Guest Editors' Introduction: Cross-Reality Environments

In this article, we define cross-reality as the union between ubiquitous sensor/actuator networks and shared online virtual worlds-a place where collective human perception meets the machines' view of pervasive computing. We describe how five of the articles in this issue expand on aspects of this theme.     

移动环境下支持实时事务处理的数据预取

随着移动通信技术的迅速发展,人们提出了新的应用要求:在移动环境下处理实时事务.而移动通信带宽有限性引起较大的数据访问延迟,有时甚至由于网络传输的断接使得事务得不到所需要的数据,数据预取能够很好地解决这个问题.已有的移动环境下数据预取没有考虑到数据的流行性和事务的时间特性.该文分析影响实时事务数据预取的因素,首先考虑数据易变性、活跃性等因素,获得高价值预取数据集合;然后考虑访问预取数据的事务优先级、数据流行性等因素,构造预取数据的选择函数,通过该函数在前面选取的集合中筛选出对满足实时事务截止期更有价值的数据对象进行预取.实验表明,该数据预取策略能降低移动实时事务满足截止期的比率,更好地支持移动实时事务处理.     

Building and Employing Cross-Reality

Considers present and future practical applications of cross-reality. From tools to build new 3D virtual worlds to the products of those tools, cross-reality is becoming a staple of our everyday reality. Practical applications of cross-reality include the ability to virtually visit a factory to manage and maintain resources from the comfort of your laptop or desktop PC as well as sentient visors that augment reality with additional information so that users can make more informed choices. Tools and projects considered are:Project Wonderland for multiuser mixed reality;ClearWorlds: mixed- reality presence through virtual clearboards; VICI (Visualization of Immersive and Contextual Information) for ubiquitous augmented reality based on a tangible user interface; Mirror World Chocolate Factory; and sentient visors for browsing the world.     

Component Based Simulation Environments of Distributed Discrete Event Simulation

Component based design is a software design method developing from object-oriented design. It uses hierarchical, rnodular ideas to analyze and design systerns. It improves the reusability of software, decreases the cost of system developments. This paper applies the ideas of component based software design and distributed discrete event simulation （DDES）, and puts forward simulation component model standards and distributed simulation method, and develops the simulation environment. This method allows the users to reuse existing models and to build simulation by assembling basic models. It is fit for modeling and simulating large and complex systems in especial domain such as communication network, and supports the reuse of models effectively. The simulation environment has good flexibility and expansibility.     

Foundations for Streaming Model Transformations by Complex Event Processing

Streaming model transformations represent a novel class of transformations to manipulate models whose elements are continuously produced or modified in high volume and with rapid rate of change. Executing streaming transformations requires efficient techniques to recognize activated transformation rules over a live model and a potentially infinite stream of events. In this paper, we propose foundations of streaming model transformations by innovatively integrating incremental model query, complex event processing (CEP) and reactive (event-driven) transformation techniques. Complex event processing allows to identify relevant patterns and sequences of events over an event stream. Our approach enables event streams to include model change events which are automatically and continuously populated by incremental model queries. Furthermore, a reactive rule engine carries out transformations on identified complex event patterns. We provide an integrated domain-specific language with precise semantics for capturing complex event patterns and streaming transformations together with an execution engine, all of which is now part of the Viatra reactive transformation framework. We demonstrate the feasibility of our approach with two case studies: one in an advanced model engineering workflow; and one in the context of on-the-fly gesture recognition.     

Complex Event Processing (CEP)

Ereignisgesteuerte Informationssysteme ben?tigen eine systematische und automatische Verarbeitung von Ereignissen: Complex Event Processing (CEP).     

Complex Event Processing (CEP)

Zusammenfassung  Ereignisgesteuerte Informationssysteme ben?tigen eine systematische und automatische Verarbeitung von Ereignissen: Complex Event Processing (CEP).     

Out-of-Order Event Processing in Kinetic Data Structures

We study the problem of designing kinetic data structures (KDS’s for short) when event times cannot be computed exactly and events may be processed in a wrong order. In traditional KDS’s this can lead to major inconsistencies from which the KDS cannot recover. We present more robust KDS’s for the maintenance of several fundamental structures such as kinetic sorting and kinetic tournament trees, which overcome the difficulty by employing a refined event scheduling and processing technique. We prove that the new event scheduling mechanism leads to a KDS that is correct except for finitely many short time intervals. We analyze the maximum delay of events and the maximum error in the structure, and we experimentally compare our approach to the standard event scheduling mechanism.     

面向车联网的时空事件处理语言STEP

车联网是物联网技术应用于智能交通领域所形成的重要研究领域。复杂事件处理技术是车联网系统数据流处理的重要方法。有别于经典的物联网系统,车联网中数据流包含大量的时间和空间信息。在复杂事件处理技术中,如何有效地表达和处理车联网的时空数据流成为亟待解决的问题。针对该问题,提出了一种时空事件处理语言(spatial-temporal event processing language,STEP)。STEP分别采用时间段和栅格地图作为时间和空间模型。基于该时空模型,首先给出STEP语言的相关时空算子和完整语法,从而有效地表达车联网中时空事件流的时空信息。然后,分别从形式语义学角度引入STEP语言的操作语义,并且从实现角度给出了基于Petri网模型的时空事件流处理算法,从而建立车联网时空事件流处理机制。最后,通过实验说明了基于STEP语言的车联网时空事件流处理机制的有效性。     

