Process bottlenecks identification and its root cause analysis using fusion-based clustering and knowledge graph

Drawing the strengths of data science and machine learning, process mining has recently emerged as an effective research approach for process management and its decision support. Bottleneck identification and analysis is a key problem in process mining which is considered a critical component for process improvement. While previous studies focusing on bottlenecks have been reported, visible gaps remain. Most of these studies considered bottleneck identification from local perspectives by quantitative metrics, such as machine operation and resource requirement, which can not be applied to knowledge-intensive processes. Moreover, the root cause of such bottlenecks has not been given enough attention, which limits the impact of process optimisation. This paper proposes an approach that utilises fusion-based clustering and hyperbolic neural network-based knowledge graph embedding for bottleneck identification and root cause analysis. Firstly, a fusion-based clustering is proposed to identify bottlenecks automatically from a global perspective, where the execution frequency of each stage at different periods is calculated to reveal the abnormal stage. Secondly, a process knowledge graph representing tasks, organisations, workforce and relation features as hierarchical and logical patterns is established. Finally, a hyperbolic cluster-based community detection mechanism is researched, based on the process knowledge graph embedding trained by a hyperbolic neural network, to analyse the root cause from a process perspective. Experimental studies using real-world data collected from a multidisciplinary design project revealed the merits of the proposed approach. The execution of the proposed approach is not limited to event logs; it can automatically identify bottlenecks without local quantitative metrics and analyse the causes from a process perspective.     

A tool supporting root cause analysis for synchronous retrospectives in distributed software teams

ContextRoot cause analysis (RCA) is a useful practice for software project retrospectives, and is typically carried out in synchronous collocated face-to-face meetings. Conducting RCA with distributed teams is challenging, as face-to-face meetings are infeasible. Lack of adequate real-time tool support exacerbates this problem. Furthermore, there are no empirical studies on using RCA in synchronous retrospectives of geographically distributed teams.ObjectiveThis paper presents a real-time cloud-based software tool (ARCA-tool) we developed to support RCA in distributed teams and its initial empirical evaluation. The feasibility of using RCA with distributed teams is also evaluated.MethodWe compared our tool with 35 existing RCA software tools. We conducted field studies of four distributed agile software teams at two international software product companies. The teams conducted RCA collaboratively in synchronous retrospective meetings by using the tool we developed. We collected the data using observations, interviews and questionnaires.ResultsComparison revealed that none of the existing 35 tools matched all the features of our ARCA-tool. The team members found ARCA-tool to be an essential part of their distributed retrospectives. They considered the software as efficient and very easy to learn and use. Additionally, the team members perceived RCA to be a vital part of the retrospectives. In contrast to the prior retrospective practices of the teams, the introduced RCA method was evaluated as efficient and easy to use.ConclusionRCA is a useful practice in synchronous distributed retrospectives. However, it requires software tool support for enabling real-time view and co-creation of a cause-effect diagram. ARCA-tool supports synchronous RCA, and includes support for logging problems and causes, problem prioritization, cause-effect diagramming, and logging of process improvement proposals. It enables conducting RCA in distributed retrospectives.     

Linking Bayesian networks and PLS path modeling for causal analysis

Causal knowledge based on causal analysis can advance the quality of decision-making and thereby facilitate a process of transforming strategic objectives into effective actions. Several creditable studies have emphasized the usefulness of causal analysis techniques. Partial least squares (PLS) path modeling is one of several popular causal analysis techniques. However, one difficulty often faced when we commence research is that the causal direction is unknown due to the lack of background knowledge. To solve this difficulty, this paper proposes a method that links the Bayesian network and PLS path modeling for causal analysis. An empirical study is presented to illustrate the application of the proposed method. Based on the findings of this study, conclusions and implications for management are discussed.     

Perceived causes of software project failures – An analysis of their relationships

