On the refactoring of activity labels in business process models

Large corporations increasingly utilize business process models for documenting and redesigning their operations. The extent of such modeling initiatives with several hundred models and dozens of often hardly trained modelers calls for automated quality assurance. While formal properties of control flow can easily be checked by existing tools, there is a notable gap for checking the quality of the textual content of models, in particular, its activity labels. In this paper, we address the problem of activity label quality in business process models. We designed a technique for the recognition of labeling styles, and the automatic refactoring of labels with quality issues. More specifically, we developed a parsing algorithm that is able to deal with the shortness of activity labels, which integrates natural language tools like WordNet and the Stanford Parser. Using three business process model collections from practice with differing labeling style distributions, we demonstrate the applicability of our technique. In comparison to a straightforward application of standard natural language tools, our technique provides much more stable results. As an outcome, the technique shifts the boundary of process model quality issues that can be checked automatically from syntactic to semantic aspects.     

How novices design business processes

Process modeling is an important design practice in organizational improvement projects. In this paper, we examine the design of business process diagrams in contexts where novice analysts only have basic design tools such as paper and pencils available, and little to no understanding of formalized modeling approaches. Based on a quasi-experimental study with 89 BPM students, we identify five distinct process design archetypes ranging from textual to hybrid and graphical representation forms. We examine the quality of the designs and identify which representation formats enable an analyst to articulate business rules, states, events, activities, temporal and geospatial information in a process model. We found that the quality of the process designs decreases with the increased use of graphics and that hybrid designs featuring appropriate text labels and abstract graphical forms appear well-suited to describe business processes. We further examine how process design preferences predict formalized process modeling ability. Our research has implications for practical process design work in industry as well as for academic curricula on process design.     

Repeated labeling using multiple noisy labelers

This paper addresses the repeated acquisition of labels for data items when the labeling is imperfect. We examine the improvement (or lack thereof) in data quality via repeated labeling, and focus especially on the improvement of training labels for supervised induction of predictive models. With the outsourcing of small tasks becoming easier, for example via Amazon’s Mechanical Turk, it often is possible to obtain less-than-expert labeling at low cost. With low-cost labeling, preparing the unlabeled part of the data can become considerably more expensive than labeling. We present repeated-labeling strategies of increasing complexity, and show several main results. (i) Repeated-labeling can improve label quality and model quality, but not always. (ii) When labels are noisy, repeated labeling can be preferable to single labeling even in the traditional setting where labels are not particularly cheap. (iii) As soon as the cost of processing the unlabeled data is not free, even the simple strategy of labeling everything multiple times can give considerable advantage. (iv) Repeatedly labeling a carefully chosen set of points is generally preferable, and we present a set of robust techniques that combine different notions of uncertainty to select data points for which quality should be improved. The bottom line: the results show clearly that when labeling is not perfect, selective acquisition of multiple labels is a strategy that data miners should have in their repertoire; for certain label-quality/cost regimes, the benefit is substantial.     

Towards automatic bounding box annotations from weakly labeled images

In this work we discuss the problem of automatically determining bounding box annotations for objects in images whereas we only assume weak labeling in the form of global image labels. We therefore are only given a set of positive images all containing at least one instance of a desired object and a negative set of images which represent background. Our goal is then to determine the locations of the object instances within the positive images by bounding boxes. We also describe and analyze a method for automatic bounding box annotation which consists of two major steps. First, we apply a statistical model for determining visual features which are likely to be indicative for the respective object class. Based on these feature models we infer preliminary estimations for bounding boxes. Second, we use a CCCP training algorithm for latent structured SVM in order to improve the initial estimations by using them as initializations for latent variables modeling the optimal bounding box positions. We evaluate our approach on three publicly available datasets.     

A systematic literature review of studies on business process modeling quality

ContextBusiness process modeling is an essential part of understanding and redesigning the activities that a typical enterprise uses to achieve its business goals. The quality of a business process model has a significant impact on the development of any enterprise and IT support for that process.ObjectiveSince the insights on what constitutes modeling quality are constantly evolving, it is unclear whether research on business process modeling quality already covers all major aspects of modeling quality. Therefore, the objective of this research is to determine the state of the art on business process modeling quality: What aspects of process modeling quality have been addressed until now and which gaps remain to be covered?MethodWe performed a systematic literature review of peer reviewed articles as published between 2000 and August 2013 on business process modeling quality. To analyze the contributions of the papers we use the Formal Concept Analysis technique.ResultsWe found 72 studies addressing quality aspects of business process models. These studies were classified into different dimensions: addressed model quality type, research goal, research method, and type of research result. Our findings suggest that there is no generally accepted framework of model quality types. Most research focuses on empirical and pragmatic quality aspects, specifically with respect to improving the understandability or readability of models. Among the various research methods, experimentation is the most popular one. The results from published research most often take the form of intangible knowledge.ConclusionWe believe there is a lack of an encompassing and generally accepted definition of business process modeling quality. This evidences the need for the development of a broader quality framework capable of dealing with the different aspects of business process modeling quality. Different dimensions of business process quality and of the process of modeling still require further research.     

