An Improved Security Requirement for Data Perturbation with Implications for E‐Commerce

With the rapid increase in the ability to store and analyze large amounts of data, organizations are gathering extensive data regarding their customers, vendors, and other entities. There has been a concurrent increase in the demand for preserving the privacy of confidential data that may be collected. The rapid growth of e‐commerce has also increased calls for maintaining privacy and confidentiality of data. For numerical data, data perturbation methods offer an easy yet effective solution to the dilemma of providing access to legitimate users while protecting the data from snoopers (legitimate users who perform illegitimate analysis). In this study, we define a new security requirement that achieves the objective of providing access to legitimate users without an increase in the ability of a snooper to predict confidential information. We also derive the specifications under which perturbation methods can achieve this objective. Numerical examples are provided to show that the use of the new specification achieves the objective of no additional information to the snooper. Implications of the new specification for e‐commerce are discussed.     

Data Mining for Network Intrusion Detection: A Comparison of Alternative Methods*

Intrusion detection systems help network administrators prepare for and deal with network security attacks. These systems collect information from a variety of systems and network sources, and analyze them for signs of intrusion and misuse. A variety of techniques have been employed for analysis ranging from traditional statistical methods to new data mining approaches. In this study the performance of three data mining methods in detecting network intrusion is examined. An experimental design (3times2x2) is created to evaluate the impact of three data mining methods, two data representation formats, and two data proportion schemes on the classification accuracy of intrusion detection systems. The results indicate that data mining methods and data proportion have a significant impact on classification accuracy. Within data mining methods, rough sets provide better accuracy, followed by neural networks and inductive learning. Balanced data proportion performs better than unbalanced data proportion. There are no major differences in performance between binary and integer data representation.     

An Enhanced Data Perturbation Approach for Small Data Sets

As modern organizations gather, analyze, and share large quantities of data, issues of privacy, and confidentiality are becoming increasingly important. Perturbation methods are used to protect confidentiality when confidential, numerical data are shared or disseminated for analysis. Unfortunately, existing perturbation methods are not suitable for protecting small data sets. With small data sets, existing perturbation methods result in reduced protection against disclosure risk due to sampling error. Sampling error may also produce different results from the analysis of perturbed data compared to the original data, reducing data utility. In this study, we develop an enhancement of an existing perturbation technique, General Additive Data Perturbation, that can be used to effectively mask both large and small data sets. The proposed enhancement minimizes the risk of disclosure while ensuring that the results of commonly performed statistical analyses are identical and equal for both the original and the perturbed data.     

基于流数据挖掘的网络流量异常检测及分析研究

网络流量异常检测及分析是网络及安全管理领域的重要研究内容.本文探讨了网络流量异常的种类、网络流量异常检测的方法,分析了基于传统检测方法在网络流量异常检测应用中存在的问题.并重点对基于流数据模型的网络流量异常检测进行了研究,综述了已有流数据挖掘研究方法在网络流量异常检测中的研究进展.最后,本文对现有研究工作存在的问题及未来的研究方向进行了探讨.     

基于SVM的Web日志挖掘及潜在客户发现

潜在的客户资源是商家未来的利润来源,发现了潜在的客户就可以制定相应的商业决策,并进行有针对性的客户关系管理。使用SVM方法对Web日志文件进行挖掘,以发现站点访问者中潜在客户的共同行为模式,并将其分为不同级别的目标客户群。同时,通过试验4种不同比例的训练样本,研究了非对称数据对分类结果的影响,以期获得较优的模型。     

基于动态分类器集成选择的不完整数据客户分类方法实证研究

在管理决策的制定中,分类已经成为一种十分重要的方法和技术。由于现实客户数据常常是不完整的,因此,研究不完整数据的客户分类问题具有重要意义。通过分析以往分类过程中对不完整数据的处理方法,提出了一种基于动态分类器集成选择的不完整数据分类方法DCES-ID。分别在UCI客户分类数据集以及某券商客户数据集上进行分类的实验和实证分析。结果表明,与已有的6种分类算法相比,DCES-ID算法具有更高的分类准确性及稳定性,能够更有效地进行客户分类。     

An icon-based data image construction method for production data visualization

Visualization takes advantage of human's rapid and flexible pattern recognition capacity to provide a powerful information-processing environment. It is especially suitable for identifying patterns that are difficult, or impossible, to be defined by a mathematical or computer model. This paper presents an icon-based method for constructing images to visualize the abstract, multidimensional production data stored in tables. The method employs a simple, topology-preserving mapping to convert numbers in a table to icons. This mapping strategy not only makes the meaning of the resulted data image easy to understand, but also makes the maximum number of dimensions and data items that could be displayed be virtually unlimited. Visual patterns in the data image are formed by adjacent icons that have similar grey scales. The area sampling techniques could be applied to reduce the size of the data image while still retaining its visual patterns. The proposed visualization method has been applied to several production planning and control activities, including analysing WIP patterns, production indices, and the machine idle cost performance of production plans. These industrial case studies demonstrate that the ability to quickly and easily examine large amount of manufacturing data through visualization is critical for manufacturing companies to understand their performance, discover possible problems and take necessary actions so that they could remain competitive in the market.     

