面向CPS复杂事件流的不确定性研究

信息物理融合系统CPS获得广泛应用需要解决的一个关键问题是软件中的信息处理部分,而复杂事件处理是CPS中信息处理的核心任务之一。CPS环境下的事件具有异构、分散、海量和不确定性等特征。在CPS实际应用中,因噪声、传感器误差、通讯技术等原因而造成的事件不确定性急需解决。为了解决CPS系统中存在的海量不确定事件流问题,提出一种处理不确定事件流的复杂事件处理方法USCEP,该方法不仅可以实时有效地处理海量不确定事件流,还可以有效计算复杂事件的概率。USCEP对现有RFID复杂事件监测方法 RCEDA进行了改进,提供了历史概率事件查询处理的支持,提出一种事件概率模型进行概率计算,并通过关联查询表来提高效率。实验表明,在处理不确定事件流时,该方法比传统方法具有更好的性能。     

一种改进的分布式数据流查询操作放置算法

在分布式数据流管理系统中,需要将查询操作放置到不同的处理结点执行。因此,如何放置查询操作成为分布式数据流管理研究的核心问题。Peter等人提出一种基于时延空间和弹簧张弛技术的查询操作放置算法,但是该算法假设查询操作之间数据流的流速不变,没有考虑数据流的流速与数据流查询操作之间的相关性。为此,通过分析不同的数据流查询操作与其输出的数据流的流速之间的关系,对Peter等人提出的算法加以改进,实验结果表明,改进后的算法可以有效地应用于分布式数据流管理系统。     

数据流管理技术

最近,人们已经广泛认识到：在某些新的应用领域中,把数据看作瞬时的数据流比看作持久的关系更为适合。本文首先分析了传统数据库管理系统处理数据流的局限性,然后分析了三个典型的数据流管理系统的基本实现技术,讨论了当前数据流管理技术的研究现状和今后的研究方向,最后,给出了一个数据流管理原型系统的体系结构。     

The “Authoring on the Fly” system for automated recording and replay of (tele)presentations

We discuss the problem of capturing media streams which occur during a live lecture in class or during a telepresentation. Instead of presenting yet another method or system for capturing the classroom experience, we introduce some informal guidelines and show their importance for such a system. We derive from these guidelines a formal framework for sets of data streams and an application model to handle these sets so that a real-time replay becomes possible. The Authoring on the Fly system is a possible realization of a framework which follows these guidelines. It allows the capture and real-time replay of data streams captured during a (tele)presentation, including audio, video, and whiteboard action streams. This article gives an overview of the different AoF system components for the various phases of the teaching and learning cycle. It comprises an integrated text and graphics editor for the preparation of pages to be loaded by the whiteboard during the presentation phase. The recording component of the system captures various data streams of the live presentation. They are postprocessed by the system so that they become instances of the class of media for whose replay the general application model was developed. From a global point of view, the Authoring on the Fly system allows one to merge three apparently distinct tasks – teaching in class, telepresentation, and multimedia authoring – into one single activity. The system has been used routinely for recording telepresentations over the MBone net and has already led to a large number of multimedia documents which have been integrated automatically into Web-based teaching and learning environments.     

Ztreamy: A middleware for publishing semantic streams on the Web

In order to make the semantic sensor Web a reality, middleware for efficiently publishing semantically-annotated data streams on the Web is needed. Such middleware should be designed to allow third parties to reuse and mash-up data coming from streams. These third parties should even be able to publish their own value-added streams derived from other streams and static data. In this work we present Ztreamy, a scalable middleware platform for the distribution of semantic data streams through HTTP. The platform provides an API for both publishing and consuming streams, as well as built-in filtering services based on data semantics. A key contribution of our proposal with respect to other related systems in the state of the art is its scalability. Our experiments with Ztreamy show that a single server is able, in some configurations, to publish a real-time stream to up to 40 000 simultaneous clients with delivery delays of just a few seconds, largely outperforming other systems in the state of the art.     