Label noise correction and application in crowdsourcing

The important task of correcting label noise is addressed infrequently in literature. The difficulty of developing a robust label correction algorithm leads to this silence concerning label correction. To break the silence, we propose two algorithms to correct label noise. One utilizes self-training to re-label noise, called Self-Training Correction (STC). Another is a clustering-based method, which groups instances together to infer their ground-truth labels, called Cluster-based Correction (CC). We also adapt an algorithm from previous work, a consensus-based method called Polishing that consults with an ensemble of classifiers to change the values of attributes and labels. We simplify Polishing such that it only alters labels of instances, and call it Polishing Labels (PL). We experimentally compare our novel methods with Polishing Labels by examining their improvements on the label qualities, model qualities, and AUC metrics of binary and multi-class data sets under different noise levels. Our experimental results demonstrate that CC significantly improves label qualities, model qualities, and AUC metrics consistently. We further investigate how these three noise correction algorithms improve the data quality, in terms of label accuracy, in the context of image labeling in crowdsourcing. First, we look at three consensus methods for inferring a ground-truth label from the multiple noisy labels obtained from crowdsourcing, i.e., Majority Voting (MV), Dawid Skene (DS), and KOS. We then apply the three noise correction methods to correct labels inferred by these consensus methods. Our experimental results show that the noise correction methods improve the labeling quality significantly. As an overall result of our experiments, we conclude that CC performs the best. Our research has illustrated the viability of implementing noise correction as another line of defense against labeling error, especially in a crowdsourcing setting. Furthermore, it presents the feasibility of the automation of an otherwise manual process of analyzing a data set, and correcting and cleaning the instances, an expensive and time-consuming task.     

面向电力业务对话系统的意图识别数据集

针对供电营业厅客服机器人的智能对话系统，构建了一个较大规模的电力业务用户意图数据集。该数据集包括了9 577条用户问询语句及其标注类别。首先对从供电营业厅采集到的真实语音数据进行清洗、处理和过滤。为了使数据能够驱动意图分类相关的深度学习模型的研究，专业人员根据电力业务背景知识对数据进行高质量的标注和扩充。标注中根据电力业务定义了35种业务类别标签。为了测试该数据集的实用性和有效性，采用了多个意图分类经典模型进行实验，并将得到的意图分类模型嵌入到对话系统中。经典的文本分类模型循环卷积神经网络（Text-RCNN）在该数据集上可得到87.1%的准确率。实验结果表明该数据集可以有效驱动电力业务相关对话系统的研究，提升用户的满意度。     

Using Hidden Markov Models for the accurate linguistic analysis of process model activity labels

Many process model analysis techniques rely on the accurate analysis of the natural language contents captured in the models’ activity labels. Since these labels are typically short and diverse in terms of their grammatical style, standard natural language processing tools are not suitable to analyze them. While a dedicated technique for the analysis of process model activity labels was proposed in the past, it suffers from considerable limitations. First of all, its performance varies greatly among data sets with different characteristics and it cannot handle uncommon grammatical styles. What is more, adapting the technique requires in-depth domain knowledge. We use this paper to propose a machine learning-based technique for activity label analysis that overcomes the issues associated with this rule-based state of the art. Our technique conceptualizes activity label analysis as a tagging task based on a Hidden Markov Model. By doing so, the analysis of activity labels no longer requires the manual specification of rules. An evaluation using a collection of 15,000 activity labels demonstrates that our machine learning-based technique outperforms the state of the art in all aspects.     

