基于全局同步逻辑时间的访存依赖约减方法

并发执行的并行多线程程序执行过程中,不同的访存顺序会得到不同的执行结果.由于再次执行时,难以重现首次执行时的错误,导致并行程序的调试非常困难.确定性重放是解决该问题的一种方法,目的是通过记录并行程序执行过程中的不确定性事件,然后利用记录的事件重现出程序的原始执行.然而,已有的确定性重放方法会产生大量的记录日志,如何减小记录日志是确定性重放领域的研究热点,在实际应用中也是非常具有挑战性的问题.为了减小记录日志的开销,文中提出了一种基于逻辑时间的访存依赖约减方法,并在支持松弛存储一致性模型的处理器上提出具体的实现技术,该方法利用了访存依赖对应的逻辑时间之间的序关系进行约减.通过模拟评估所提出方法的性能和可扩展性.其中,在8核模拟平台上,通过Splash2测试程序进行评估,结果显示所提出的记录方法平均日志开销为0.11Bytes/Kilo-Instruction,与目前最好的访存依赖约减方法Timetraveler相比提高了75%;通过4核、8核和16核平台的评估结果,表明所提出约减方法具有较好的可扩展性.     

逻辑Petri网非自由选择结构模型修复方法

业务过程通常在信息系统中实现之前由过程模型描述和验证。过程模型可以描述系统的特性,并通过向系统设计者提供反馈的功能来验证系统的正确性。当系统生成的事件日志中的活动与过程模型中的活动存在偏差时,需修复现有模型。对于含非自由选择结构的模型,尽管事件日志中的活动可以由现有的修复方法而得到重放,但修复后的模型往往会与原模型在结构上有很大的不同,此外,还会导致模型精确度不高且模型结构复杂。因此本文提出一种基于逻辑Petri网新的模型修复方法。首先给出了变迁对和后继关系的概念,构造出后继关系矩阵。接着通过遍历变迁对来确定模型需要修复的位置。最后通过实验验证方法的正确性和可行性。     

基于混合人工免疫算法的流程挖掘事件日志融合方法

流程挖掘是流程管理和数据挖掘交叉领域中的一个研究热点.在实际业务环境中,流程执行的数据往往分散记录到不同的事件日志中,需要将这些事件日志融合成为单一事件日志文件,才能应用当前基于单一事件日志的流程挖掘技术.然而,由于流程日志间存在着执行实例的多对多匹配关系、融合所需信息可能缺失等问题,导致事件日志融合问题具有较高挑战性.本文对事件日志融合问题进行了形式化定义,指出该问题是一个搜索优化问题,并提出了一种基于混合人工免疫算法的事件日志融合方法：以启发式方法生成初始种群,人工免疫系统的克隆选择理论基础,通过免疫进化获得“最佳”的融合解,从而支持包含多对多的实例匹配关系的日志融合;考虑两个实例级别的因素：流程执行路径出现的频次和流程实例间的时间匹配关系,分别从“量”匹配和“时间”匹配两个维度来评价进化中的个体;通过设置免疫记忆库、引入模拟退火机制,保证新一代种群的多样性,减少进化早熟几率.实验结果表明,本文的方法能够实现多对多的实例匹配关系的事件日志融合的目标,相比随机方法生成初始种群,启发式方法能加快免疫进化的速度.文中还针对利用分布式技术提高事件日志融合性能,探讨了大规模事件日志的分布式融合中的数据划问题.     

基于行为轮廓和逻辑Petri网的模型修复方法

现实中的业务流程不断发生变化，需要对初始的业务流程模型进行修复以更好地表示实际业务流程。模型修复的关键步骤是分析现实日志和模型间的偏差，目前寻找偏差的方法主要采用对齐重演技术，未从行为的角度定量分析抽象的结构。因此，提出了一种通过行为轮廓分析日志和模型偏差的方法，并在此基础上进一步给出了基于逻辑Petri网的模型修复方法。首先，基于行为轮廓计算日志和模型间的服从度以识别偏差迹；然后，在偏差迹中依据偏差三元组集从偏差活动中选择逻辑变迁；最后，基于逻辑变迁设置逻辑函数，并通过添加新的分支或重构新的结构来修复原模型。对修复模型的适应度和精确度进行了验证，仿真实验结果表明，在尽可能保持修复模型与原始模型相似的基础上，相较于Fahland方法与Goldratt方法，所提修复方法在适应度都为1的情况下，得到的修复模型具有更高的精确度。     