ContextSoftware project failures are common. Even though the reasons for failures have been widely studied, the analysis of their causal relationships is lacking. This creates an illusion that the causes of project failures are unrelated.ObjectiveThe aim of this study is to conduct in-depth analysis of software project failures in four software product companies in order to understand the causes of failures and their relationships. For each failure, we want to understand which causes, so called bridge causes, interconnect different process areas, and which causes were perceived as the most promising targets for process improvement.MethodThe causes of failures were detected by conducting root cause analysis. For each cause, we classified its type, process area, and interconnectedness to other causes. We quantitatively analyzed which type, process area, and interconnectedness categories (bridge, local) were common among the causes selected as the most feasible targets for process improvement activities. Finally, we qualitatively analyzed the bridge causes in order to find common denominators for the causal relationships interconnecting the process areas.ResultsFor each failure, our method identified causal relationships diagrams including 130–185 causes each. All four cases were unique, albeit some similarities occurred. On average, 50% of the causes were bridge causes. Lack of cooperation, weak task backlog, and lack of software testing resources were common bridge causes. Bridge causes, and causes related to tasks, people, and methods were common among the causes perceived as the most feasible targets for process improvement. The causes related to the project environment were frequent, but seldom perceived as feasible targets for process improvement.ConclusionPrevention of a software project failure requires a case-specific analysis and controlling causes outside the process area where the failure surfaces. This calls for collaboration between the individuals and managers responsible for different process areas.     

Development and evaluation of a lightweight root cause analysis method (ARCA method) - Field studies at four software companies

Context

The key for effective problem prevention is detecting the causes of a problem that has occurred. Root cause analysis (RCA) is a structured investigation of the problem to identify which underlying causes need to be fixed. The RCA method consists of three steps: target problem detection, root cause detection, and corrective action innovation. Its results can help with process improvement.

Objective

This paper presents a lightweight RCA method, named the ARCA method, and its empirical evaluation. In the ARCA method, the target problem detection is based on a focus group meeting. This is in contrast to prior RCA methods, where the target problem detection is based on problem sampling, requiring heavy startup investments.

Method

The ARCA method was created with the framework of design science. We evaluated it through field studies at four medium-sized software companies using interviews and query forms to collect feedback from the case attendees. A total of five key representatives of the companies were interviewed, and 30 case participants answered the query forms. The output of the ARCA method was also evaluated by the case attendees, i.e., a total 757 target problem causes and 124 related corrective actions.

Results

The case attendees considered the ARCA method useful and easy to use, which indicates that it is beneficial for process improvement and problem prevention. In each case, 24-77 target problem root causes were processed and 13-40 corrective actions were developed. The effort of applying the method was 89 man-hours, on average.

Conclusion

The ARCA method required an acceptable level of effort and resulted in numerous high-quality corrective actions. In contrast to the current company practices, the method is an efficient method to detect new process improvement opportunities and develop new process improvement ideas. Additionally, it is easy to use.     

根因分析研究综述

作为问题发现和问题解决之间的关键问题与枢纽环节,根因分析目前的研究主要包括基于数据驱动和基于因果驱动两大类方法。鉴于数据驱动方法在缩小根因范围方面具有优势,因而目前根因研究主要聚焦在基于关联规则挖掘、基于启发式搜索、基于机器学习和基于深度学习等数据驱动方法,鲜有从因果知识的角度对根因进行分析,也尚未基于方法维度对根因进行归纳分析研究,缺乏相关研究成果。因此,对近几年根因分析的主要成果进行梳理总结,分析在不同方法维度下根因分析的区别及优势,并提出融合因果知识的根因分析方法,将非对称Shapley值与因果链图相结合以提升根因分析的准确度,最后讨论了现有的研究难点与发展趋势,提出有意义的未来研究方向。     

基于知识图谱的岭南文化热词分析与知识演化研究

随着计算机技术的发展,大量的岭南文化信息被记录下来.而海量的岭南文化信息却难以被有效地利用.文章采用知识图谱技术对岭南文化信息进行有效的语义抽取和融合,采用Citespace可视化分析软件完成了对岭南文化研究热词和趋势的研究,为岭南文化的人文历史的研究提供了技术框架和研究方案.     

Simplified Granger causality map for data-driven root cause diagnosis of process disturbances

Root cause diagnosis is an important step in process monitoring, which aims to identify the sources of process disturbances. The primary challenge is that process disturbances propagate between different operating units because of the flow of material and information. Data-driven causality analysis techniques, such as Granger causality (GC) test, have been widely adopted to construct process causal maps for root cause diagnosis. However, the generated causal map is over-complicated and difficult to interpret because of the existence of process loops and the violation of statistical assumptions. In this work, a two-step procedure is proposed to solve this problem. First, a causal map is built by adopting the conditional GC analysis, which is viewed as a graph in the next step. In this graph, each vertex corresponds to a process variable under investigation, while the weight of the edge connecting two vertices is the F-value calculated by conditional GC. This graph is then simplified by computing its maximum spanning tree. Thus, the results of the causality analysis are transformed into a directed acyclic graph, which eliminates all loops, highlights the root cause variable, and facilitates the diagnosis. The feasibility of this method is illustrated with the application to the Tennessee Eastman benchmark process. In the investigated case studies, the proposed method outperforms the conditional GC test and provides an easy way to identify the root cause of process disturbances.     

