面向物联网的分布式上下文敏感复杂事件处理方法

物联网产生的数据具有大数据特征,而这些数据难以用现有数据处理技术进行有效处理.作为物联网中间件的核心技术,复杂事件处理技术具备大数据的海量、复杂性等特征和实时处理的需求.上下文敏感是复杂事件处理引擎的重要特征.提出一种高效的面向物联网的分布式上下文敏感复杂事件处理架构和方法.该方法使用模糊本体进行上下文建模,以支持事件的不确定性及模糊事件查询问题.以基于模糊本体的查询和基于相似性的分布式推理为基础,生成复杂事件查询规划,并通过查询重写,把上下文相关查询转换为上下文无关子查询.根据不同的事件模型和上下文划分数据,并通过优化和多级并行来提高性能.实验结果表明该方法能够处理模糊事件上下文,对于面向物联网的分布式上下文敏感复杂事件处理具有比一般方法更好的性能和可伸缩性.     

RFID数据流分布式层次复杂事件处理

当前RFID应用中的复杂事件处理通常采用集中式的体系结构,这样不能适用于分布式的应用,并且由于存在单点失效和网络拥塞而缺乏健壮性和可伸缩性。同时,很多高效的RFID复杂事件处理方法不能很好地支持层次复杂事件。提出了一种基于CORBA的分布式层次复杂事件处理模型以及高效的基于查询规划的分布式层次复杂事件处理方法。实验结果表明该方法在处理大规模的分布式RFID应用时是有效的。     

CEStream:一种复杂事件流处理语言

复杂事件处理是支持大数据处理的流式计算平台的核心技术之一。CEStream语言作为一种新型的事件流处理语言,支持分布式环境下的复杂事件处理。该语言以XML等层次化数据为数据模型,为复杂事件检测提供了一种正规树模式匹配功能,并且支持结构连接和正规式匹配。同时,针对分布式的多个事件流,其能够将各个事件源模式匹配的结果按照时间顺序再一次进行正规式模式匹配,满足多源组合型复杂事件的检测需求,具有较强的事件处理能力。为了实现CEStream语言,研制了一个基于流数据处理集群和远端查询代理的执行引擎系统。该系统通过远程查询代理实现基于正规树模式的事件检测,通过流处理集群完成多源组合型复杂事件处理。实验表明,该系统实现了CEStream语言,有效地限制了各个节点之间的通信量,充分利用了集群的计算能力,整体性能能够满足应用需求。     

基于事件共享机制的物联网复杂事件处理方法

针对物联网(IOT)复杂事件查询处理过程中的重复查询、存储和处理的问题,提出了事件共享机制(ESM)。首先,为了实现复杂事件的查询与检测,给出了物联网语义事件定义及事件操作符的语义描述;其次,从公共子查询的定义、公共内部查询结构的设计以及事件资源的共享三个角度对物联网事件共享机制展开研究,通过查询表达式的重写、有向无环图(DAG)的构建,以及在结点上使用改进的Continuous参数上下文对事件流进行处理,实现公共子事件查询、存储和处理的共享;最后,构建了基于事件共享机制的语义形式化查询计划处理模型(SFQPM),该模型可自动对查询表达式和查询谓词进行处理,实现复杂事件检测和处理的自动化。仿真结果表明,与基于二叉树(BTree)的处理方法进行对比,所提出的SFQPM具有较高的处理效率和可靠性,实现了复杂事件检测与中间结果共享机制的有机统一,提高了系统的处理效率。最后通过案例研究验证了所提出算法的有效性和可行性。     

分布式RFID数据流的复合事件检测方法*

针对当前RFID(radio frequency identification)复合事件处理技术在性能和处理分布式应用方面的不足,提出了一种基于CORBA（分布对象请求代理体系结构）的分布式复合事件处理模型以及高效的基于查询规划和代价估算的分布式复合事件处理方法。实验结果表明,该方法在处理大规模的分布式RFID应用时是有效的。     

分布式复杂事件流处理平台的研究

复杂事件处理是大数据时代的关键技术之一。针对现有的复杂事件处理平台存在的各种问题,提出并设计了一个以复杂事件流处理语言CEStream为基础的分布式复杂事件流处理平台,实现了基于正规树模式的事件检测功能,支持多数据源的复杂事件处理。针对多数据源组合事件模式的检测需求,该平台可以将复杂事件分解为多个独立的事件检测任务,部署在集群中不同节点和远端的事件检测代理上。实验结果表明,系统的吞吐量和响应时间达到了预期结果,可以满足目前主流的复杂事件处理的应用需求。     

RFID复杂事件检测方法的研究和改进

复杂事件处理（CEP）是一个新兴的技术领域,用于处理大量的简单事件,并从其中整理出有价值的事件。RFID事件和传统事件相比具有海量、时间性和空间性、数据不准确等特征。针对RFID事件的相关特征进行研究,建立一个可扩展的基于规则的RFID复杂事件处理系统是非常必要的。从复杂事件的描述模型、处理框架、上下文语义、检测方法等方面总结了国内外RFID复杂事件处理的研究现状,分析了目前的研究存在的不足,并且针对SASE的复杂事件算法存在的三点不足进行了改进。实验证明相关的改进对简单数据集是有效的。     

