流程挖掘研究

企业信息系统，例如企业资源计划（ERP），客户关系管理（CRM），供应链管理（SCM），Web服务等等都支持企业流程．但是它们都不能跟踪并改进流程。流程挖掘的目的就是从日志数据中抽取信息构建商业流程执行时的模型，从而能跟踪改进流程。本文简单介绍了流程挖掘涉及到的一系列方面，包括流程挖掘的定义、框架、研究难点。最后从九个方面对四种流程挖掘工具EMiT，Little Thumnb，InWoLvE，Process Miner进行了比较。     

Designing software for operational decision support through coloured Petri nets

Operational support provides, during the execution of a business process, replies to questions such as ‘how do I end the execution of the process in the cheapest way?’ and ‘is my execution compliant with some expected behaviour?’ These questions may be asked several times during a single execution and, to answer them, dedicated software components (the so-called operational support providers) need to be invoked. Therefore, an infrastructure is needed to handle multiple providers, maintain data between queries about the same execution and discard information when it is no longer needed. In this paper, we use coloured Petri nets (CPNs) to model and analyse software implementing such an infrastructure. This analysis is needed to clarify the requirements before implementation and to guarantee that the resulting software is correct. To this aim, we present techniques to represent and analyse state spaces with 250 million states on a normal PC. We show how the specified requirements have been implemented as a plug-in of the process mining tool ProM and how the operational support in ProM can be used in combination with an existing operational support provider.     

数据挖掘在质量管理体系中的应用

数据挖掘技术提供了对海量质量数据的强大分析处理功能。本文分析了质量管理体系中质量数据的特点，提出了质量数据挖掘系统的系统模型和分层体系结构；并介绍了质量数据挖掘的主要模型及方法，包括质量预测模型、分类模型和关联模型。     

基于Web日志挖掘中的数据预处理

Web日志挖掘是Web数据挖掘研究领域中一个最重要的应用方面,数据预处理是Web日志挖掘中的关键技术之一。本文概述了Web日志挖掘的概念和基本步骤,重点分析了数据预处理中各个环节的主要任务和实现技术,通过实例详细说明了对Web日志实施数据预处理的过程。     

数据挖掘技术及其在生产过程质量控制中的应用

生产过程中质量控制对提高产品质量具有重要的意义。本文对数据挖掘技术的概念及方法进行了简单的介绍,以挤塑产品生产过程中温度的多元回归预测挖掘为例介绍了数据挖掘技术在生产过程质量控制的应用,表明这种方法在实际应用中的正确性和有效性。     

Inference of the Russian drug community from one of the largest social networks in the Russian Federation

The criminal nature of narcotics complicates the direct assessment of a drug community, while having a good understanding of the type of people drawn or currently using drugs is vital for finding effective intervening strategies. Especially for the Russian Federation this is of immediate concern given the dramatic increase it has seen in drug abuse since the fall of the Soviet Union in the early nineties. Using unique data from the Russian social network ‘LiveJournal’ with over 39 million registered users worldwide, we were able for the first time to identify the on-line drug community by context sensitive text mining of the users’ blogs using a dictionary of known drug-related official and ‘slang’ terminology. By comparing the interests of the users that most actively spread information on narcotics over the network with the interests of the individuals outside the on-line drug community, we found that the ‘average’ drug user in the Russian Federation is generally mostly interested in topics such as Russian rock, non-traditional medicine, UFOs, Buddhism, yoga and the occult. We identify three distinct scale-free sub-networks of users which can be uniquely classified as being either ‘infectious’, ‘susceptible’ or ‘immune’.     

Price volatility and the political economy of resource-rich nations

This paper attempts to understand how price volatility affects the political transition of a resource-rich nation. Two states reflect price volatility: ‘high prices’ and ‘low prices’. We argue that whether or not political transition (i.e., a switch from one regime to another) will take place in a particular state depends critically on the kind of goods a country produces. If the main economic activity in a country is the extraction of “point-source” resources such as oil that demands capital-intensive production, the opportunity cost of switching the existing regime does not alter if the price of the resource changes but the benefit becomes more lucrative. Therefore, the incumbent group is most vulnerable during ‘high prices’. If the main economic activity of the nations is the production of “diffused resources” such as coffee that requires labor, prices do affect the opportunity cost. Nations concentrating in these commodities face acute political crisis during downturns.     

