主数据管理驱动的高校信息化SOA建设

异构信息系统在高校中的大量存在阻碍了数据资产整合与信息交互，面向服务的架构（SOA）的出现及在企业中的广泛采用为解决此问题提供了思路，但在高校中实施SOA存在难度大、难以形成以SOA为基础的信息化生态的问题。针对这些问题，提出主数据管理驱动的SOA建设方案。首先，在数据层面运用主数据管理平台对校级核心数据资产进行建模和整合；为实现数据同步和消费，并解决其中存在的协议转换及服务鉴权问题，提出了基于企业服务总线的解决方案；然后，针对遗留"信息孤岛"系统进行SOA改造，提出主数据驱动的建设方案。实验结果表明单用户、10用户、100用户及10000用户并发下的平均延迟分别为8、11、59及18 ms，表明在不同并发场景下所提方案性能均满足高校业务需求。实施结果表明，数据资产整合和信息交互问题得到了解决，因此方案具有可行性。     

基于信息总线服务的智能数据流自适应集成方法

为了提高信息服务中心的智能数据处理能力,提出一种基于信息总线服务的智能数据流自适应集成方法,构建智能数据流自适应信息处理模型,采用统计时间序列分析方法进行信息总线服务的大数据处理和信号集成,采用同步与匹配滤波方法进行信息总线服务数据信息流的滤波检测,采用快速傅里叶变换进行智能数据流自适应集成信号滤波处理,提高智能数据流的纯度。采用重叠保留法实现信息总线服务数据流的混频处理,构建相关匹配检测器实现信息总线服务数据流的分路、插零、取反等运算,根据运算结果实现智能数据流自适应集成。仿真结果表明,采用该方法进行信息总线服务数据流集成的混频性能较好,提高了信息总线服务数据流的检测和信息融合能力,提高了输出信噪比。     

The design and implementation of service process reconfiguration with end-to-end QoS constraints in SOA

Service processes in SOA are composed dynamically by services from different service providers. At run-time, some services may become faulty and cause a service process to violate its end-to-end quality of service (QoS) constraints. We propose an effective approach for replacing only faulty services and some of their neighboring services to maintain the original end-to-end QoS constraints. We use an iterative algorithm to search for a reconfiguration region that has replaceable services to meet the original QoS constraint for the region. Services in reconfiguration regions may be replaced using one-to-one, one-to-many, or many-to-one service mappings. By replacing only services in reconfiguration regions rather than the whole service process, reconfiguration overheads are lowered and service disruptions may be reduced. We have implemented the Adaptation Manager in the Llama ESB middleware. Performance study shows that our approach may efficiently repair service processes.     

Vehicle mobility pattern-based handover scheme using discrete-time Markov chain

For the improvement of the quality of service (QoS) of wireless Internet users traveling in vehicles, it is effective to reduce the service disruption time by avoiding unnecessary handover occurrence, considering the vehicles’ movement paths. This paper proposes a handover scheme suitable for users traveling in vehicles, which enables continuous learning of the handover process using a discrete-time Markov chain (DTMC). The proposed handover scheme avoids unnecessary handover trials when a short dwell time in a target cell is expected or when the target cell is an intermediate cell through which the vehicle quickly passes. For verifying the performance of the proposed scheme, we observe the average number of handover trials and the average throughput along various paths, which are real bus lines. The results show that the proposed scheme reduces the number of handover occurrences and maintains adequate throughput.     

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.     

Research on development of embedded uninterruptable power supply system for IOT-based mobile service

Under the banner of Internet of Things (IOT), mobile service is an important hot research topic. As a part of IOT, embedded systems can offer access to the devices on Internet. For supporting mobile service, one design and implementation solution of embedded Uninterruptable Power Supply (UPS) system is brought forward for long-distance monitoring and controlling UPS based on IOT. The key insight of our development is that we adopt new idea to realize new application based on new technology of IOT. The target is for mobile service to support pervasive computing paradigm. Our new idea is involved in the developed system which is based on new Arm chips, a set of new ATmega products (such as ATmega 161, ATmega 162), new Network Interface Controller, TCP/IP protocol suite and other software components. In our designed UPS system, an enhanced embedded file system is designed and implemented which saves the data and index information on the chip (such as a serial EEPROM) in structured way and communicates with MCU through I²C bus. The new file system is embedded in the UPS system. Mobile users can access and manipulate local or remote data on the client side based on IOT. Embedded file system on the chips plays a major role in the growth of IP networking. Based on our experiments, mobile users can easily monitor and control UPS in different places of long-distance. By our experiments and applications, the performance of this kind of new embedded UPS system is satisfied the requirements of all kinds of IOT-based mobile services.     

