电子文档时间戳的分布式时间链安全协议

时间安全在电子合同中是一个十分重要的问题。时间戳解决两个问题：文档是何时生成的；文档是何时发出的。由信任的第三方(T.T.P Trust Third Party)来完成工作，他必须连续记录这些时间形成时间链以保证安全。使用并相信惟一的第三方，仍存在安全隐患介绍了现有的两种协议，提出以多个第三方采用分布式模型形成时间链的协议，提高了时间戳的安全度。     

InforSIB中的复合事件时间模型

复合事件处理通过分析多个事件类型实例之间的关系以产生对应用感兴趣的复合事件.事件处理中已有的时间模型或者使用点时间戳建模原子和复合事件,或者定义的复合事件时间戳考虑不周,导致复合事件检测与复合事件语义存在不一致的结果;另外,需要根据应用需求对时间模型的准确性与复合事件的检测效率作出权衡.针对这两个问题,在面向服务计算平台InforSIB中定义了复合事件时间模型,包括复合事件时间戳和事件不同步与传输延迟的解决方案,最后基于时间模型给出了相应的高效的复合事件检测算法.实验结果证明了时间模型的有效性.     

基于事件顺序的时间戳协议处理

在不考虑网络延迟的情况下,提出一种基于事件顺序的时间戳协议处理方法。在分析时间戳大小与事件关系的基础上,证明为使事件绑定时主体接收的时间戳是新的,状态转移必须满足时间戳关系无环性,并基于事件顺序给出时间戳关系无环性的验证算法。实例表明,该方法易于实现,且具有一定适用性。     

基于椭圆曲线盲签名的安全数字时间戳方案

时间戳能为电子文件或电子交易提供准确的时间证明。该文在时间戳相对认证和时间戳绝对认证2种机制的基础上,提出一种新型的基于椭圆曲线盲签名的安全数字时间戳方案,该方案能有效解决前向时间伪造,防止签名者和TSS合谋作假所引起的后向时间伪造。     

嵌入式CPU的RTC实现时间戳标定的误差分析及改进

RTC时钟为嵌入式CPU系统提供了实时时间信息，在智能终端设备中通常被用来记录外部随机事件发生的时间。本文对此时间戳标定过程中存在的误差进行分析，并给出了基于FPGA代替RTC实现高精度时间戳标定的改进方案。     

基于聚类的区间数时间序列的索引方法

在时间序列数据库中,大多数现有的相似性搜索方法都集中在如何提高算法的效率,而对于由不精确数据组成的时间序列如何进行相似性搜索,则研究比较少,不精确数据经常用区间数据来表示;通过识别区间数时间序列中的重要区间数,使得区间数时间序列的维数大幅度降低,该文针对由区间数组成的时间序列,提出了一种基于低分率聚类的索引方法。实验表明,该方法加快了区间数时间序列的查找过程,不会出现漏报现象。     

超媒体表现时间模型的建立和实现

文献表现的时间模型通过常用时间轴方法，超媒体文献的表现由于增加了交互性，用传统的时间模型来描述已不合适，本文针对这一问题，提出了在传统的时间模型中引入“热点选择”来建立超媒体文献的时间模型，并已在分布式超媒体数据库系统ＨＤＢ中实现，为表示异步和同步的时间事件提供了一个新的方法。     

时间Petri网的ECS-class可达性分析方法

时间Petri网(TPNs)是实时系统时间特性常用的描述和验证的Petri网模型,可达性分析是Petri网模型最基本分析方法.基于"状态类(State-class)"的可达性分析方法不能正确计算并发情况下的时间延迟,而基于"带时间戳的状态类(CS-class)"的可达性分析方法不能正确处理冲突情况下的事件调度,因此提出了"扩展的带时间戳的状态类(ECS-class)"可达性分析方法.它不仅正确的计算时间延迟,而且合理地调度事件.并对一个时间Petri网模型进行可达性分析验证.     