Mining sequential patterns from data streams: a centroid approach

In recent years, emerging applications introduced new constraints for data mining methods. These constraints are typical of a new kind of data: the data streams. In data stream processing, memory usage is restricted, new elements are generated continuously and have to be considered in a linear time, no blocking operator can be performed and the data can be examined only once. At this time, only a few methods has been proposed for mining sequential patterns in data streams. We argue that the main reason is the combinatory phenomenon related to sequential pattern mining. In this paper, we propose an algorithm based on sequences alignment for mining approximate sequential patterns in Web usage data streams. To meet the constraint of one scan, a greedy clustering algorithm associated to an alignment method is proposed. We will show that our proposal is able to extract relevant sequences with very low thresholds.     

A comparative study on automatic audio–visual fusion for aggression detection using meta-information

Multimodal fusion is a complex topic. For surveillance applications audio–visual fusion is very promising given the complementary nature of the two streams. However, drawing the correct conclusion from multi-sensor data is not straightforward. In previous work we have analysed a database with audio–visual recordings of unwanted behavior in trains (Lefter et al., 2012) and focused on a limited subset of the recorded data. We have collected multi- and unimodal assessments by humans, who have given aggression scores on a 3 point scale. We showed that there are no trivial fusion algorithms to predict the multimodal labels from the unimodal labels since part of the information is lost when using the unimodal streams. We proposed an intermediate step to discover the structure in the fusion process. This step is based upon meta-features and we find a set of five which have an impact on the fusion process. In this paper we extend the findings in (Lefter et al., 2012) for the general case using the entire database. We prove that the meta-features have a positive effect on the fusion process in terms of labels. We then compare three fusion methods that encapsulate the meta-features. They are based on automatic prediction of the intermediate level variables and multimodal aggression from state of the art low level acoustic, linguistic and visual features. The first fusion method is based on applying multiple classifiers to predict intermediate level features from the low level features, and to predict the multimodal label from the intermediate variables. The other two approaches are based on probabilistic graphical models, one using (Dynamic) Bayesian Networks and the other one using Conditional Random Fields. We learn that each approach has its strengths and weaknesses in predicting specific aggression classes and using the meta-features yields significant improvements in all cases.     

A Formal Composition and Interaction Model for a Web Component Platform

A framework for components on the Web needs a formal model that captures essential concepts such as contractual information and service matching. We propose a typed π-calculus-based model for Web components that formalises an extension of the currently discussed Web Services framework. We address in particular activities in the stages of a component life cycle – such as matching, commitment, connection and interaction – that are part of the process that a component is involved in.     

Web应用程序中用流技术动态生成Excel报表

Web报表的动态生成是个复杂的问题。在基于ASP．NET的Web应用程序中,利用流技术和ADO．NET数据访问技术,根据用户选择的字段和查询条件,就可以从数据库中读取符合要求的数据,并且能快速地产生Excel报表输出,这种设计方法克服了B／S模式报表设计的诸多局限性,是一种切实可行的Web报表解决方案。     

Enabling inter-PMIPv6-domain handover with traffic distributors

As a network-based localized mobility management protocol, Proxy Mobile IPv6 (PMIPv6) Gundavelli et al. (2008) enables mobile node (MN) to move in a local domain without any involvement in the protocol signaling. In contrast to other mobility protocols (such as cellular IP (CIP) Valkó, 1999, and hierarchical mobile IP (HMIP) Soliman et al., 2005), PMIPv6 does not require any upgrade of MN's protocol stack. Instead, PMIPv6 employs network entities to handle the handover for MN. However, the PMIPv6 can only manage MN's reachability within a local domain. If MN moves beyond the border of PMIPv6 domain, the mobility support will be broken. To provide MN continuous support across domains, we propose a solution to interconnect neighboring PMIPv6 domains. In our proposal, we have introduced a new network entity called traffic distributor (TD). The TD is used to deliver the cross-domain traffic. If MN moves across domain borders, LMA will notify the TD and the TD will redirect MN's traffic to the new domain. To evaluate our proposal, we conduct experiments to compare it with Neumann et al., 2009a, Neumann et al., 2009b proposal which is another proposal to handle inter-PMIPv6-domain issues. Results show that our proposal is a feasible alternative for inter-domain handover, and it outperforms Neumann's proposal in terms of binding cache entry number, transmission delay and handover delay.     

Engineering Rich Internet Application User Interfaces over Legacy Web Models

A steadily growing trend in Web applications is the development of user interfaces through rich Internet applications. Among other capabilities, RIAs offer high interactivity and native multimedia support, giving them a major advantage over standard HTML To update existing HTML Web applications, the authors propose the RUX-Model, which facilitates the user interface adaptation of existing Web 1.0 applications to Web 2.0. Their proposal focuses on new RIA capacities that exploit the data and business logic already provided in legacy Web models.     