一种面向临床决策推理的可视诊疗技术

海量医学信息的快速增长已远远超出人类认知能力,医疗服务环境和用户人群的复杂多样性使得海量数据难以在现有能力和工具的支持下满足广大用户对于信息服务的需求.临床诊疗服务的可视化、智能化程度不高导致现有的医学知识服务水平难以保证海量资源信息的充分利用.在分析了临床诊疗环境下人机协同认知特性的基础上给出了一种基于语义层次的信息组织方式;分析了符合该数据组织模式的可视形态及自然的可视交互技术;在上述工作的基础上构建了一个面向临床决策推理的可视诊疗分析框架,并给出了原型系统实例加以验证.结果表明,通过结合交互式可视化和自动分析技术,可以有效地帮助人们从海量数据中获取到有用的信息模式,减轻人们对数据进行分析的负担,为医疗诊断过程提供决策支持服务.     

A simulation study of an on‐demand transportation system

In this paper we present the results of a simulation study aimed at assessing an on‐demand transportation system. The on‐demand system uses minibuses that have neither fixed itineraries nor fixed stops. The minibuses are dynamically routed to accommodate the requests received by the users. To use the on‐demand service, users communicate, close to their desired departure time, the origin and destination of the trip. They accept the service if the estimated arrival time at destination fulfills their service level threshold. In the simulation users may decide whether to walk, to use a standard bus, to call the on‐demand service, and, if none of these options is satisfactory, to use a private car. We consider different scenarios to assess the potential benefits of the introduction of an on‐demand service. We also analyze the scalability and responsiveness of the service. The results suggest that an on‐demand system may be able to satisfy a large portion of user transportation requests and may be put beside standard transportation systems in order to provide a better transportation service to the users and substantially reduce the use of private cars.     

An observational comparison of the older and younger bus passenger experience in a developing world city

This study was an unobtrusive observational analysis of 333 older and younger bus passengers in Guadalajara, Mexico. A set of data were collected for each observed passenger, as well as more general observations related to driver behaviour, bus design and bus service characteristics. There were significant differences between older and younger passengers in terms of boarding and alighting times, use of handrails, seat location preferences, passenger stability and coping strategies in order to maintain postural stability. The conditions of travel are conducive to a poor passenger experience for the older passengers in particular. Although the problems may be attributed to bus design and driver behaviour typical of that in developing countries, they are also influenced by the wider transport infrastructure, and a lack of a regulatory regime which places drivers under time pressure and in direct competition with each other.

Practitioner Summary: Bus services must cater for all ages of passengers, including the elderly. This unobtrusive observational study investigated the passenger experience in a developing world city. Bus and wider service design were found to compromise the journey experience, with the older users being particularly negatively impacted. Design recommendations are provided.     

一种动态和自适应公交到站时间预测方法

公交到站时间预测是实现智能化公交信息服务的基础,可靠地预测公交到站时间有利于提高公共交通的服务水平,以吸引更多的城市居民选择公共交通.以某城市公交系统海量的历史数据为基础,建立了基于SVM的集合了静态和动态数据的公交预测模型,该模型引入上游路段速度、下游路段最新速度、下游路段最新花时、时间段和路况拥挤程度等动态信息作为模型特征.在此基础上,根据大量公交到站时间历史数据的波动性,提出了一个基于波动性的自适应预测模型.实验结果表明,自适应预测模型优于现有模型,提高了预测的精确度和效率.     