基于高精度时间的时间同步方法研究

为了实现低成本、高精度的时间同步,根据时间戳获取的不同方式,设计了3种方法,并分析了采用这3种方法所能取得的时间戳精度.在此基础上,提出了一种基于Windows平台的时间同步方法,通过在网卡驱动程序和传输驱动程序之间插入一层处理程序,截获时钟计数器并在应用层与系统时间建立关联,同时引入时钟频率调整算法,实现了高精度时间同步.实验结果表明,该方法的同步精度达到亚毫秒级,从而证明了模型的可行性和算法的有效性.     

基于可变时间窗口的增量数据抽取模型

基于合适的数据抽取模型持续不断地将变化的数据从各个数据源系统进行抽取集成,是各个异构系统之间进行数据共享融合的关键,也是构建增量式数据仓库来进行数据分析的关键。传统的时间戳变化数据捕获方式存在因数据抽取过程中发生异常而导致数据抽取失效,进而影响数据抽取效率的问题。鉴于此,文中借鉴时间窗口的思想,采用先抽取少量重复记录再去重的做法,对传统的时间戳增量数据捕获模型进行了改进,提出了基于可变时间窗口的增量数据抽取模型。该模型减少了异常对数据抽取的影响,增强了时间戳增量数据抽取ETL流程的可靠性,在一定程度上提高了数据的抽取效率。     

时间偏差的并行离散事件模拟研究综述*

时间偏差是并行离散事件模拟中广泛使用的一种同步机制。从事件列表管理、消息取消、乐观性控制、状态保存和恢复、内存管理以及全局虚拟时间计算等几个主要方面对时间偏差的并行离散事件模拟进行了探讨,阐述了其存在的问题,并对各种优化策略进行了分析比较和对并行离散事件模拟的应用前景作了一个展望。     

On-line rule matching for event prediction

The prediction of future events has great importance in many applications. The prediction is based on episode rules which are composed of events and two time constraints which require all the events in the episode rule and in the predicate of the rule to occur in a time interval, respectively. In an event stream, a sequence of events which matches the predicate of the rule satisfying the specified time constraint is called an occurrence of the predicate. After finding the occurrence, the consequent event which will occur in a time interval can be predicted. However, the time intervals computed from some occurrences for predicting the event can be contained in the time intervals computed from other occurrence and become redundant. As a result, how to design an efficient and effective event predictor in a stream environment is challenging. In this paper, an effective scheme is proposed to avoid matching the predicate events corresponding to redundant time intervals for prediction. Based on the scheme, we respectively consider two methodologies, forward retrieval and backward retrieval, for the efficient matching of predicate events over event streams. The approach based on forward retrieval construct a queue structure to incrementally maintain parts of the matched results as events arrive, and thus it avoids backward scans of the event stream. On the other hand, the approach based on backward retrieval maintains the recently arrived events in a tree structure. The matching of predicate events is triggered by identifiable events and achieved by an efficient retrieval on the tree structure, which avoids exhaustive scans of the arrived events. By running a series of experiments, we show that each of the proposed approaches has its advantages on particular data distributions and parameter settings.     

Rollback overhead reduction methods for time warp distributed simulation

Parallel discrete event simulation is a useful technique to improve performance of sequential discrete event simulation. We consider the time warp algorithm for asynchronous distributed discrete event simulation. Time warp is an optimistic synchronization mechanism for asynchronous distributed systems that allows a system to violate the synchronization constraint and, in this case, make the system rollback to a correct state. We focus on the kernel of the time warp algorithm, that is the rollback operation, and we propose some techniques to reduce the overhead due to this operation. In particular, we propose a method to reduce the overhead involved in state saving operation, two methods to reduce the overhead of a single rollback operation and a method to reduce the overall number of rollbacks. These methods have been implemented in a distributed simulation environment on a distributed memory system. Some experimental results show the effectiveness of the proposed techniques.     

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.     