NCSA's World Wide Web server: design and performance

To support continued growth, WWW servers must manage a multigigabyte (in some instances a multiterabyte) database of multimedia information while concurrently serving multiple request streams. This places demands on the servers' underlying operating systems and file systems that lie far outside today's normal operating regime. Simply put, WWW servers must become more adaptive and intelligent. The first step on this path is understanding extant access patterns and responses. The article examines extant Web access patterns with the aim of developing more efficient file-caching and prefetching strategies     

Capturing data quality requirements for web applications by means of DQ_WebRE

The number of Web applications which are part of Business Intelligence (BI) applications has grown exponentially in recent years, as has their complexity. Consequently, the amount of data used by these applications has also increased. The larger the number of data used, the greater the chance to make errors is. That being the case, managing data with an acceptable level of quality is paramount to success in any organizational business process. In order to raise and maintain adequate levels of Data Quality (DQ), it is indispensable for Web applications to be able to satisfy specific DQ requirements. To do so, DQ requirements should be captured and introduced into the development process of the Web Application, together with the other software requirements needed in the applications. In the field of Web application development, however, there appears to us to exist a lack of proposals aimed at managing specific DQ software requirements. This paper considers the MDA (Model Driven Architecture) approach and, principally, the benefits provided by Model Driven Web Engineering (MDWE), putting forward a proposal for two artifacts. These consist of a metamodel and a UML profile for the management of Data Quality Software Requirements for Web Applications (DQ_WebRE).     

基于Web2.0的情景应用程序的构建研究

在企业软件应用中,开发生命周期短、面向业务的情景应用程序需求大量增加。随着新一代互联网Web 2.0的普及,推动了基于Web 2.0的情景应用程序的发展。本文提出基于Web 2.0的情景应用程序体系结构,并阐述如何创建一个实例。     

Learning from data streams with only positive and unlabeled data

Many studies on streaming data classification have been based on a paradigm in which a fully labeled stream is available for learning purposes. However, it is often too labor-intensive and time-consuming to manually label a data stream for training. This difficulty may cause conventional supervised learning approaches to be infeasible in many real world applications, such as credit fraud detection, intrusion detection, and rare event prediction. In previous work, Li et al. suggested that these applications be treated as Positive and Unlabeled learning problem, and proposed a learning algorithm, OcVFD, as a solution (Li et al. 2009). Their method requires only a set of positive examples and a set of unlabeled examples which is easily obtainable in a streaming environment, making it widely applicable to real-life applications. Here, we enhance Li et al.’s solution by adding three features: an efficient method to estimate the percentage of positive examples in the training stream, the ability to handle numeric attributes, and the use of more appropriate classification methods at tree leaves. Experimental results on synthetic and real-life datasets show that our enhanced solution (called PUVFDT) has very good classification performance and a strong ability to learn from data streams with only positive and unlabeled examples. Furthermore, our enhanced solution reduces the learning time of OcVFDT by about an order of magnitude. Even with 80 % of the examples in the training data stream unlabeled, PUVFDT can still achieve a competitive classification performance compared with that of VFDTcNB (Gama et al. 2003), a supervised learning algorithm.     

RIoTBench: An IoT benchmark for distributed stream processing systems

The Internet of Things (IoT) is an emerging technology paradigm where millions of sensors and actuators help monitor and manage physical, environmental, and human systems in real time. The inherent closed‐loop responsiveness and decision making of IoT applications make them ideal candidates for using low latency and scalable stream processing platforms. Distributed stream processing systems (DSPS) hosted in cloud data centers are becoming the vital engine for real‐time data processing and analytics in any IoT software architecture. But the efficacy and performance of contemporary DSPS have not been rigorously studied for IoT applications and data streams. Here, we propose RIoTBench , a real‐time IoT benchmark suite, along with performance metrics, to evaluate DSPS for streaming IoT applications. The benchmark includes 27 common IoT tasks classified across various functional categories and implemented as modular microbenchmarks. Further, we define four IoT application benchmarks composed from these tasks based on common patterns of data preprocessing, statistical summarization, and predictive analytics that are intrinsic to the closed‐loop IoT decision‐making life cycle. These are coupled with four stream workloads sourced from real IoT observations on smart cities and smart health, with peak streams rates that range from 500 to 10 000 messages/second from up to 3 million sensors. We validate the RIoTBench suite for the popular Apache Storm DSPS on the Microsoft Azure public cloud and present empirical observations. This suite can be used by DSPS researchers for performance analysis and resource scheduling, by IoT practitioners to evaluate DSPS platforms, and even reused within IoT solutions.     

