Data mining in course management systems: Moodle case study and tutorial

Educational data mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. This work is a survey of the specific application of data mining in learning management systems and a case study tutorial with the Moodle system. Our objective is to introduce it both theoretically and practically to all users interested in this new research area, and in particular to online instructors and e-learning administrators. We describe the full process for mining e-learning data step by step as well as how to apply the main data mining techniques used, such as statistics, visualization, classification, clustering and association rule mining of Moodle data. We have used free data mining tools so that any user can immediately begin to apply data mining without having to purchase a commercial tool or program a specific personalized tool.     

Data warehousing and data mining techniques for intrusion detection systems

This paper describes data mining and data warehousing techniques that can improve the performance and usability of Intrusion Detection Systems (IDS). Current IDS do not provide support for historical data analysis and data summarization. This paper presents techniques to model network traffic and alerts using a multi-dimensional data model and star schemas. This data model was used to perform network security analysis and detect denial of service attacks. Our data model can also be used to handle heterogeneous data sources (e.g. firewall logs, system calls, net-flow data) and enable up to two orders of magnitude faster query response times for analysts as compared to the current state of the art. We have used our techniques to implement a prototype system that is being successfully used at Army Research Labs. Our system has helped the security analyst in detecting intrusions and in historical data analysis for generating reports on trend analysis. Recommended by: Ashfaq Khokhar     

Visual exploration of production data using small multiples design with non-uniform color mapping

Visual data mining may overcome some of the flexibility problem often suffered by computer-centered data mining approaches. This can happen because human beings are introduced to the information discovery loop to take advantage of their natural strength in creative thinking and rapid visual pattern recognition to discover information not defined a priori and to perform approximated reasoning that computer algorithms are hard to do. This paper presents a novel visual exploration approach for mining abstract, multi-dimensional data stored in tables in a relational database. The visual image is constructed by converting each table into a visualization unit called a table graph and then assembling these table graphs together to form a small multiples design. Different types of non-uniform color mappings to render this small multiples design could be automatically generated by minimizing the weight differences of colors in the visual image. These non-uniform color mappings are designed in such a way that the adjacent glyphs in a table graph that have near underlying values will be assigned with the same color. As such, visual patterns not able to see under the traditional uniform color mapping could be revealed. This enables the users to examine the input tables from different perspectives. The proposed flexible visualization method has been applied to generate visual images from which the users could quickly and easily compare the machine idle cost performances of alternative master production plans.     

Increasing availability of industrial systems through data stream mining

Improving industrial product reliability, maintainability and thus availability is a challenging task for many industrial companies. In industry, there is a growing need to process data in real time, since the generated data volume exceeds the available storage capacity. This paper consists of a review of data stream mining and data stream management systems aimed at improving product availability. Further, a newly developed and validated grid-based classifier method is presented and compared to one-class support vector machine (OCSVM) and a polygon-based classifier.     

Shape mining: A holistic data mining approach for engineering design

Although the integration of engineering data within the framework of product data management systems has been successful in the recent years, the holistic analysis (from a systems engineering perspective) of multi-disciplinary data or data based on different representations and tools is still not realized in practice. At the same time, the application of advanced data mining techniques to complete designs is very promising and bears a high potential for synergy between different teams in the development process. In this paper, we propose shape mining as a framework to combine and analyze data from engineering design across different tools and disciplines. In the first part of the paper, we introduce unstructured surface meshes as meta-design representations that enable us to apply sensitivity analysis, design concept retrieval and learning as well as methods for interaction analysis to heterogeneous engineering design data. We propose a new measure of relevance to evaluate the utility of a design concept. In the second part of the paper, we apply the formal methods to passenger car design. We combine data from different representations, design tools and methods for a holistic analysis of the resulting shapes. We visualize sensitivities and sensitive cluster centers (after feature reduction) on the car shape. Furthermore, we are able to identify conceptual design rules using tree induction and to create interaction graphs that illustrate the interrelation between spatially decoupled surface areas. Shape data mining in this paper is studied for a multi-criteria aerodynamic problem, i.e. drag force and rear lift, however, the extension to quality criteria from different disciplines is straightforward as long as the meta-design representation is still applicable.     