一种基于Token Log的符合性检查方法

使用事件日志进行符合性检查的主要方法是:使用过程模型模拟执行事件日志中的任务序列,通过统计可被模型再现的任务序列及模型运行中可能触发的非运行序列中的任务个数,判断模型与日志的符合程度.但这种判断方法并不完备:如果模型中包含大量选择结构,则即使日志是模型本身的日志,也会因为模拟执行较多任务时会触发当前序列外的其他任务,而误判日志与模型的符合性较低;或者,如果模型中只包含少数的并发结构和多数的顺序结构,则即使日志只包含顺序结构的内容且非该模型对应日志时,也会因为在模拟执行时只有个别任务会导致模型无法继续执行,而其他多数任务可以执行而误判日志与模型有较高的符合性.基于已有方法的弱点,提出了使用日志内容检查模型结构正确性与使用模型结构检查日志内容完整性的双向检查标准,并提出一种内容特征与模型结构特征一一对应的新型日志——Token Log,用于过程模型与系统日志的符合性检查,使得检查和判断过程更加清晰简洁,结果更加准确.     

基于活动恢复集的有效低频行为分析方法

低频行为识别是揭示业务流程重要信息和优化流程模型的方法之一,现有流程发现方法忽略了数据影响链对低频行为产生的影响,导致了一些低频行为被视为噪声直接过滤掉。针对这一问题,提出了一种基于活动恢复集的有效低频行为分析方法。首先根据事件日志中的行为重要性过滤日志,并构建初始流程模型;其次从事务日志中提取活动的输入输出数据项,并根据这些数据项构造活动影响链图,在此基础上获取每个活动基于迹的活动恢复集;最后根据活动恢复集来计算每条迹的行为容忍度以区分有效低频行为和噪声。实验结果表明,与其他方法相比,该方法能够有效区分有效低频行为与噪声,并且从拟合度、精度以及简单性方面提高了流程模型的质量。该方法考虑了由活动恢复集而导致的偏差情况,可以成功识别事件日志中的有效低频行为,从而优化了流程模型。     

工作流重构技术研究

先进的工作流技术与传统的企业管理信息系统相结合,日益成为提高企业信息化的一个重要手段。目前的工作流是基于模型驱动的,定义一个完整的模型是相当复杂和费时的;而且,实际业务流程同流程模型之间必然存在差异。本文介绍了工作流网,工作流日志的概念;提出了一种基于日志包含的信息来重构业务流程模型的算法,该算法还能处理日志中的干扰信息和有效地度量流程模型和实际业务流程之间的差异。     

基于拟间接依赖的流程挖掘优化分析

业务流程挖掘旨在从记录的事件日志中挖掘出满足人们需求的流程模型。以往的方法多是根据事件之间的直接依赖关系建立流程模型,具有一定的局限性,提出了基于拟间接依赖的流程挖掘优化分析方法。依据事件日志,以行为轮廓为基础,构建初始模型。在执行日志下,通过基于整数线性规划流程发现算法的基本约束体查找出具有拟间接依赖关系的变迁对,并对模型进行完善,挖掘出优化模型。通过具体的实例分析验证了该方法的有效性。     

基于BERT的多视角事件日志修复

在业务流程执行过程中,由于信息系统故障或者人工记录出错等问题导致事件日志中数据的丢失,从而产生缺失的事件日志,使用这种缺失日志会严重影响业务流程分析结果的质量。针对这种缺失日志的修复问题,现有的研究大部分仅从数据视角或者行为视角进行展开,很少从数据和行为相融合的视角开展事件日志的修复工作。提出了一种基于BERT模型的多视角事件日志修复方法。该方法利用双层BERT模型,从数据和行为融合的视角训练模型,通过BERT模型的预训练任务（masked attribute model,MAM）和（masked event model,MEM）以及Transformer编码块的注意力机制,捕获输入属性的双向语义信息和长期依赖关系,使用微调策略进行模型训练,以预测的形式修复事件日志中的缺失值。最后,通过公开可用的数据集进行评估分析,结果表明,该方法在修复事件日志方面表现良好。     