一种基于数据流模式表示的半懒惰式分类算法

依据从大规模数据中抽取的模式来建立分类模型是模式挖掘的重要研究问题之一。一种可行的方法是根据模式集合建立贝叶斯分类模型。然而,目前基于模式的贝叶斯分类模型大多是针对静态数据集合的,通常不能适应于高速动态变化与无限的数据流环境。对此,提出一种数据流环境下基于模式发现的贝叶斯分类学习模型,其采用半懒惰式学习策略,针对分类实例在不断更新的频繁项集合上建立局部的分类模型;为加快流数据处理的速度,提出了结构更为简单的混合树结构,同时提出了给定项限制的模式抽取机制以减少候选项集的生成;对数据流中模式抽取不完全的情况,使用平滑技术处理未被抽取的项。大量实验分析证明,相较于其他数据流分类器,所提模型具有更高的分类正确率。     

Active Data: A programming model to manage data life cycle across heterogeneous systems and infrastructures

The Big Data challenge consists in managing, storing, analyzing and visualizing these huge and ever growing data sets to extract sense and knowledge. As the volume of data grows exponentially, the management of these data becomes more complex in proportion. A key point is to handle the complexity of the data life cycle, i.e. the various operations performed on data: transfer, archiving, replication, deletion, etc. Indeed, data-intensive applications span over a large variety of devices and e-infrastructures which implies that many systems are involved in data management and processing. We propose Active Data, a programming model to automate and improve the expressiveness of data management applications. We first define the concept of data life cycle and introduce a formal model that allows to expose data life cycle across heterogeneous systems and infrastructures. The Active Data programming model allows code execution at each stage of the data life cycle: routines provided by programmers are executed when a set of events (creation, replication, transfer, deletion) happen to any data. We implement and evaluate the model with four use cases: a storage cache to Amazon-S3, a cooperative sensor network, an incremental implementation of the MapReduce programming model and automated data provenance tracking across heterogeneous systems. Altogether, these scenarios illustrate the adequateness of the model to program applications that manage distributed and dynamic data sets. We also show that applications that do not leverage on data life cycle can still benefit from Active Data to improve their performances.     

Improving SCTP retransmission delays for time-dependent thin streams

A large number of network services rely on IP and reliable transport protocols. For applications that provide abundant data for transmission, loss is usually handled satisfactorily, even if the application is latency-sensitive (Wang et al. 2004). For data streams where small packets are sent intermittently, however, applications can occasionally experience extreme latencies (Griwodz and Halvorsen 2006). As it is not uncommon that such thin-stream applications are time-dependent, any unnecessarily induced delay can have severe consequences for the service provided. Massively Multiplayer Online Games (MMOGs) are a defining example of thin streams. Many MMOGs (like World of Warcraft and Age of Conan) use TCP for the benefits of reliability, in-order delivery and NAT/firewall traversal. It has been shown that TCP has several shortcomings with respect to the latency requirements of thin streams because of the way it handles retransmissions (Griwodz and Halvorsen 2006). As such, an alternative to TCP may be SCTP (Stewart et al. 2000), which was originally developed to meet the requirements of signaling transport. In this paper, we evaluate the Linux-kernel SCTP implementation in the context of thin streams. To address the identified latency challenges, we propose sender-side only enhancements that reduce the application-layer latency in a manner that is compatible with unmodified receivers. These enhancements can be switched on by applications and are used only when the system identifies the stream as thin. To evaluate the latency performance, we have performed several tests over various real networks and over an emulated network, varying parameters like RTT, packet loss and amount of competing cross traffic. When comparing our modifications with SCTP on Linux and FreeBSD and TCP New Reno, our results show great latency improvements and indicate the need for a separate handling of thin and thick streams.