一种基于事件的情境感知框架

为更好地满足用户的个性化需求、支持情境感知服务,提出一种基于事件的情境感知框架。该框架包括传感器层、事件总线和应用服务层,采用事件触发的方式调用应用服务与用户交互,并建立基于规则的事件过滤机制,对无效事件进行屏蔽。应用结果表明,该框架可提高系统的感知性能和重用性。     

Discovering users with similar internet access performance through cluster analysis

Users typically subscribe to an Internet access service on the basis of a specific download speed, but the actual service may differ. Several projects are active collecting internet access performance measurements on a large scale at the end user location. However, less attention has been devoted to analyzing such data and to inform users on the received services. This paper presents MiND, a cluster-based methodology to analyze the characteristics of periodic Internet measurements collected at the end user location. MiND allows to discover (i) groups of users with a similar Internet access behavior and (ii) the (few) users with somehow anomalous service. User measurements over time have been modeled through histograms and then analyzed through a new two-level clustering strategy. MiNDhas been evaluated on real data collected by Neubot, an open source tool, voluntary installed by users, that periodically collects Internet measurements. Experimental results show that the majority of users can be grouped into homogeneous and cohesive clusters according to the Internet access service that they receive in practice, while a few users receiving anomalous services are correctly identified as outliers. Both users and ISPs can benefit from such information: users can constantly monitor the ISP offered service, whereas ISPs can quickly identify anomalous behaviors in their offered services and act accordingly.     

Predicting the passenger demand on bus services for mobile users

Public transport, especially the bus transport, can reduce the private car usage and fuel consumption, and alleviate traffic congestion. However, when traveling in buses, the travelers not only care about the waiting time, but also care about the crowdedness in the bus itself. Excessively overcrowded bus may drive away the anxious travelers and make them reluctant to take buses. So accurate, real-time and reliable passenger demand prediction becomes necessary, which can help determine the bus headway and help reduce the waiting time of passengers. Based on a large database from a real bus system, this paper aims to present a passenger demand prediction system for mobile users. The system includes a server-side bus information data stream processing and mining program and a client-side mobile application for Android smartphones. The server program continuously monitors for each bus stop the number of passengers waiting at the bus stop, the number of passengers that will pass the bus stop, as well as the traffic conditions in the area around the stop. It delivers real time bus and traffic information to mobile users via restful web services. The client-side location-based mobile application consumes these services to help mobile users make informed transportation choices. For example the availability of buses might be a deterrent when they are too crowded. However, there are three major challenges for predicting the passenger demands on bus services: inhomogeneous, seasonal bursty periods and periodicities. To overcome the challenges, we propose three predictive models and further take a data stream ensemble framework to predict the number of passengers. We develop a prototype system with different types of Android based mobile phones and comprehensively experiment over a 22-week period. The evaluation results suggest that the proposed system achieves outstanding prediction accuracy among 86,411 passenger demands on bus services, more than 78% of them are accurately forecasted.     

Wearable IoT data stream traceability in a distributed health information system

With the soaring interest in the Internet of Things (IoT), some healthcare providers are facilitating remote care delivery through the use of wearable devices. These devices are employed for continuous streaming of personal medical data (e.g., vitals, medications, allergies, etc.) into healthcare information systems for the purposes of health monitoring and efficient diagnosis. However, a challenge from the perspective of the physicians is the inability to reliably determine which data belongs to who in real-time. This challenge emanates from the fact that healthcare facilities have numerous users who own multiple devices; thereby creating an N x M data source heterogeneity and complexities for the streaming process. As part of this research, we seek to streamline the process by proposing a wearable IoT data streaming architecture that offers traceability of data routes from the originating source to the health information system. To overcome the complexities of mapping and matching device data to users, we put forward an enhanced Petri Nets service model that aids with a transparent data trace route generation, tracking and the possible detection of medical data compromises. The results from several empirical evaluations conducted in a real-world wearable IoT ecosystem prove that: 1) the proposed system’s choice of Petri Net is best suited for linkability, unlinkability, and transparency of the medical IoT data traceability, 2) under peak load conditions, the IoT architecture exhibits high scalability, and 3) distributed health information system threats such as denial of service, man-in-the-middle, spoofing, and masking can be effectively detected.     