An application of causal analysis to the software modification process

The development of high quality large-scale software systems within schedule and budget constraints is a formidable software engineering challenge. The modification of these systems to incorporate new and changing capabilities poses an even greater challenge. This modification activity must be performed without adversely affecting the quality of the existing system. Unfortunately, this objective is rarely met. Software modifications often introduce undesirable side-effects, leading to reduced quality. In this paper, the software modification process for a large, evolving real-time system is analysed using causal analysis. Causal analysis is a process for achieving quality improvements via fault prevention. The fault prevention stems from a careful analysis of faults in search of their causes. This paper reports our use of causal analysis on several significant modification activities resulting in about two hundred defects. Recommendations for improved software modification and quality assurance processes based on our findings are also presented.     

基于知识图谱的设备缺陷闭环管理

本文针对传统的火力发电企业设备缺陷管理方法存在的诸多问题,如处理效率低、准确性差、历史经验及数据未得到有效利用等提出一种基于知识图谱技术的系统化解决方案。通过构建一种基于知识图谱的设备全寿命周期管理系统,可以在设备缺陷闭环管理中,有效实现设备维修辅助决策,通过设备维修历史、缺陷记录的自动分析,为运维人员提供优化的检修策略。本文首先简单介绍了整体技术方案及背景,接着对系统的整体设计及架构和功能进行了阐述,然后对系统的整体实现进行了详细说明,最后总结本文的工作,并讨论了存在的问题及未来的优化方向。希望本文能为国内火电企业建设类似系统提供一定的参考借鉴。     

基于半定量概率图模型的溯因分析方法

复杂工业系统的故障原因定位可协助操作人员快速调整设备运行参数,保障生产高效稳定地运行.铝电解过程机理复杂且外部因素干扰多,信息具有不确定性特征,难以建立精确的定量模型,而定性分析的准确度不高.为此,本文针对铝电解溯因过程的层次性、相关性、不确定性的特点,构建了一种基于半定量概率图模型的溯因分析框架,将定量和定性分析相结合,通过不确定理论对信息进行处理和描述,采用图形符号可视化知识变量间的因果关系,再基于概率图模型的推理方法实现不确定性条件下的溯因诊断,为实现铝电解异常槽况的原因分析与定位提供了理论支撑.     

融合知识图谱与熵权评价的电力设备缺陷文本检索方法

随着电力设备数量的不断增长,如何有效管理和处理其缺陷记录成为了一个重要问题。传统的人工处理方法效率低下,且难以应对文本挖掘的挑战。为解决这一问题,本文提出了一种结合知识图谱技术和熵权评价策略的电力设备缺陷文本精准检索方法。该方法首先根据缺陷规范标准构建了电力设备缺陷的知识图谱,并建立了标准缺陷路径库。在分词和关键词提取过程中,考虑到电力行业的专业特性,采用了预学习处理和大规模知识图谱数据的应用,有效解决了共指消解问题。最后,基于电力设备缺陷知识图谱,运用熵权评价方法完成了标准路径的检索和相似度的排序。通过算例分析,验证了该方法能够精准检索缺陷文本,定位标准缺陷路径库,并为规范标准的本地化提供了重要参考。这一研究对于提升电力设备运维管理的质量和效率,保证电网的安全稳定和可靠供电具有重要意义。     

面向电厂关键设备故障知识图谱构建的关系抽取方法研究

当今电厂面临着诸多挑战，包括电力设备种类繁多、设备数量庞大、故障类型众多、数据耦合关系复杂以及海量的故障信息数据等。知识图谱能够将各种信息整合、可视化呈现，并支持智能化应用，有助于人们更好地获取、管理和应用知识，从而提高效率、创造价值。运用知识图谱来分析电厂故障数据，有助于深入研究电厂设备故障情况。在构建知识图谱的过程中，关系抽取是关键步骤之一，其准确率直接影响最终知识图谱构建的质量。本文提出了一个面向电厂关键发电设备故障知识图谱构建的关系抽取工具，该工具能将故障信息中海量、异构的数据以及相关故障处理进行可视化表达，同时支持用户交互式地参与到关系抽取的过程中，通过迭代训练来优化关系抽取模型。在实验测试阶段，利用真实电厂设备故障数据进行验证，证明了该工具在显著提高关系抽取的准确率方面的有效性。因此，构建的知识图谱质量得以提升，为电厂管理人员更好地运维管理发电设备提供了重要支持，为管控电厂相关数据以及推动电厂完备建设提供有力支撑。     