PMTree:一种高效的事件流模式匹配方法

复杂事件处理技术从多个持续事件流中分析并提取满足特定模式的事件序列.高吞吐率场景下,如何快速准确地识别事件序列是复杂事件处理技术中一个非常重要的问题.现在事件流的模式匹配方法——NFA、Petri网、有向图等——存在语义描述能力不足、部分算子实现代价高等缺陷.针对这一现状,设计并实现了一种基于树的模式匹配方法——PMTree.PMTree定义了事件模型及相应事件算子,将事件序列映射为树节点,同时将时间窗口约束及谓词约束等放置在相应节点,这些树节点连接成一棵PMTree来支持实时的事件筛选与过滤.进一步研究了PMTree构建过程中的优化策略,并提出了开销模型以及优化构建算法,以尽可能减少模式匹配开销.实验结果表明,相同测试条件下基于PMTree实现的复杂事件处理引擎Cesar吞吐率是基于NFA实现的开源引擎Esper的3～6倍,并且在不同事件量或事件序列复杂度下性能表现稳定.     

复杂事件处理在RFID中间件中的应用

KFID中间件作为RFID技术的神经中枢.它的事件与数据处理能力直接关系到KFID应用的推广.复杂事件处理是一种事件与数据处理的新技术,通过介绍它的工作原理,说明在RFID中间件中引入复杂事件处理技术的依据,并给出基于复杂事件处理技术的RFID中间件的模块化设计方案.     

自然语言处理系统中的事件类名词的耦合处理

介绍了自然语言处理系统中事件类名词耦合的一种处理方法.事件类名词是汉语名词的一个特殊子类,这类名词兼事物性与动作性于一身.给出了事件类名词的语义认知基础,并采用基于知识的方法,应用概念从属理论,对事件类名词进行详细的语义分析.在此基础上,对事件类名词的耦合情况进行分类,并给出了事件名词解耦处理的具体方法.这种对事件类名词的耦合处理方法在智能辅导领域中得到了较好的应用.     

自然语言处理系统中的事件类名词的耦合处理

介绍了自然语言处理系统中事件类名词耦合的一种处理方法。事件类名词是汉语名词的一个特殊子类,这类名词兼事物性与动作性于一身。给出了事件类名词的语义认知基础,并采用基于知识的方法,应用概念从属理论,对事件类名词进行详细的语义分析。在此基础上,对事件类名词的耦合情况进行分类,并给出了事件名词解耦处理的具体方法。这种对事件类名词的耦合处理方法在智能辅导领域中得到了较好的应用。     

REvent:一种面向RFID应用的复杂事件处理系统

无线射频识别技术RFID有着重要的应用背景.设计并实现了一个面向无线射频识别应用的复杂事件处理系统REvent.构建了一个仿真环境并在此基础上测试Revent系统的数据预处理与事件检测等功能.对系统框架、关键技术和主要演示过程进行了说明.     

Ceper:一个高性能复杂事件处理引擎

复杂事件处理技术从连续的输入事件流中分析并提取出满足特定模式的事件序列。它将传统先存储静态数据再进行处理和挖掘的数据处理方法,转变为实时处理动态事件流,更加符合真实世界的数据处理模式,能够高效及时地从数据流中过滤出相对少量的具有应用价值的数据。文章设计并实现了一个高性能复杂事件处理引擎——Ceper,提出了Ceper的事件处理模型,展示了它的系统结构和模式匹配树的方法 PMT,介绍了Ceper的实现的关键技术,并进行了一定的测试。     

在线-离线数据流上复杂事件检测

随着数据采集和处理技术的发展,在物联网对象跟踪、网络监控、金融预测、电信消费模式等领域中进行事件检测显得越发重要.事件检测在一次扫描数据流的假设下完成,数据流在被处理完后丢弃.事实上,很多应用场景中,历史数据流因含有丰富的信息而不能简单丢弃,且一些事件检测查询需要同时在实时和历史数据流上进行.鉴于已有复杂事件检测很少考虑同时在实时-历史数据流上进行模式匹配,作者研究了在线-离线数据流上复杂事件检测的关键问题.主要工作如下:(1)针对滑动窗口内产生的大量模式匹配中间结果,提出利用时态关系和时空关系管理中间结果的方法 TPM和STPM.STPM以中间结果的时态和状态信息为权值对中间结果进行管理,将最近的、最有可能更新状态的中间结果置于内存,极大地减少了中间结果的读取操作代价.(2)给出了基于选择度的在线-离线复杂事件检测优化算法;(3)给出了算法的复杂性分析和代价模型;(4)在基于时空关系的中间结果管理模型下,在一个在线-离线复杂事件检测原型系统中进行实验,对多个参数(子窗口大小,选择度,匹配率,命中率)进行了算法对比分析.实验结果充分验证了所提出的算法的可行性和高效性.     

Runtime Support for Parallelization of Data-Parallel Applications on Adaptive and Nonuniform Computational Environments

In this paper we discuss the runtime support required for the parallelization of unstructured data-parallel applications on nonuniform and adaptive environments. We describe several optimization techniques for fast remapping of data and for reducing the amount of communications between machines when the computational load on the machines changes during the execution of data-parallel applications. The approach presented is reasonably general and is applicable to a wide variety of regular as well as irregular applications. We present performance results for the solution of an unstructured mesh on a cluster of workstations.     

云计算环境下的大规模图数据处理技术

随着社交网络分析、语义Web分析、生物信息网络分析等新兴应用的快速增长,对亿万个顶点级别大规模图的处理能力的需求愈加迫切,这是当前高性能计算领域的研究和开发热点.文中结合云计算的特点,从图数据管理与图数据处理机制两个方面,综述了云计算环境下进行大规模图数据处理的关键问题,包括图数据的存储方式、图索引结构、图分割策略、图...