Visualization of Innovation in Global Supply Chain Networks

This methodological note identifies and describes a data‐driven visualization approach to study innovations in supply chain networks (ISCN). We demonstrate its value and applicability with illustrative examples to pertinent structure‐related ISCN research questions in the global electronics industry. Our visualization approach can be used to reveal and understand important clusters, patterns, trends, and outliers of ISCN not necessarily identified with traditional methods. The broader aim of this note is to demonstrate the complementary value of emerging visual analytic approaches in managerial decision‐making contexts and describe how actionable insights can be achieved.     

A Multicriteria Outranking Modeling Approach for Credit Rating

Rating models are widely used by credit institutions to obtain estimates for the probabilities of default for their clients (firms, organizations, individuals) and to assess the risk of credit portfolios. Several statistical and data mining methods are used to develop such models. In this article, the potential of an outranking multicriteria decision‐aiding approach is explored. An evolutionary algorithm is used to fit a credit rating model on the basis of the ELimination Et Choix Traduisant la REalité trichotomique method. The methodology is applied to a large sample of Greek firms. The results indicate that outranking models are well suited to credit rating, providing good classification results and useful insight on the relative importance of the evaluation criteria.     

基于信息融合的数据挖掘方法在公司财务预警中的应用

目前越来越多的数据挖掘方法被用于风险预警中,决策树、支持向量机、神经网络、Logistic回归等方法在风险预警中都表现出了较好的特性和预警效果,但是不同数据挖掘分类方法得到的结果不同,往往导致预警结果的不一致,因此也会存在一定风险。本文引入信息融合技术对不同数据挖掘分类方法得到的结果进行融合处理得到最优的结果,解决了不同数据挖掘方法得到的结果不一致问题。文章在SVM和Logistic回归的数据挖掘模型基础上建立基于信息融合的公司财务预警模型,提高了财务预警准确率,并且保留了原数据挖掘方法在分类预测上的优势。在实证研究中,论文选取了中国制造业的上市公司作为研究对象,在SVM和Logistic回归两种数据挖掘模型的基础上利用信息融合方法建立了财务预警模型,实证结果表明,基于信息融合的数据挖掘方法的预测准确率要高于单独的SVM和Logistic回归两种方法。     

Comparison and Validation of Statistical Methods for Predicting Power Outage Durations in the Event of Hurricanes

This article compares statistical methods for modeling power outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of power outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk management resources to balance the disruption from hurricanes with the cost of hardening power systems. We compare the out‐of‐sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict outage durations with reasonable accuracy.     

数据挖掘技术在商业银行中的应用

数据挖掘能够有效分析商业银行数据库中的信息,将其转化为知识为银行的经验决策服务。本文在介绍数据挖掘技术及其主要任务的基础上,总结了数据挖掘在商业银行业务中的主要应用领域为客户关系管理、风险管理和金融欺诈监测,并具体介绍了数据挖掘技术在上述几个领域内的应用。     

信息共享环境的数据与技术模块研究

信息共享环境与国家安全和国际安全密切相关,近几年才受到国内外政府与学者的广泛关注和高度重视.数据模块与技术模块是构建信息共享环境的核心.数据模块探讨了功能标准、模块构架、数据描述的抽象模型、数据环境的抽象模型和数据共享的抽象模型等.技术模块探讨了模块架构、服务接入与输出模型、服务平台与基础设施模型、框架模型、以及服务界面与集成模型等.     

基于XML的通用关联规则挖掘应用模式

本文分析了关联规则挖掘应用中在通用性、用户简易性以及可扩展性方面所面临的一些困难,提出了一种基于XML的通用关联规则挖掘应用模式。该模式充分利用了XML在自描述能力、异质系统数据交换能力以及可扩展性方面的优势,提供了一个模块化、易于集成、适合于最终用户使用的应用框架。     

一个新的银行信用风险评估方法

本文以银行信用风险管理为例,将粗糙集和决策树两种具有互补优势的数据挖掘方法相结合,对客户信用做出归类分析判断,最后利用决策树生成决策规则.实践证明,这种方法忠于原始数据,提高了分类准确度,减小了决策树规模,具有良好的性能.     

Exploring the Effects of the Privacy‐Handling Management Styles of Social Networking Sites on User Satisfaction: A Conflict Management Perspective

The issue of consumer information privacy, arguably the most substantial and persistent problem confronting e‐commerce companies in general and online social networking service providers in particular, often results in unsatisfied customers. Researchers have investigated privacy from various perspectives and in a multitude of settings, yet there have been few attempts to understand privacy versus satisfaction, particularly from the perspective of conflict management. Because users’ privacy can be negatively affected by social networking sites (SNSs), this study focuses on the privacy conflicts between SNSs and their users. Drawing on conflict management theory, this article investigates the effects of different conflict management styles exhibited by an SNS on users’ satisfaction with its privacy practices, which then affects their intention to disclose personal information. The SNS examined in this study is Facebook, the most popular SNS with the largest number of users. Data were collected using an online survey and were analyzed using structural equation modeling. The results suggest that the two cooperative conflict management styles demonstrated by the SNS—accommodating and problem‐solving—positively affect users’ satisfaction with the privacy practices of the SNS both directly and indirectly through perceived privacy risk control. With regard to uncooperative styles, an avoiding style negatively affects user satisfaction directly, whereas an asserting style negatively affects user satisfaction indirectly through perceived privacy risk. The results also support that satisfaction is positively related to intention; users who are satisfied with the privacy practices of SNSs are more willing to disclose personal information than unsatisfied ones.     