Discriminative Random Fields

In this research we address the problem of classification and labeling of regions given a single static natural image. Natural images exhibit strong spatial dependencies, and modeling these dependencies in a principled manner is crucial to achieve good classification accuracy. In this work, we present Discriminative Random Fields (DRFs) to model spatial interactions in images in a discriminative framework based on the concept of Conditional Random Fields proposed by lafferty et al.(2001). The DRFs classify image regions by incorporating neighborhood spatial interactions in the labels as well as the observed data. The DRF framework offers several advantages over the conventional Markov Random Field (MRF) framework. First, the DRFs allow to relax the strong assumption of conditional independence of the observed data generally used in the MRF framework for tractability. This assumption is too restrictive for a large number of applications in computer vision. Second, the DRFs derive their classification power by exploiting the probabilistic discriminative models instead of the generative models used for modeling observations in the MRF framework. Third, the interaction in labels in DRFs is based on the idea of pairwise discrimination of the observed data making it data-adaptive instead of being fixed a priori as in MRFs. Finally, all the parameters in the DRF model are estimated simultaneously from the training data unlike the MRF framework where the likelihood parameters are usually learned separately from the field parameters. We present preliminary experiments with man-made structure detection and binary image restoration tasks, and compare the DRF results with the MRF results. Sanjiv Kumar is currently with Google Research, Pittsburgh, PA, USA. His contact email is: sanjivk@google.com.     

面向众包数据的特征扩维标签质量提高方法

众包是一个新兴的收集数据集标签的方法。虽然它经济实惠，但面临着数据标签质量无法保证的问题。尤其是当客观原因存在使得众包工作者工作质量较差时，所得的标签会更加不可靠。因此提出一个名为基于特征扩维提高众包质量的方法(FA-method)，其基本思想是，首先由专家标注少部分标签，再利用众包者标注的数据集训练模型，对专家集进行预测，所得结果作为专家数据集新的特征，并利用扩维后的专家集训练模型进行预测，计算每个实例为噪声的可能性以及噪声数量上限来过滤出潜在含噪声标签的数据集，类似地，对过滤后的高质量集再次使用扩维的方法进一步校正噪声。在8个UCI数据集上进行验证的结果表明，和现有的结合噪声识别和校正的众包标签方法相比，所提方法能够在重复标签数量较少或标注质量较低时均取得很好的效果。     

网络流量预测方法

分析网络流量的行为特性并建立模型进行预测,对于网络管理以及安全预警具有重要意义。基于此,针对网络异常处理滞后、网络服务质量差等问题,研究多种经典流量预测方法,从流量特性、建模复杂性、预测精度及应用场景等多角度进行分析比较。实验结果证明,预测模型与具体场景密切相关,实际操作时需根据流量特性及预测目标选择合适的模型。     

Factors affecting accuracy in the quality control checking of fresh produce labels: A situational and laboratory‐based exploration

Manufacturing industries often rely on quality control staff to ensure mistakes are detected before products are shipped to customers. Undetected errors can result in large financial and environmental costs to packaging companies and supermarkets but the contributors to such error are underexplored. The research reported in this paper investigated human error in the quality control checking of information displayed on the labels which accompany packaged fresh produce. Initial work sought to understand the demands of label‐checking in the packhouse environment, through interviews with key quality control staff, in situ observations, and the study of historical error data held by a fresh produce packaging company. This study highlighted the dynamic and cognitively challenging environment in which label‐checking occurred, while the historical error data indicated both the scale of the packhouse's work and the infrequency of error occurring. In a separate strand of laboratory‐based research, experienced and novice label‐checkers were presented with a simulated label‐checking task and a battery of computerized and pen‐and‐paper tests. These tasks were administered to determine whether cognitive abilities could predict label‐checking accuracy in a controlled laboratory environment. Stronger abilities in two cognitive processes (information processing speed and inhibition) predicted greater overall accuracy and higher detection of labeling errors. In identifying potential contributors to human error in the quality control checking of product labels both in situ and in the laboratory, the results are relevant to manufacturing, wherever information is printed on labels, especially when labeling processes depend upon human data entry and human quality control checking.     

Activity sequence modelling and dynamic clustering for personalized e-learning

Monitoring and interpreting sequential learner activities has the potential to improve adaptivity and personalization within educational environments. We present an approach based on the modeling of learners?? problem solving activity sequences, and on the use of the models in targeted, and ultimately automated clustering, resulting in the discovery of new, semantically meaningful information about the learners. The approach is applicable at different levels: to detect pre-defined, well-established problem solving styles, to identify problem solving styles by analyzing learner behaviour along known learning dimensions, and to semi-automatically discover learning dimensions and concrete problem solving patterns. This article describes the approach itself, demonstrates the feasibility of applying it on real-world data, and discusses aspects of the approach that can be adjusted for different learning contexts. Finally, we address the incorporation of the proposed approach in the adaptation cycle, from data acquisition to adaptive system interventions in the interaction process.     