基于双数组trie树的多模式复杂事件检测方法

制造物联网中海量实时数据流急需高效的事件检测与处理方法，高效意味着单位时间内使用较小的存储空间处理更多的输入事件。提出一种基于双数组trie树的多模式复杂事件检测方法，通过构建多模式匹配自动机模型减少查询过程中冗余的检测和计算，并利用双数组trie树充分压缩存储空间，从而提高了复杂事件处理的效率。仿真实验表明，提出的方案相比传统的单模式复杂事件检测，具有较小的空间和时间消耗。     

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。     

采用变结构动态贝叶斯网络的交通流量预测

物联网和大数据流式计算的快速发展为智能交通系统的研究带来新的机遇。交通流量预测一直是智能交通系统的关键问题。针对交通流量预测中一个固定模型无法适应多种环境的问题,以及面向数据流的模型更新问题,提出了一种基于变结构动态贝叶斯网络的交通流量预测方法。该方法以复杂事件处理和事件上下文为基础,通过上下文聚类进行历史数据的划分,并通过事件流在线聚类支持聚簇的更新。面向不同聚簇的数据,采取搜索-打分的方法学习对应的贝叶斯网络结构,基于高斯混合模型实现贝叶斯网络的近似推断。在线预测时根据当前上下文选择合适的模型或模型组合进行预测。真实和仿真数据上的实验结果表明,该方法能够获得比当前常用方法更好的预测效果。     

基于Drools规则引擎的物联网数据查询方法设计

物联网(IoT)数据具有数据量大和实时性好的特点。通过复杂事件处理技术处理物联网数据时需要设置复杂的规则,但规则往往会随着业务的变化而变化。Drools规则引擎可以通过分离的配置文件设置规则,在不修改设备数据或管理平台代码的情况下,筛选到匹配规则的数据或事物。为解决DRL规则文件架构和决策表文件架构筛选数据产生的重复匹配的冗余问题,设计了一种修正数据库的数据查询方法。在不同数据量、不同规则数的查询环境下,对DRL规则文件架构、决策表文件架构和修正数据库架构进行了数据查询性能分析。实验结果表明：修正数据库架构在大数据量查询时时间损耗更少,可有效减少冗余。     

情境计算研究综述

作为一种新的计算模式,情境计算得到了学术界和产业界越来越多的关注.随着物联网、云计算、大数据、社会计算等相关技术的不断发展成熟,情境计算进入了快速发展阶段.情境计算是一种通过对获取到的情境信息进行处理、从而得出用户所需服务并主动向用户提供相应情境感知服务的计算模式.这一新的计算模式为使用者的工作、生活带来了舒适和便利.对情境计算的诞生背景进行阐述,介绍情境、情境计算、情境感知、情境感知系统和情境感知服务等关键概念,总结情境数据获取、情境模型与建模、情境推理、主动服务提供、情境感知中间件和安全与隐私等重要研究内容以及其中使用到的关键技术,最后依托情境计算的一般性架构分析得出情境计算的未来发展挑战.     

物联网情景感知技术研究

与互联网不同,物联网(Internet of Things, IoT)通过各类通信技术将具有标识、感知或者执行能力的物理实体互联,形成了“物物互连”的虚拟网络。随着计算机及通信技术的迅速发展,计算资源将通布人们周围的环境,情景感知技术应运而生。情景感知获得传感器采集的情景信息以后,对信息进行智能处理,自主地为用户提供服务。物联网具有海量信息的特性,传统的情景信息处理方法已不再适用。对物联网情景感知技术进行了详细的介绍,首先给出情景和情景感知的概念及其研究发展和应用。然后,结合物联网特性,以情景感知流程为主线,探讨了信息获取、建模和智能处理等内容。最后,系统结构是情景感知的关键,因此对现有的系统结构进行了分析和对比,结合物联网环境论述了当前情景感知系统的不足之处,并给出了情景感知系统的参考结构。     

COLLECT: COLLaborativE ConText-aware service oriented architecture for intelligent decision-making in the Internet of Things

Internet of Things (IoT) has radically transformed the world; currently, every device can be connected to the Internet and provide valuable information for decision-making. In spite of the fast evolution of technologies accompanying the grow of IoT, we are still faced with the challenge of providing a service oriented architecture, which facilitates the inclusion of data coming together from several IoT devices, data delivery among a system's agents, real-time data processing and service provision to users. Furthermore, context-aware data processing and architectures still pose a challenge, in spite of being key requirements in order to get stronger IoT architectures. To face this challenge, we propose a COLLaborative ConText Aware Service Oriented Architecture (COLLECT), which facilitates both the integration of IoT heterogeneous domain context data — through the use of a light message broker — and easy data delivery among several agents and collaborative participants in the system — making use of an enterprise service bus —. In addition, this architecture provides real-time data processing thanks to the use of a complex event processing engine as well as services and intelligent decision-making procedures to users according to the needs of the domain in question. As a result, COLLECT has a great impact on context-aware decentralized and collaborative reasoning for IoT, promoting context-aware intelligent decision making in such scope. Since context-awareness is key for a wide range of recommender and intelligent systems, the presented novel solution improves decision making in a large number of fields where such systems require to promptly process a variety of ubiquitous collaborative and context-aware data.     

