPSS: A Hybrid Approach to Planning and Scheduling Integration

Recently, the areas of planning and scheduling in artificial intelligence (AI) have witnessed a big push toward their integration in order to solve complex problems. These problems require both reasoning on which actions are to be performed as well as their precedence constraints (planning) and the reasoning with respect to temporal constraints (e.g., duration, precedence, and deadline); those actions should satisfy the resources they use (scheduling). This paper describes IPSS (integrated planning and scheduling system), a domain independent solver that integrates an AI planner that synthesizes courses of actions with constraint-based techniques that reason based upon time and resources. IPSS is able to manage not only simple precedence constraints, but also more complex temporal requirements (as the Allen primitives) and multicapacity resource usage/consumption. The solver is evaluated against a set of problems characterized by the use of multiple agents (or multiple resources) that have to perform tasks with some temporal restrictions in the order of the tasks or some constraints in the availability of the resources. Experiments show how the integrated reasoning approach improves plan parallelism and gains better makespans than some state-of-the-art planners where multiple agents are represented as additional fluents in the problem operators. It also shows that IPSS is suitable for solving real domains (i.e., workflow problems) because it is able to impose temporal windows on the goals or set a maximum makespan, features that most of the planners do not yet incorporate     

Effcient Location Updates for Continuous Queries over Moving Objects

The significant overhead related to frequent location updates from moving objects often results in poor performance. As most of the location updates do not affect the query results, the network bandwidth and the battery life of moving objects are wasted. Existing solutions propose lazy updates, but such techniques generally avoid only a small fraction of all unnecessary location updates because of their basic approach (e.g., safe regions, time or distance thresholds). Furthermore, most prior work focuses on...     

The Quantum Setting with Randomized Queries for Continuous Problems

The standard setting of quantum computation for continuous problems uses deterministic queries and the only source of randomness for quantum algorithms is through measurement. Without loss of generality we may consider quantum algorithms which use only one measurement. This setting is related to the worst case setting on a classical computer in the sense that the number of qubits needed to solve a continuous problem must be at least equal to the logarithm of the worst case information complexity of this problem. Since the number of qubits must be finite, we cannot solve continuous problems on a quantum computer with infinite worst case information complexity. This can even happen for continuous problems with small randomized complexity on a classical computer. A simple example is integration of bounded continuous functions. To overcome this bad property that limits the power of quantum computation for continuous problems, we study the quantum setting in which randomized queries are allowed. This type of query is used in Shor’s algorithm. The quantum setting with randomized queries is related to the randomized classical setting in the sense that the number of qubits needed to solve a continuous problem must be at least equal to the logarithm of the randomized information complexity of this problem. Hence, there is also a limit to the power of the quantum setting with randomized queries since we cannot solve continuous problems with infinite randomized information complexity. An example is approximation of bounded continuous functions. We study the quantum setting with randomized queries for a number of problems in terms of the query and qubit complexities defined as the minimal number of queries/qubits needed to solve the problem to within ɛ by a quantum algorithm. We prove that for path integration we have an exponential improvement for the qubit complexity over the quantum setting with deterministic queries.     

连续近邻查询方法的研究

连续近邻查询(CNN)要检索一给定查询线段上每一点的近邻。它是时空数据库中一种重要的查询类型,在智能交通系统中有着广泛的应用。Voronoi图解决连续近邻查询问题,思想简单明晰,但Voronoi图构造代价太高,尤其是高阶的Voronoi图。本文从文献得到启示:用分枝限界的思想去界定预创建Voronoi图生成点范围的上限。提出了一种动态地创建局部Voronoi图的办法解决连续近邻查询问题。这种方法只是在给定查询段上所有点的k个近邻范围上限内创建一个局部的k阶Voronoi图,这样会大大降低基于Voronoi图的连续k近邻查询的代价。