数据挖掘技术的应用

本文探讨数据挖掘技术在中油集团新疆培训中心的应用。现有培训管理信息系统的数据库积累了大量历史数据,在此基础上使用数据挖掘技术,应用微软SQLServer2005的数据挖掘集成环境,以Microsoft时序算法为例,建立数据挖掘模型,进行数据挖掘,预测各承办部门的培训能力,实现为管理人员合理配置培训资源的决策提供有用信息,最后总结了在开发过程遇到的问题及解决办法。     

Combining data mining and Game Theory in manufacturing strategy analysis

The work presented in this paper is result of a rapid increase of interest in game theoretical analysis and a huge growth of game related databases. It is likely that useful knowledge can be extracted from these databases. This paper argues that applying data mining algorithms together with Game Theory poses a significant potential as a new way to analyze complex engineering systems, such as strategy selection in manufacturing analysis. Recent research shows that combining data mining and Game Theory has not yet come up with reasonable solutions for the representation and structuring of the knowledge in a game. In order to examine the idea, a novel approach of fusing these two techniques has been developed in this paper and tested on real-world manufacturing datasets. The obtained results have been indicated the superiority of the proposed approach. Some fruitful directions for future research are outlined as well.     

From telephones to iPhones: applying systems thinking to networked, interoperable products

An expanding array of consumer products have the facility to have things added in and plugged on, their firmware upgraded, and as yet un-thought of future capability supported. In short, more and more products can be connected to something and/or someone, and in doing so are slowly adapting to the current day state of modernity that is called ‘the information age’. Inevitably, this brings with it changes in the way that products should be thought about and designed. The purpose of this paper is to try and help product designers and Ergonomists to get a grip on all the complexity and non-linearity that the information age brings with it, and help make themselves and their increasingly networked and interoperable products at home in it. Our case study, Apple's new iPhone, serves as a pertinent example.     

A novel deterministic approach for aspect-based opinion mining in tourism products reviews

This work proposes an extension of Bing Liu’s aspect-based opinion mining approach in order to apply it to the tourism domain. The extension concerns with the fact that users refer differently to different kinds of products when writing reviews on the Web. Since Liu’s approach is focused on physical product reviews, it could not be directly applied to the tourism domain, which presents features that are not considered by the model. Through a detailed study of on-line tourism product reviews, we found these features and then model them in our extension, proposing the use of new and more complex NLP-based rules for the tasks of subjective and sentiment classification at the aspect-level. We also entail the task of opinion visualization and summarization and propose new methods to help users digest the vast availability of opinions in an easy manner. Our work also included the development of a generic architecture for an aspect-based opinion mining tool, which we then used to create a prototype and analyze opinions from TripAdvisor in the context of the tourism industry in Los Lagos, a Chilean administrative region also known as the Lake District. Results prove that our extension is able to perform better than Liu’s model in the tourism domain, improving both Accuracy and Recall for the tasks of subjective and sentiment classification. Particularly, the approach is very effective in determining the sentiment orientation of opinions, achieving an F-measure of 92% for the task. However, on average, the algorithms were only capable of extracting 35% of the explicit aspect expressions, using a non-extended approach for this task. Finally, results also showed the effectiveness of our design when applied to solving the industry’s specific issues in the Lake District, since almost 80% of the users that used our tool considered that our tool adds valuable information to their business.     

A systematic data-mining-based methodology for product family design and product configuration

Product family design and product configuration based on data mining technology is identified as an intelligent and automated means to improve the efficiency of product development. However, few of previous literatures have proposed systematic product family design method based on data mining technology. To make up for this deficiency, this research put forward a systematic data-mining-based method for product family design and product configuration. First, the customer requirement information and product engineering information in the historical order are formatted into structural data. Second, principal component analysis is performed on historical orders to extract the customers' differentiated needs. Third, association rule algorithm is introduced to mine the rules between differentiated needs and module instances in the historical orders, thus obtained the configuration knowledge between customer needs and product engineer. Forth, the mined rules are used to construct association rule-based classifier (CBA) that is employed to sort out the best product configuration schemes as popular product variants. Fifth, sequence alignment technique is employed to identify modules for popular product variants, so that the module instances are divided into optional, common and special module, respectively, thereby the product platform is generated based on common modules. Finally, according to new customer needs, the CBA classifier is used to recommend the best configuration schemes, and then popular product variants are configured based on the product platform. The feasibility of the proposed method is demonstrated by the product family design example of desktop computer hosts.     

