Desiderata for Languages to be Used in the Defnition of Reference Business Processes

In many modern enterprises, explicit business process de nitions facilitate the pursuit of business goals in such ways as best practice reuse, process analysis, processe ciency improvement, and automation. Most real-world business processes are large and complex. Successfully capturing, analysing, and automating these processes requires process de nition languages that capture a variety of process aspects with a wealth of details. Mostcurrent process modelling languages, such as Business Process Modelling Notation (BPMN),focus on structural control ows among activities while providing inadequate support for other process de nition needs. In this paper, we rst illustrate these inadequacies through our experiences with a collection of real-world reference business processes from the Aus-tralian lending industry. We observe that the most signi cant inadequacies include lack of resource management, exception handling, process variation, and data ow integration.These identi ed shortcomings led us to consider the Little-JIL language as a vehicle forde ning business processes. Little-JIL addresses the afore-mentioned inadequacies with a number of innovative features. Our investigation concludes that these innovative features are e ective in addressing a number of key reference business process de nition needs.     

一种面向重构的工作流活动状态语义描述方法

工作流活动行为的精确描述是实现工作流过程控制的基础。基于状态的工作流活动行为描述方法在当前的工作流管理系统中被普遍采用。然而,非形式化的活动状态描述缺乏精确的语义并且不易扩充,导致系统适应性较差。针对业务过程管理需求的多样性和动态性,提出了一种支持快速重构的工作流活动状态语义的形式化描述方法。在分析工作流活动与工作项的状态关联基础上,定义了活动状态配置、活动状态配置全集等用于活动状态语义描述的基本概念。通过把活动状态映射为具有相同特征的活动状态配置的集合,建立了形式化的活动状态语义,研究了在此语义下活动状态的重构机制和基本实现步骤,并通过实例验证了该机制的有效性。应用本方法能够提高工作流管理系统适应企业业务过程管理需求快速变化的能力。     

我国甲烷氯化物产品成本核算规程的探讨

从核算规程修改的原理、原则及内容等方面对甲烷氯化物产品成本核算规程的修改、规范和完善情况进行了介绍。     

The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the minority samples and generated high total accuracy meanwhile. The proposed approach makes CBR useful in imbalanced forecasting.     

Distinguishing the adoption of business intelligence systems from their implementation: the role of managers’ personality profiles

For decades, the marketing guidelines of enterprise software providers have focused on those managers who are likely to be more innovative in adopting new information systems. The current study argues that this approach demands improvements for two reasons: (1) this tactic may be biased, since past studies have only examined the single trait of innovativeness and its impact on an individual adoption intention and (2) the organisational implementation intention might be more important than the individual adoption intention, but the former has been largely ignored in the existing literature. Based on the case of business intelligence (BI) systems and data from 62 senior managers, this study is a pioneer in that it empirically reveals that managers’ individual adoption intention is distinct from their organisational implementation intention. Further, while managers’ innovativeness may be a significant determinant of their individual adoption intention towards BI systems, the issue of whether managers actually implement BI systems in their organisations is dominated by their involvement characteristics. Fruitful suggestions are proposed for practitioners and scholars.     

CRUDi framework proposal: financial industry application

The alignment of information systems with the business goals of an organisation, although a topic of great importance, is not always properly valued or taken into consideration. In general, managers have different opinions to chief information officers (CIOs) in relation to IS, especially with regard to their importance and value to the business and also in terms of investment needs. Here, we discuss and study new approaches to methods and tools for assessing the relative importance of each information system to business, focusing on the financial sector including banks and insurance companies. We suggest the introduction of new key indicators for better decision support and to identify investment priorities, and present results regarding the relative importance of each process to support the business strategy. The primary goal for the inherent research project is to analyse the main problems and difficulties encountered by IS and IT managers, featuring different players and how they relate. The main contributions of this work are the CRUDi framework as a tool to improve alignment between business and IS strategies and the CRUDi survey and its results qualifying the financial sector's opinion regarding the relative importance of processes and investments.     

浅析企业管理创新

在企业中,要使每个员工都有施展和创新的机会。每个企业都要有自己的思想和具备自身特色的经营模式,要随着时间的推移不断运用新的创新理念,从而使企业在不断的变化中立于不败之地,在创新中不断发展。     

智慧社保服务体系建设思考与实践

建立智慧型社会保障服务体系,是政府惠民工程的重要组成部分之一。以精确的人员基本信息库建设、经办业务下延、银行自助服务、网上自主经办等方面为依托,充分利用信息化手段打造一体化的社保经办体系,将业务经办突破时间、空间限制,构建完整的智慧社保服务体系。经信息系统上线近二年的实时运行,证明运用现代信息技术建立多层次、多渠道、差异化的服务体系,极大地提高了服务效率,产生了良好的社会效益。     

基于完井业务流程的加权序列模式挖掘算法研究

针对完井移动平台Web访问模式中用户对整体上符合完井业务流程习惯的序列模式更加感兴趣的特点,提出一种基于完井业务流程的加权序列模式挖掘算法.通过对完井业务流程模型和完井Web访问日志作分析,确定完井业务依赖强度计算模型并作为序列的权重,同时在加权挖掘的过程中采用k-最小加权支持数技术,对PrefixSpan算法进行改进.该算法在加权挖掘的过程中可以舍弃一大批不满足业务流程的访问序列,有效地避免候选组合爆炸的发生.实验表明该算法能快速地挖掘出有意义的完井访问序列模式.     

一种基于区分服务的嵌套队列调度算法

现有队列调度算法只能满足某一种特定类型业务流量的服务质量(QoS)需求,无法广泛支持多种类型业务流量。为此,提出一种基于区分服务的嵌套队列调度算法。将现有队列调度算法通过嵌套模型进行组合,并根据该嵌套模型进行队列调度,从而为多种类型业务流量提供均衡的QoS保障。仿真结果表明,该算法能够满足不同类型业务流量的QoS需求,其各项性能指标所达效果与最优效果的差距较小,并且在多种类型业务流量的支持方面比现有队列调度算法表现更好。