A node-centric analysis of metagraphs and its applications to workflow models

Business processes and its related workflow systems have received greater interest in practice and research in the last decade. Many analytical methodologies for analysis and design of workflow systems emerged. A recent formal approach to study workflows using a graph-theoretic method called ‘metagraphs’ has demonstrated effectiveness for analysing connectivity and interactions of information and resources between workflow components. However, past works in analysis of metagraph are element-based. Since nodes in metagraphs represent either the input or output of an activity it is natural to process information contained in a node taken as a unit. This paper takes a node-centric view on metagraphs that is a major departure from the element-based approach today. The change in focus requires provisioning an analysis framework under the node-centric views. New basic constructs including, but not limited to, concepts such as: ‘surplus sets’, ‘deficit sets’, ‘state of a path’, and ‘node-centric view of adjacency matrices’ are introduced. The approach produces computational feasible systems for elements that are over supplied and/or under supplied from a source node to a target node of any path of the metagraph. Such information could be valuable for designing workflow systems. Also, the node-centric approach is shown to be an extension of the basic constructs of element-view metagraphs and is a complementary method for validating information requirements of workflow modelling. Illustrative examples are given.     

Statistical methods for some simple disclosure limitation rules

To guard the confidentiality of information provided by respondents, statistical offices apply disclosure limitation techniques. An often applied technique is to ensure that there are no categories for which the population frequency is presumed to be small (‘rare’ categories). This is attained by recoding, top-coding or setting values to ‘unknown’. Since population frequencies are usually not available, the decision that a category is rare is often based on intuitive considerations. This is a time consuming process, involving many decisions of the disclosure limitation practitioners. In this paper it will be explored to what extent the sample frequencies can be used to make such decisions. This leads to a procedure which enables to automatically scan a data set for rare category combinations, whereby ‘rare’ is defined by the disclosure limitation policy of the statistical office.     

Belgian economic policy uncertainty index: Improvement through text mining

Recently, the literature has measured economic policy uncertainty using news references, resulting in the frequently-mentioned ‘Economic Policy Uncertainty index’ (EPU). In the original setup, a news article is assumed to address policy uncertainty if it contains certain predefined keywords. We argue that the original setup is prone to measurement error, and propose an alternative methodology using text mining techniques. We compare the original method to modality annotation and support vector machines (SVM) classification in order to create an EPU index for Belgium. Validation on an out-of-sample test set speaks in favour of using an SVM classification model for constructing a news-based policy uncertainty indicator. The indicators are then used to forecast 10 macroeconomic and financial variables. The original method of measuring EPU does not have predictive power for any of these 10 variables. The SVM indicator has a higher predictive power and, notably, changes in the level of policy uncertainty during tumultuous periods of high uncertainty and risk can predict changes in the sovereign bond yield and spread, the credit default swap spread, and consumer confidence.     

数据挖掘应用性的研究

随着信息技术的高速发展,人们积累的数据量急剧增长,如何从海量的数据中提取有用的知识成为当务之急。数据挖掘就是为顺应这种需要应运而生发展起来的数据处理技术。数据挖掘(Data Mining)是从存放在数据库、数据仓库或其他信息库中的大量数据中挖掘有趣知识的过程。数据挖掘算法和可视化工具适用于挖掘数据中的重要模式,并且提供有价值的预测,它是当代人工智能、统计学等技术在数据库领域中的应用。     

Representative account or greenwashing? Voluntary sustainability reports in Australia's mining/metals and financial services industries

Why are more and more companies voluntarily issuing costly, potentially exposing standalone sustainability reports on environmentally and socially problematic issues? Using legitimacy theory, this study analyses ways companies seek to strategically enhance legitimacy by leaning towards either ‘representative’ reporting of both favourable and unfavourable information especially in an industry's highest impact domains, or ‘greenwashing’ (including whitewashing non-environmental issues), which downplays unfavourables and high-impact domains and highlights favourable but less relevant points. Content analysis compared Global Reporting Initiatives (GRI) reports from 2011 to 2019 by Australian financial services companies (107 reports) and mining and metals companies (122). Specifically, to critique reporting quality in fine grain, it disaggregated results into levels (from omission to full quantitative treatment) of disclosure of good/bad/neutral news indicators and violation-related/non-violation-related ones and identified highest impact domains. Neither industry's reporting was very representative. Relatively though, mining and metals leant towards representation: fuller disclosure on environmental aspects (its highest impact domain), including unfavourables: bad and violation-related indicators. Financial services companies only led in disclosing neutral social indicators, not bad or violation-related ones, so leant towards greenwashing. Results also suggest that after the GRI clarifying materiality principle in 2016, financial services disclosure quality dropped further by de-emphasising environmental without lifting social disclosures, while mining and metals' stayed unchanged. The results confirm and better specify widely indicated reporting weaknesses, contributing to content analysis methodology and legitimacy theory. This arms guideline setters, investors and other stakeholders to better evaluate/design reports and might encourage firms to voluntarily improve disclosures.     