Novel quadratic programming approach for time series clustering with biomedical application

Fundamental problems in data mining mainly involve discrete decisions based on numerical analyses of data (e.g., class assignment, feature selection, data categorization, identifying outlier samples). These decision-making problems in data mining are combinatorial in nature and can naturally be formulated as discrete optimization problems. One of the most widely studied problems in data mining is clustering. In this paper, we propose a new optimization model for hierarchical clustering based on quadratic programming and later show that this model is compact and scalable. Application of this clustering technique in epilepsy, the second most common brain disorder, is a case point in this study. In our empirical study, we will apply the proposed clustering technique to treatment problems in epilepsy through the brain dynamics analysis of electroencephalogram (EEG) recordings. This study is a proof of concept of our hypothesis that epileptic brains tend to be more synchronized (clustered) during the period before a seizure than a normal period. The results of this study suggest that data mining research might be able to revolutionize current diagnosis and treatment of epilepsy as well as give a greater understanding of brain functions (and other complex systems) from a system perspective. This work was partially supported by the NSF grant CCF 0546574 and Rutgers Research Council grant-202018.     

Risk Assessment Can Be a Game‐Changing Information Technology—But Too Often It Isn't

The printing press was a game‐changing information technology. Risk assessment could be also. At present, risk assessments are commonly used as one‐time decision aids: they provide justification for a particular decision, and afterwards usually sit on a shelf. However, when viewed as information technologies, their potential uses are much broader. Risk assessments: (1) are repositories of structured information and a medium for communication; (2) embody evaluative structures for setting priorities; (3) can preserve information over time and permit asynchronous communication, thus encouraging learning and adaptation; and (4) explicitly address uncertain futures. Moreover, because of their “what‐if” capabilities, risk assessments can serve as a platform for constructive discussion among parties that hold different values. The evolution of risk assessment in the nuclear industry shows how such attributes have been used to lower core‐melt risks substantially through improved templates for maintenance and more effective coordination with regulators (although risk assessment has been less commonly used in improving emergency‐response capabilities). The end result of this evolution in the nuclear industry has been the development of “living” risk assessments that are updated more or less in real time to answer even routine operational questions. Similar but untapped opportunities abound for the use of living risk assessments to reduce risks in small operational decisions as well as large policy decisions in other areas of hazard management. They can also help improve understanding of and communication about risks, and future risk assessment and management. Realization of these opportunities will require significant changes in incentives and active promotion by the risk analytic community.     

Competitive and Cooperative Positioning in Supply Chain Logistics Relationships*

Cooperative logistics relationships require the sharing of information, which must be enabled by the integration of disparate information systems across partners. In this article, we theorize business‐to‐business logistics relationships should be managed using cooperative and competitive postures. Based on data from 91 dyadic relationships using interorganizational information technology (IT), we find that performance gains accrue when parties share strategic information and customize IT; mutual trust enables IT customization and strategic‐information flows and equitable relationship‐specific investments positively impact IT customization, mutual trust, and performance. Among other scholarly and practical implications discussed, partners should compete on resources for IT customization and cooperate to share strategic information. Managers tend to think of relationships with firms as polar opposites and view them as entirely cooperative or entirely competitive. Our results support active balancing and understanding of both competitive and cooperative stances. Such an approach enables conditions for participation symmetry that yields greater performance gains.     

Corporate Responsibility and Compliance with the Law: A Case Study of Land,Dispossession, and Aftermath at Newmont's Ahafo Project in Ghana1

An important part of responsible business practices is compliance with the law. This article details what actually happens when the laws of the host country fail to ensure adequate protection. The focus here is on land dispossession and loss of livelihood in relation to a gold mine project in central Ghana. How is it that a well‐known international company—Newmont—with its own corporate social responsibility (CSR) statements sets up a project in the year 2003 that displaces subsistence farmers from their land without compensating in cash or with replacement land? The analysis identifies the factors that lead the company to not compensate farmers for their lost land: cost‐cutting, strict adherence to the law, CSR commitment that was new and not internalized, complexities of the Ghanaian land tenure system, peer pressure to preserve the status quo, selection of an “old‐school” CSR manager, and the inadequacy of Ghanaian mining law to account for relatively novel, “open‐pit” mining techniques. However, the specter of famine raised by civil society activism, the involvement of the International Financial Corporation, and a better qualified CSR team constitute another set of factors that lead to a comprehensive package of livelihood improvement measures. There is a contrast between the complexity, long‐term, and advanced type of assistance Newmont currently envisages and the backward, short‐term, formalism, and brutality of denying compensation for land back in 2003. This research is based on the extensive documentation Newmont makes available on its web site, interviews conducted in Ghana, and literature research.