Adaptive modeling of the drying of baker's yeast in a batch fluidized bed

This study investigates the drying of baker's yeast in a fluidized-bed dryer. Mathematical modeling of the process was performed, incorporating the important process and quality parameters of the system. Artificial neural network (ANN) and adaptive neural network-based fuzzy inference system (ANFIS) structures were used to create process and quality models. Due to uncertainty regarding the process parameters, various different ANN structures were built, and the ANN with the optimum performance results for the proposed models was selected. This study also presents an ANFIS modeling approach with adaptive structure. ANN quality modeling was performed using process output parameters, and the quality loss incurred from drying the product was determined. These proposed models are easy to apply and do not impose any additional burden on the process (or the employees). The database used in this work was gathered from large quantities of industrial data (about 570 batches) obtained under various working conditions at random times over one year.     

Two faces of active learning

An active learner has a collection of data points, each with a label that is initially hidden but can be obtained at some cost. Without spending too much, it wishes to find a classifier that will accurately map points to labels. There are two common intuitions about how this learning process should be organized: (i) by choosing query points that shrink the space of candidate classifiers as rapidly as possible; and (ii) by exploiting natural clusters in the (unlabeled) data set. Recent research has yielded learning algorithms for both paradigms that are efficient, work with generic hypothesis classes, and have rigorously characterized labeling requirements. Here we survey these advances by focusing on two representative algorithms and discussing their mathematical properties and empirical performance.     

面向CIPS的企业集成化建模技术研究

CIPS是流程性企业实现企业信息化,实现企业各系统的信息集成和过程集成,从而 提高企业市场竞争力的综合自动化系统工程．CIPS的本质是系统集成和技术融合．为了在流 程性企业实现这一目标,急需要一套先进的方法论和支持工具来支持和指导CIPS的规划和实 施．其中,集成化建模技术为缩小CIPS理论研究与CIPS工程实施之间的差距提供了机会,基 于集成化建模理论的建模方法、建模工具和分析工具也能够有效地帮助企业建模理论转化为 可操作的企业模型．本文在分析了CIPS的应用特点及系统构成的基础上对面向CIPS的企业集 成化建模技术发展趋势及其关键技术做了概要性的描述,并对其中的两项较关键的技术进行 了探讨,提出了面向CIPS的企业集成化建模体系结构及面向CIPS的企业集成化建模与仿真过 程框架．面向CIPS的企业集成化建模技术研究为CIPS的理论研究与系统应用提供有利的支持 ．     

Trans-situated use of integrated information systems

Practice-based perspectives have established the situated nature of how technology is appropriated, enacted, and improvised in organisations. Empirical studies demonstrate how the same technology produces different results in different contexts of use. However, practice-based research has, to date, less to offer in terms of accounting for the relationship between instances of situated use (i.e., work practices) that are separated in space and/or time. The term trans-situated use is intended to highlight this blind spot. We focus on one type of relationship, viz., significant degrees of similarities between technologically mediated, geographically dispersed work practices. This degree of similarity is achieved through a process of commensurability consisting of (i) standardisation (addressing interdependencies between multiple instances of the ‘same’ work practice at geographically dispersed sites); and (ii) heterogeneity (addressing the entanglement of one work practice with apparently unrelated work practices and modules). Empirically, we report on a longitudinal, interpretative case study (1998–2004) of a company strategically targeting an integrated information system as a principal vehicle to establish similar services globally.     

Exploring cognitive style and task-specific preferences for process representations

Process models describe someone’s understanding of processes. Processes can be described using unstructured, semi-formal or diagrammatic representation forms. These representations are used in a variety of task settings, ranging from understanding processes to executing or improving processes, with the implicit assumption that the chosen representation form will be appropriate for all task settings. We explore the validity of this assumption by examining empirically the preference for different process representation forms depending on the task setting and cognitive style of the user. Based on data collected from 120 business school students, we show that preferences for process representation formats vary dependent on application purpose and cognitive styles of the participants. However, users consistently prefer diagrams over other representation formats. Our research informs a broader research agenda on task-specific applications of process modeling. We offer several recommendations for further research in this area.     

The effects of development process modeling and task uncertainty on development quality performance

We examine the impact of development process modeling on outcomes in software development projects, limiting our attention to process and product quality. Modeling the software development process requires a careful determination of tasks and their logical relationships. Essentially, the modeling is undertaken to establish a management framework of the project. We define and interrelate development process modeling, task uncertainty, and development outcomes, as assessed by product and process quality. A survey-based research design was used to collect data to prove our model. The results suggest that development process modeling is positively related to both product and process quality, while task uncertainty is negatively related to them. Development process modeling reduces the negative impact of task uncertainty on quality-oriented development outcomes. Development projects operating with high levels of task uncertainty should consider defining development process models that provide a framework for management of the project by establishing tasks and their logical interrelationships. Such a model should promote shared understanding of the work process among development constituents and enhance resource utilization efficiency.