Australia’s mining productivity decline: implications for MFP measurement

This paper investigates the mining sector’s poor productivity performance as measured by the growth accounting formula for multifactor productivity (MFP) index during the recent mining boom in Australia. We provide an alternative measure of productivity growth by estimating a translog variable cost function, with parameters that separate productivity growth due to technical change from that due to the effects of returns to scale, capacity utilisation and natural resource inputs. The results show that the average MFP growth in Australian mining based on the dual cost-function measure of technical change is 2 % over the sample period 1974–1975 to 2007–2008, rather than ?0.2 % from the published index. The difference arises because declining natural resource inputs, the effects of capacity utilisation and returns to scale have all reduced the ‘true’ MFP growth.     

厚煤层采煤法回采工艺分析

一般而言,厚度在3.5m以上的煤层为厚煤层。按照煤层的赋存状况,厚煤层的开采方法主要有分层开采和一次采全高。本文主要分析了再生顶板及假顶、厚煤层一次采全高等回采工艺。     

WALKING DOWN WALL STREET WITH A TABLET: A SURVEY OF STOCK MARKET PREDICTIONS USING THE WEB

‘A blindfolded chimpanzee throwing darts at The Wall Street Journal could select a portfolio that would do as well as the (stock market) experts’ [Malkiel (2003) The efficient market hypothesis and its critics. Journal of Economic Perspectives 17(1): 59–82)]. However, what if this chimpanzee could browse the Internet before throwing any darts? In this paper, we ask whether online news has any influence on the financial market, and we also investigate how much influence it has. We explore the burgeoning literature on the predictability of financial movements using online information and report its mixed findings. In addition, we collate the efforts of various disciplines, including economics, text mining, sentiment analysis and machine learning, and we offer suggestions for future research.     

论采矿引起的地质危害及治理技术的应用

多数地质灾害的发生是由于不科学的资源开采引发的,而现有的技术手段完全可以控制由于采矿引发的地质灾害。文章分析了矿山开采对地质、水环境和生态地质环境的影响,并提出了相应的治理措施。     

Success factors for continuous supply chain process improvement: evidence from Japanese manufacturers

This study explores how firms can realise ‘continuous’ supply chain process improvement. Specifically, this study proposes the success factors of routinised activities (also called ‘repeated activities’) in the supply chain process improvement. Eight Japanese manufacturers are selected for the case studies. These firms are selected based on their supply chain process operation capabilities. From the case studies, we find that the planning of supply chain process improvement depends on the existing stage of the firm’s Supply Chain Management reform. In addition, even firms with high scores for supply chain process operation capabilities do not have supply chain performance systems. Furthermore, quite surprisingly, many Japanese manufacturers tend to improve their supply chain processes in the absence of such systems.     

Influence of freight transport purchasing processes on logistical variables related to CO2 emissions: a case study in Sweden

Emissions from freight transport stem from logistical variables such as vehicle utilisation, fuel efficiency, and distance. The purpose is to determine how shippers’ freight transport purchasing processes influence logistical variables. A multiple case study of freight transport purchasing processes was conducted, based on interviews with transport purchasers and providers. Three causes of influence of shippers’ purchasing processes on logistical variables were found: specific requirements, network structure of transport providers, and scope of contract. Specifications by purchasers, especially time requirements, influence several logistical variables (‘mode used’, ‘length of haul’, ‘load factor’, ‘empty running’, and ‘fuel efficiency’). This paper clarifies the implications of transport purchasing on CO₂ emissions in terms of logistical variables, which are understood in transportation research and practice. It describes the effects of shippers’ requirements on transport providers’ execution of transport. The results provide a foundation for shippers to discuss their influence on logistical variables with transport providers.     

On the way to Ithaka[1]: Commemorating the 50th Anniversary of the Publication of Karl E. Weick’s The Social Psychology of Organizing

Karl E. Weick’s The Social Psychology of Organizing has been one of the most influential books in organization studies, providing the theoretical underpinnings of several research programs. Importantly, the book is widely credited with initiating the process turn in the field, leading to the ‘gerundizing’ of management and organization studies: the persistent effort to understand organizational phenomena as ongoing accomplishments. The emphasis of the book on organizing (rather than on organizations) and its links with sensemaking have made it the most influential treatise on organizational epistemology. In this introduction, we review Weick’s magnum opus, underline and assess its key themes, and suggest ways in which several of them may be taken forward.