An extended model of design process of lean production systems by means of process variables

In this paper, we present an axiomatic modeling of lean production system design, using process variables (PVs). So far, we had developed a model for conceptual design of lean production systems by means of FR–DP relationships, the key characteristics of axiomatic design (AD) methodology, appeared in the proceedings of Second International Conference of Axiomatic Design. Albeit the model in question was thorough enough to be applied in various cases, its embedded abstract principles hamper straightforward applications and the required resources, tools, and techniques are not clarified. In AD terms, it lacks PVs created by mapping the design parameters (DPs) to the process domain to clarify the means that produce the specified DPs. Owing to the difficulties involved in the definition of PVs for manufacturing systems, there is few works in this area. This paper is an attempt to introduce PVs in production system design. When we are developing a product (i.e. a part), we can simply interpret the set of PVs as the process design. In the case of a production system we interpret PVs as the tools, methods, and resources, required for implementing a lean production system. In this paper, according to AD methodology, we have developed a hierarchical structure to model the design process of a lean production system, composed of FRs, DPs, and PVs. Serving as an efficient guideline for the design process and clarifying the required tools, methods, and resources, this structure is general enough to be applied for different cases.     

ManufAg: a multi-agent-system framework for production control of complex manufacturing systems

In this paper, we present a framework for the implementation of multi-agent-systems for production control of complex manufacturing systems. We present the results of a requirement analysis for production control systems for complex manufacturing systems; then we describe the framework design criteria. Our framework supports the inclusion of distributed hierarchical decision-making schemes into the production control. Furthermore, in order to increase the coordination abilities of multi-agent-systems, we follow the decision-making and staff agent architecture suggested in the PROSA reference architecture. We indicate the usage of the framework for designing and implementing an agent-based production control system for semiconductor manufacturing processes in a case study.     

Anticipating the use of future things: Towards a framework for prospective use analysis in innovation design projects

Anticipation of future product use is a persistent issue in User-Centered Design. In this paper, we argue that one obstacle to early integration of use analysis in innovation design is overreliance on retrospective use analysis, i.e. that which is based on clear references to existing products or activities. In contrast, innovation design projects are full of uncertainty, leading to a need for prospective analysis. After having described some limitations of prospective use analysis, we contend that creativity tools may be used to assist the anticipation of future product use, by allowing designers to approach the variability of situations of future use in a structured manner rather than by “muddling through”. We illustrate the expected benefits of this approach with two case studies, and describe some prospects for future research and practice in ergonomics.     

Ontology-based data mining approach implemented on exploring product and brand spectrum

In physics, a spectrum is, the series of colored bands diffracted and arranged in the order of their respective wave lengths by the passage of white light through a prism or other diffracting medium. Outside of physics, a spectrum is a condition that is not limited to a specific set of values but can vary infinitely within a continuum. In commerce, an effective visualization tool, especially for stakeholders or managers, is a brand spectrum diagram highlighting where the company’s brands and products are situated compared to other competitors. This paper investigates the research issues on product and brand spectrum in the beverage product market of Taiwan, which proposes using the Apriori algorithm of association rules, and clustering analysis based on an ontology-based data mining approach, for mining customer and product knowledge from the database. Knowledge extracted from data-mining results is illustrated as knowledge patterns, rules, and maps in order to propose suggestions and solutions to beverage firms for possible product development, promotion, and marketing.     

数据挖掘技术研究

本文首先对数据挖掘技术进行了系统的分析和说明,并详细叙述了数据挖掘的过程以及几种技术和方法,最后对数据挖掘技术在各个领域的应用做了叙述。     

电信网络管理的数据仓库解决方案

数据仓库技术是近几年迅速发展起来的一种技术,国际上许多电信运营企业都积极关注于数据仓库技术在电信运营部门和网络操作上的应用。本文对数据仓库技术在电信管理网(TMN)中的应用作了一些探讨,并提出了一种具体的应用方案。