基于Petri网行为紧密度的有效低频行为模式分析

低频行为模式分析是流程管理的重要内容之一,有效区分低频日志和噪音日志在业务流程过程挖掘中显得尤为重要。目前已有的研究大部分是将流程模型中的低频行为当作噪音直接过滤,但有些低频行为对模型是有效的。文中提出了基于Petri网行为紧密度的有效低频模式分析方法。首先,根据给定的事件日志建立合理的流程模型;然后,通过迭代扩展初始模式来发现流程模型中的所有低频日志序列,并在此基础上计算日志与模型的行为距离向量,利用日志与模型的行为紧密度找出有效的低频行为模式;最后,通过实例分析验证了所提方法的可行性。     

Model repair — aligning process models to reality

Process mining techniques relate observed behavior (i.e., event logs) to modeled behavior (e.g., a BPMN model or a Petri net). Process models can be discovered from event logs and conformance checking techniques can be used to detect and diagnose differences between observed and modeled behavior. Existing process mining techniques can only uncover these differences, but the actual repair of the model is left to the user and is not supported. In this paper we investigate the problem of repairing a process model w.r.t. a log such that the resulting model can replay the log (i.e., conforms to it) and is as similar as possible to the original model. To solve the problem, we use an existing conformance checker that aligns the runs of the given process model to the traces in the log. Based on this information, we decompose the log into several sublogs of non-fitting subtraces. For each sublog, either a loop is discovered that can replay the sublog or a subprocess is derived that is then added to the original model at the appropriate location. The approach is implemented in the process mining toolkit ProM and has been validated on logs and models from several Dutch municipalities.     

基于托肯重演的并行结构过程模型修复方法

过程挖掘可以根据企业信息系统生成的事件日志建立业务过程模型。当实际业务过程发生变化时,过程模型与事件日志之间会产生偏差,这时需要对过程模型进行修正。对于含有并行结构的过程模型修复,由于加入自环和不可见变迁等因素,有些现有的修正方法的精度会降低。因此提出一种基于逻辑Petri网和托肯重演的并行结构过程模型修复方法。首先根据子模型的输入输出库所与日志的关系,确定子模型的插入位置;然后通过托肯重演的方式确定偏差所在位置;最后根据基于逻辑Petri网提出的方法进行过程模型的修复。在ProM平台上进行了仿真实验,验证了该方法的正确性和有效性,并与Fahland等方法进行对比分析。结果表明,所提方法的精度达到85%左右,相比Fahland、Goldratt方法分别提高了17和11个百分点;在简洁度方面该算法没有增加自环和不可见变迁,而Fahland和Goldratt方法均增加了不可见变迁和自环;三种方法的拟合度均在0.9以上,而Goldratt方法略低一些。以上证明用所提方法修正后的模型具有更高的拟合度和精度。     

Discovering process models from event multiset

The aim of process mining is to discover the process model from the event log which is recorded by the information system. Typical steps of process mining algorithm can be described as: (1) generating event traces from event log, (2) analyzing event traces and obtaining ordering relations of tasks, (3) generating process model with ordering relations of tasks. The first two steps could be very time consuming involving millions of events and thousands of event traces. This paper presents a novel algorithm (λ-algorithm) which almost eliminates these two steps in generating event traces from event log and analyzing event traces so as to reduce the performance of process mining algorithm. Firstly, we retrieve the event multiset (input data of algorithm marked as MS) which records the frequency of each event but ignores their orders when extracted from event logs. The event in event multiset contains the information of post-activities. Secondly, we obtain ordering relations from event multiset. The ordering relations contain causal dependency, potential parallelism and non-potential parallelism. Finally, we discover a process models with ordering relations. The complexity of λ-algorithm is only bound up with the event classes (the set of events in event logs) that has significantly improved the performance of existing process mining algorithms and is expected to be more practical in real-world process mining based on event logs, as well as being able to detect SWF-nets, short-loops and most of implicit dependency (generated by non-free choice constructions).     