QoS-aware service selection via collaborative QoS evaluation

We present in this paper a novel collaborative filtering based scheme for evaluating the QoS of large scale Web services. The proposed scheme automates the process of assessing the QoS of a priori unknown service providers and thus facilitates service users in selecting services that best match their QoS requirements. Most existing service selection approaches ignore the great diversity in the service environment and assume that different users receive identical QoS from the same service provider. This may lead to inappropriate selection decisions as the assumed QoS may deviate significantly from the one actually received by the users. The collaborative filtering based approach addresses this issue by taking the diversity into account instead of uniformly applying the same QoS value to different users. They predict a user’s QoS on an unknown service by exploiting the historical QoS experience of similar users. Nevertheless, when only limited historical QoS data is available, these approaches either fail to make any predictions or make very poor ones. The cornerstone of the proposed QoS evaluation scheme is a Relational Clustering based Model (or RCM) that effectively addresses the data scarcity issue as stated above. Experimental results on both real and synthetic datasets demonstrate that the proposed scheme can more accurately predict the QoS on unknown service providers. The efficient performance also makes it applicable to QoS evaluation for large scale Web services.     

MS/TP总线网络延迟性能分析与仿真

研究了BACnet总线通信,针对MS/TP网络中的节点和报文传输性能进行了分析。运用有限状态机理论,在仿真环境下建立了混合型MS/TP网络的随机通信模型。针对应用层服务如读属性报文的传输等进行了仿真运行,评估了MS/TP网络传输的平均服务延迟特性,给出了楼宇控制器网络通信中的网络数据帧数、传输速率、延迟三者之间的关系。研究表明,当报文产生时间平均间隔一定,随着最大数据帧数增加,总线利用率变大,网络平均延迟变大。运用该方法可针对实际的网络结构和节点数量,评估出该网络的数据传输最大平均延迟和最大数据帧数,为总线网络的设计及优化提供了依据。     

基于物元和QoS约束的数据挖掘服务选择

为更好地选择满足用户要求的数据挖掘服务,运用可拓物元模型描述数据挖掘服务和用户需求。对数据挖掘服务进行可拓分析,计算候选数据挖掘服务与用户需求服务的相似度。构造数据挖掘QoS用户约束元,设计用于评估相似服务的方法,帮助用户选择满足要求的数据挖掘服务。实验结果表明该方法提高了选择效率。     

Location-Aware Tools for Improving Public Transit Usability

Real-time arrival information, immediately available on mobile devices, can significantly enhance the usability of public transit systems. The OneBusAway system provides just such information to more than 7,000 Seattle-area bus riders per day. The authors describe a new location-aware native iPhone application for OneBusAway that provides bus stop and arrival information tailored to the user's location. Results from survey and user-study evaluations demonstrate quicker access to data using the location-aware tool. In addition, data for OneBusAway users as a whole show strikingly positive changes in rider satisfaction, the number of transit trips per week, reduced wait time at bus stops, and increased walking. The positive results also hold for the location-aware version users, suggesting the possibility of even further gains.     

低速总线分析系统的设计实现

提出了一种适用于弹箭的多种总线自动诊断系统,弹上总线诊断分析主要包括对多种总线的电气层和协议层的诊断分析,论述了该诊断系统的方案、硬件架构和软件设计,并阐述了系统实现原理和技术特点;试验表明自动诊断系统能够实时分析多种总线数据的各种电气特性和通信协议,并且能够对总线数据进行实时记录,从而能够全面的对总线进行评价,快速诊断多种总线中差分电压共模电压、显性电压、隐性电压、波特率等主要电气特性参数,提高故障定位和分析的能力和手段.     

A service accountability framework for QoS service management and engineering

Service science, management and engineering (SSME) research is to study the methodology and technology for service innovation, design, development and delivery. Since service industry is very quality-sensitive and trust-dependent, we propose a service accountability management framework to detect, diagnose, defuse and disclose the root cause for any problematic service process. The accountability support is important for SSME since service processes often rely on external service providers to deliver part of the service functionalities. A service system must have effective yet efficient mechanisms to ensure that every external service is delivering a consistent and acceptable level of performance to meet the end-to-end quality of service (QoS) of the whole service process. In this paper, we present the accountability framework, identify the components in an accountable service architecture, and design an accountability diagnosis methodology. We also briefly present the inteLLigent Accountability Management Architecture (LLAMA) project which implements the accountability service bus (ASB), an agent-based middleware to support the monitoring, diagnosis, and reconfiguration of e-services. LLAMA ASB interacts with accountability agents to monitor services and the Accountability Authority to automatically diagnose faulty situations. The LLAMA technology is useful to ensure the QoS in SSME-based systems.