基于知识图谱嵌入和补全的电梯故障预测

为及早预测电梯发生的常见故障,提高电梯设备的维保质量和效率,提出基于规则推理、知识图谱嵌入技术和知识图谱补全技术实现电梯故障预测的方法,在构建电梯故障知识图谱后,通过改进的组合模型将三元组中的实体和关系训练为连续的低维向量空间,实现三元组对于故障预测相关运算的兼容,通过组合模型实现电梯实体、关系和故障实体三元组的预测....     

Using logical decision trees to discover the cause of process delays from event logs

In real-world business processes it is often difficult to explain why some process instances take longer than usual to complete. With process mining techniques, it is possible to do an a posteriori analysis of a large number of process instances and detect the occurrence of delays, but discovering the actual cause of such delays is a different problem. For example, it may be the case that when a certain activity is performed or a certain user (or combination of users) participates in the process, the process suffers a delay. In this work, we show that it is possible to retrieve possible causes of delay based on the information recorded in an event log. The approach consists in translating the event log into a logical representation, and then applying decision tree induction to classify process instances according to duration. Besides splitting those instances into several subsets, each path in the tree yields a rule that explains why a given subset has an average duration that is higher or lower than other subsets of instances. The approach is applied in two case studies involving real-world event logs, where it succeeds in discovering meaningful causes of delay, some of which having been pointed out by domain experts.     

设备智能管理服务物联网平台系统设计

随着物联网智慧管理平台的飞速发展,设备远程智慧服务广泛应用于各行各业.重庆某制造企业有若干分厂,每个分厂生产不同的仪表与设备,各个分厂下有不同的生产线与设备,具有分厂与生产线多、地域分布较广、设备种类众多等特点.针对该企业对于设备监测困难、无法实时监控设备运行状态、不利于企业提高产能等特点,采用基于物联网的设备智能管理...     

基于小波分析的煤矿机电设备故障检测关键技术应用研究

针对煤矿关键设备中常见多发机械故障,深入研究煤矿设备机械故障振动特征识别技术及其应用。介绍了智能诊断技术中专家系统、模糊控制、神经网络控制等的特点,通过理论与技术分析,提出小波分析实现煤矿设备不同损伤类故障微弱特征识别,以及煤矿设备在线监测与故障智能诊断应用。     

故障检测率对软件可靠性影响实证分析

故障检测率是软件可靠性模型的主要参数之一,不同形式的故障检测率具有不同的作用。聚焦于故障检测率对软件可靠性的影响,提出基于信息熵与优劣距离决策算法的单可靠性模型单失效数据集多故障检测率与多可靠性增长模型多失效数据集多故障检测率2种实证分析方案,旨在全面地分析故障检测率的影响。经过实验分析,对于单一可靠性模型单一数据集,故障检测率对软件可靠性的影响主要与失效数据集相关,在不同数据集上不同故障检测率函数的性能差异较大;在多可靠性模型多数据集上,幂函数与S型故障检测率对应的软件可靠性模型的综合性能较好,指数型故障检测率对应的软件可靠性模型的综合性能较差。本文的研究对于软件可靠性建模中的模型参数选择、最优发布时间的确定等具有较强的指导作用。     

Leveraging the affordances of Youtube: The role of pedagogical knowledge and mental models of technology functions for lesson planning with technology

Web-based digital video tools enable learners to access video sources in constructive ways. To leverage these affordances teachers need to integrate their knowledge of a technology with their professional knowledge about teaching. We suggest that this is a cognitive process, which is strongly connected to a teacher’s mental model of the tool’s affordances. First we elaborate the theoretical integration of the notion of mental models and the Technological Pedagogical Content Knowledge (TPCK) framework. Then we report on a study where we investigated pedagogical knowledge in a sample of German pre-service teachers as a predictor for their mental models of YouTube and how these affect lesson plans for instructional use of this technology. We describe the active mental models of YouTube and present quantitative analyses suggesting mental models as mediators for the influence of pedagogical knowledge on participants’ lesson planning. Results are discussed with regard to theoretical and research implications.