Para-G: Path pattern query processing on large graphs

There are plentiful and diverse applications of graph data management and mining techniques in the real-world scientific research and business activities. As one of the most basic operations, uniform path pattern query processing on graph data faces three big challenges. In this paper, we deal with these challenges by the following points. Firstly, a new query language on graph, called G-Path, is presented, which focuses on complex path pattern query processing on a very large graph. Also, the design of a system called Para-G is proposed, which is based on a BSP-like model as well as MapReduce model, and can effectively handle distributed graph data operations and queries. Secondly, the implementation of Para-G on the de facto cloud platform — Hadoop — is brought forward. Based on the concept of distributed path finite state automaton, the query processing of a G-Path statement in Para-G is detailed. In addition, as the query optimization of G-Path queries, several tricks are utilized to dramatically improve the performance of query execution. Finally, extensive experiments on several graph data sets are conducted to show the usability of the G-Path query language and the effectiveness of Para-G.     

Rethinking the architecture design of data center networks

In the rising tide of the Internet of things, more and more things in the world are connected to the Internet. Recently, data have kept growing at a rate more than four times of that expected in Moore’s law. This explosion of data comes from various sources such as mobile phones, video cameras and sensor networks, which often present multidimensional characteristics. The huge amount of data brings many challenges on the management, transportation, and processing IT infrastructures. To address these challenges, the state-of-art large scale data center networks have begun to provide cloud services that are increasingly prevalent. However, how to build a good data center remains an open challenge. Concurrently, the architecture design, which significantly affects the total performance, is of great research interest. This paper surveys advances in data center network design. In this paper we first introduce the upcoming trends in the data center industry. Then we review some popular design principles for today’s data center network architectures. In the third part, we present some up-to-date data center frameworks and make a comprehensive comparison of them. During the comparison, we observe that there is no so-called optimal data center and the design should be different referring to the data placement, replication, processing, and query processing. After that, several existing challenges and limitations are discussed. According to these observations, we point out some possible future research directions.     

Multi‐criteria IoT resource discovery: a comparative analysis

The growth of real‐world objects with embedded and globally networked sensors allows to consolidate the Internet of things paradigm and increase the number of applications in the domains of ubiquitous and context‐aware computing. The merging between cloud computing and Internet of things named cloud of things will be the key to handle thousands of sensors and their data. One of the main challenges in the cloud of things is context‐aware sensor search and selection. Typically, sensors require to be searched using two or more conflicting context properties. Most of the existing work uses some kind of multi‐criteria decision analysis to perform the sensor search and selection, but does not show any concern for the quality of the selection presented by these methods. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi‐objective decision methods and their quality of selection comparing them with the Pareto‐optimality solutions. The gathered results allow to analyse and compare these algorithms regarding their behaviour, the number of optimal solutions and redundancy. Copyright © 2016 John Wiley & Sons, Ltd.     

基于云计算的WebGIS分析构架研究

云计算作为以互联网为中心的一种新兴商业计算模式,也是当前重要的技术热点。 WebGIS是一种利用Internet/Intranet技术,采用HTTP,在Internet/Intranet环境下实现对分布式地理信息的分布式获取、分布式存储、分布式处理、分布式分析、分布式查询、显示和输出的地理信息系统。通过分析云计算、WebGIS的特点,云计算的模型框架以及WebGIS 系统的层次结构,提出将云计算和WebGIS相结合,建立基于云计算的WebGIS构架。对实现海量空间数据存储、空间分析和空间信息检索提供实时地理信息服务,提高了系统的稳定性和效率。     

Sequential Event Pattern Based Design of Context-Aware Adaptive Application

Recent pervasive systems are designed to be context-aware so that they are able to adapt to continual changes of their environments. Rule-based adaptation, which is commonly adopted by these applications, introduces new challenges in software design and verification. Recent research results have identified some faulty or unwanted adaptations caused by factors such as asynchronous context updating, and missing or faulty context reading. In addition, adaptation rules based on simple event models and propositional logic are not expressive enough to address these factors and to satisfy users'' expectation in the design. We tackle these challenges at the design stage by introducing sequential event patterns in adaptation rules to eliminate faulty and unwanted adaptations with features provided in the event pattern query language. We illustrate our approach using the recent published examples of adaptive applications, and show that it is promising in designing more reliable context-aware adaptive applications. We also introduce adaptive rule specification patterns to guide the design of adaptation rules.