A control synthesis approach for time discrete event systems

In this paper, we introduce a control synthesis method for discrete event systems whose behavior is dependent on explicit values of time. Our goal is to control the occurrence dates of the controllable events so that the functioning of the system respects given specifications. The system to be controlled is modeled by a time Petri net. In a previous work we proposed a systematic method to build the timed automaton which models the exact behavior of a time Petri net. Furthermore, the forbidden behaviors of the system are modeled by forbidden timed automaton locations. This paper focuses on the control synthesis method, which consists in computing new firing conditions for the timed automaton transitions so that the forbidden locations are no longer reachable.     

Run-time selection of the checkpoint interval in Time Warp based simulations

Time warp discrete event simulators take advantage of the parallel processing of simulation events. On the other hand, they suffer from the overhead required to enforce the causality relation. This overhead consists of the time for saving the states of logical processes, the time for the rollback procedures and the wasted simulation time, that is the time spent for the processing of events which are undone because of rollback. Two techniques have been developed for state saving: periodic and incremental. In this paper we study the periodic technique, and we present an analytical model describing the simulation execution time in function of both the state saving cost and the rollback cost. Furthermore, we derive a methodology that allows each logical process to adapt its state saving period on line in order to reduce the simulation execution time. Experimental results show that, in some simulation scenarios, our methodology improves performance, in comparison to already existing proposals.     

基于动态时间弯曲的时序数据聚类算法的研究

时间序列是一类重要的复杂类型数据，时间序列知识发现正成为知识发现的研究热点之一。欧几里的距离及其扩展作为相似测度被广泛应用于时间序列的比较中，但是这种距离测度对数据没有好的鲁棒性。动态时间弯曲技术是基于非线性动态编程的一种模式匹配算法。该文提出了基于动态时间弯曲技术的相似搜索算法，通过计算时序数据之间的最短弯曲路径来获得序列的匹配。对综合控制时序数据进行基于不同距离测度的聚类分析对比结果表明该文提出的算法有很高的精度和对振幅差异、噪声和线性漂移有强的鲁棒性，具有良好的应用价值。     

Analyzing time-dimension communication characterizations for representative scientific applications on supercomputer systems

Exascale computing is one of the major challenges of this decade, and several studies have shown that communications are becoming one of the bottlenecks for scaling parallel applications. The analysis on the characteristics of communications can effectively aid to improve the performance of scientific applications. In this paper, we focus on the statistical regularity in time-dimension communication characteristics for representative scientific applications on supercomputer systems, and then prove that the distribution of communication-event intervals has a power-law decay, which is common in scientific interests and human activities. We verify the distribution of communication-event intervals has really a power-lawdecay on the Tianhe-2 supercomputer, and also on the other six parallel systems with three different network topologies and two routing policies. In order to do a quantitative study on the power-law distribution, we exploit two groups of statistics: bursty vs. memory and periodicity vs. dispersion. Our results indicate that the communication events show a “strong-bursty and weak-memory” characteristic and the communication event intervals show the periodicity and the dispersion. Finally, our research provides an insight into the relationship between communication optimizations and time-dimension communication characteristics.     

Probabilistic temporal reasoning and its application to fossil power plant operation

Many real-world applications, such as industrial diagnosis, require an adequate representation and inference mechanism that combines uncertainty and time. In this work, we propose a novel approach for representing dynamic domains under uncertainty based on a probabilistic framework, called temporal nodes Bayesian networks (TNBN). The TNBN model is an extension of a standard Bayesian network, in which each temporal node represents an event or state change of a variable and the arcs represent causal–temporal relationships between nodes. A temporal node has associated a probability distribution for its time of occurrence, where time is discretized in a finite number of temporal intervals; allowing a different number of intervals for each node and a different duration for the intervals within a node (multiple granularity). The main difference with previous probabilistic temporal models is that the representation is based on state changes at different times instead of state values at different times. Given this model, we can reason about the probability of occurrence of certain events, for diagnosis or prediction, using standard probability propagation techniques developed for Bayesian networks. The proposed approach is applied to fossil power plant diagnosis through two detailed case studies: power load increment and control level system failure. The results show that the proposed formalism could help to improve power plant availability through early diagnosis of events and disturbances.