BPMN Miner: Automated discovery of BPMN process models with hierarchical structure

Existing techniques for automated discovery of process models from event logs generally produce flat process models. Thus, they fail to exploit the notion of subprocess as well as error handling and repetition constructs provided by contemporary process modeling notations, such as the Business Process Model and Notation (BPMN). This paper presents a technique, namely BPMN Miner, for automated discovery of hierarchical BPMN models containing interrupting and non-interrupting boundary events and activity markers. The technique employs approximate functional and inclusion dependency discovery techniques in order to elicit a process–subprocess hierarchy from the event log. Given this hierarchy and the projected logs associated to each node in the hierarchy, parent process and subprocess models are discovered using existing techniques for flat process model discovery. Finally, the resulting models and logs are heuristically analyzed in order to identify boundary events and markers. By employing approximate dependency discovery techniques, BPMN Miner is able to detect and filter out noise in the event log arising for example from data entry errors, missing event records or infrequent behavior. Noise is detected during the construction of the subprocess hierarchy and filtered out via heuristics at the lowest possible level of granularity in the hierarchy. A validation with one synthetic and two real-life logs shows that process models derived by the proposed technique are more accurate and less complex than those derived with flat process discovery techniques. Meanwhile, a validation on a family of synthetically generated logs shows that the technique is resilient to varying levels of noise.     

Scalable process discovery and conformance checking

Considerable amounts of data, including process events, are collected and stored by organisations nowadays. Discovering a process model from such event data and verification of the quality of discovered models are important steps in process mining. Many discovery techniques have been proposed, but none of them combines scalability with strong quality guarantees. We would like such techniques to handle billions of events or thousands of activities, to produce sound models (without deadlocks and other anomalies), and to guarantee that the underlying process can be rediscovered when sufficient information is available. In this paper, we introduce a framework for process discovery that ensures these properties while passing over the log only once and introduce three algorithms using the framework. To measure the quality of discovered models for such large logs, we introduce a model–model and model–log comparison framework that applies a divide-and-conquer strategy to measure recall, fitness, and precision. We experimentally show that these discovery and measuring techniques sacrifice little compared to other algorithms, while gaining the ability to cope with event logs of 100,000,000 traces and processes of 10,000 activities on a standard computer.     

Change visualisation: Analysing the resource and timing differences between two event logs

With organisations facing significant challenges to remain competitive, Business Process Improvement (BPI) initiatives are often conducted to improve the efficiency and effectiveness of their business processes, focussing on time, cost, and quality improvements. Event logs which contain a detailed record of business operations over a certain time period, recorded by an organisation's information systems, are the first step towards initiating evidence-based BPI activities. Given an (original) event log as a starting point, an approach to explore better ways to execute a business process was developed, resulting in an improved (perturbed) event log. Identifying the differences between the original event log and the perturbed event log can provide valuable insights, helping organisations to improve their processes. However, there is a lack of automated techniques and appropriate visualisations to detect the differences between two event logs. Therefore, this research aims to develop visualisation techniques to provide targeted analysis of resource reallocation and activity rescheduling. The differences between two event logs are first identified. The changes between the two event logs are conceptualised and realised with a number of visualisations. With the proposed visualisations, analysts are able to identify resource- and time-related changes that resulted in a cost reduction, and subsequently investigate and translate them into actionable items for BPI in practice. Ultimately, analysts can make use of this comparative information to initiate evidence-based BPI activities.     

Efficient and stable quorum-based log replication and replay for modern cluster-databases

The modern in-memory database (IMDB) can support highly concurrent on-line transaction processing (OLTP) workloads and generate massive transactional logs per second. Quorum-based replication protocols such as Paxos or Raft have been widely used in the distributed databases to offer higher availability and fault-tolerance. However, it is non-trivial to replicate IMDB because high transaction rate has brought new challenges. First, the leader node in quorum replication should have adaptivity by considering various transaction arrival rates and the processing capability of follower nodes. Second, followers are required to replay logs to catch up the state of the leader in the highly concurrent setting to reduce visibility gap. Third, modern databases are often built with a cluster of commodity machines connected by low configuration networks, in which the network anomalies often happen. In this case, the performance would be significantly affected because the follower node falls into the long-duration exception handling process (e.g., fetch lost logs from the leader). To this end, we build QuorumX, an efficient and stable quorum-based replication framework for IMDB under heavy OLTP workloads. QuorumX combines critical path based batching and pipeline batching to provide an adaptive log propagation scheme to obtain a stable and high performance at various settings. Further, we propose a safe and coordination-free log replay scheme to minimize the visibility gap between the leader and follower IMDBs. We further carefully design the process for the follower node in order to alleviate the influence of the unreliable network on the replication performance. Our evaluation results with the YCSB, TPC-C and a realistic micro-benchmark demonstrate that QuorumX achieves the performance close to asynchronous primary-backup replication and could always provide a stable service with data consistency and a low-level visibility gap.