Development of a co-operative distributed process mining system for quality assurance

In this paper, a co-operative distributed process mining system (CDPMS) is developed to streamline the workflow along the supply chain in order to offer shorter delivery times, more flexibility and higher customer satisfaction with learning ability. The proposed system is equipped with the ‘distributed process mining’ feature which is used to discover the hidden relationships among each working decision in distributed manner. This method incorporates the concept of data mining and knowledge refinement into decision making process for ensuring ‘doing the right things’ within the workflow. An example of implementation is given, based on the case of slider manufacturer.

Development of an intelligent quality management system using fuzzy association rules

In order to survive in the increasingly customer-oriented marketplace, continuous quality improvement marks the fastest growing quality organization’s success. In recent years, attention has been focused on intelligent systems which have shown great promise in supporting quality control. However, only a small number of the currently used systems are reported to be operating effectively because they are designed to maintain a quality level within the specified process, rather than to focus on cooperation within the production workflow. This paper proposes an intelligent system with a newly designed algorithm and the universal process data exchange standard to overcome the challenges of demanding customers who seek high-quality and low-cost products. The intelligent quality management system is equipped with the ‘‘distributed process mining” feature to provide all levels of employees with the ability to understand the relationships between processes, especially when any aspect of the process is going to degrade or fail. An example of generalized fuzzy association rules are applied in manufacturing sector to demonstrate how the proposed iterative process mining algorithm finds the relationships between distributed process parameters and the presence of quality problems.

Visual data mining: integrating machine learning with information visualization

Today, the data available to tackle many scientific challenges is vast in quantity and diverse in nature. The exploration of heterogeneous information spaces requires suitable mining algorithms as well as effective visual interfaces. Most existing systems concentrate either on mining algorithms or on visualization techniques. Though visual methods developed in information visualization have been helpful, for improved understanding of a complex large high-dimensional dataset, there is a need for an effective projection of such a dataset onto a lower-dimension (2D or 3D) manifold. This paper introduces a flexible visual data mining framework which combines advanced projection algorithms developed in the machine learning domain and visual techniques developed in the information visualization domain. The framework follows Shneiderman’s mantra to provide an effective user interface. The advantage of such an interface is that the user is directly involved in the data mining process. We integrate principled projection methods, such as Generative Topographic Mapping (GTM) and Hierarchical GTM (HGTM), with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates, billboarding, and user interaction facilities, to provide an integrated visual data mining framework. Results on a real life high-dimensional dataset from the chemoinformatics domain are also reported and discussed. Projection results of GTM are analytically compared with the projection results from other traditional projection methods, and it is also shown that the HGTM algorithm provides additional value for large datasets. The computational complexity of these algorithms is discussed to demonstrate their suitability for the visual data mining framework.

A legal perspective of integrated surface mining, solid waste landfill reclamation and land conservation

In the area of international environmental law this thesis proposes the formulation of one-step planning and permitting regulation for the integrated utilisation of new surface mines as depositories for municipal solid waste. Additionally, the utilisation of abandoned and currently operated surface mines is proposed as solid waste landfills as an integral step in their reclamation. Existing laws, litigation and issues in the United Kingdom, the U.S. and Canada are discussed because of their common legal system, language and heritage. The critical shortage of approved space for disposal of solid waste has caused an urgent and growing problem for both the waste disposal industry and society. Surface mining can serve three important environmental and societal functions inuring to the health and welfare of the public: (1) providing basic minerals for goods and construction; (20 sequentially, to provide critically needed, safe burial sites for society's wastes, and (3) to conserve land by dual purpose use and to restore derelict land to beneficial surface use. Currently, the first two functions are treated environmentally, and in regulation, as two different siting problems, yet they both are earth-disturbing and excavating industries requiring surface restoration. The processes are largely duplicative and should be combined for better efficiency, less earth disturbance, conservation of land, and for fuller and better reclamation of completed surface mines returning the surfaces to greater utility than present mined land reclamation procedures. While both industries are viewed by a developed society and its communities as "bad neighbours", they remain essential and critical for mankind's existence and welfare. The study offers successful examples of the integrated process in each country. The study argues that most non-fuel surface mine openings, if not already safe, can economically, through present containment technology, be made environmentally safe for use as solid waste landfills. Simultaneously, the procedure safeguards and monitors protection of ground and surface waters from landfill contamination.

Visual data mining using principled projection algorithms and information visualization techniques

We introduce a flexible visual data mining framework which combines advanced projection algorithms from the machine learning domain and visual techniques developed in the information visualization domain. The advantage of such an interface is that the user is directly involved in the data mining process. We integrate principled projection algorithms, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates and billboarding, to provide a visual data mining framework. Results on a real-life chemoinformatics dataset using GTM are promising and have been analytically compared with the results from the traditional projection methods. It is also shown that the HGTM algorithm provides additional value for large datasets. The computational complexity of these algorithms is discussed to demonstrate their suitability for the visual data mining framework. Copyright 2006 ACM.