Information mining projects management process

Information Mining (also known as Knowledge Discovery Process) is a growing discipline in continuous expansion. Most of the progress accomplished, are focus on the development activities (i.e. those technical activities associated with the comprehension and adaptation of data, and the implementation of data mining algorithm). According to this conceptual framework, several process models were developed, which allow organizing and defining the set of tasks related to the development of information mining projects. These approaches omit the set of tasks oriented to the management and control of the process. In this paper, we propose a transversal management process to the development process currently in use in information mining projects. The proposed process focuses on removing existing gaps, providing an improvement on the project's maturity and quality levels.Instituto de Investigación en InformáticaFacultad de Informátic

Information mining projects management process

Information Mining (also known as Knowledge Discovery Process) is a growing discipline in continuous expansion. Most of the progress accomplished, are focus on the development activities (i.e. those technical activities associated with the comprehension and adaptation of data, and the implementation of data mining algorithm). According to this conceptual framework, several process models were developed, which allow organizing and defining the set of tasks related to the development of information mining projects. These approaches omit the set of tasks oriented to the management and control of the process. In this paper, we propose a transversal management process to the development process currently in use in information mining projects. The proposed process focuses on removing existing gaps, providing an improvement on the project's maturity and quality levels.Instituto de Investigación en InformáticaFacultad de Informátic

Process Mining for IS Project Success Factors Management: A proposal

Research on Success Factors (SF) of Information Systems (IS) projects carried out over the last decades has resulted in a vast literature. However, extant studies typically aim to identify and list generic SF for projects, denoting a static perspective, with few concerns of practical nature regarding their use as management tools to support decisions throughout the projects’ lifecycle. On the other hand, process mining has been used to discover, analyze, and improve project management processes. In this paper, we propose a new approach that involves relating the performance of those processes with SF in IS projects. By using process mining, the aim is to automatically extract and manage SF in projects, measure processes performance, and provide project managers with information on how SF correlate with performance. This will provide managers with enhanced information regarding status and improvement opportunities for current and future projects. The main purpose is to contribute to the project management theory and practice by providing a decision support system that can associate performance with IS projects’ SF automatically obtained from internal and external data sources

A Hybrid Mining Approach to Facilitate Health Insurance Decision: Case Study of Non-Traditional Data Mining Applications in Taiwan NHI Databases

This study examines time-sensitive applications of data mining methods to facilitate claims review processing and provide policy information for insurance decision-making vis-à-vis the Taiwan National Health Insurance databases. In order to obtain the best payment management, a hybrid mining approach, which has been grounded on the extant knowledge of data mining projects and health insurance domain knowledge, is proposed. Through the integration of data warehousing, online analytical processing, data mining techniques and traditional data analysis in the healthcare field, an easy-to-use decision support platform, which will facilitate the health insurance decision-making process, is built. Drawing from lessons learned in case study, results showed that not only is hybrid mining approach a reliable, powerful, and user-friendly platform for diversified payment decision support, but that it also has great relevance for the practice and acceptance of evidence-based medicine. Researchers should develop hybrid mining approach combined with their own application systems in the future

A Hybrid Mining Approach to Facilitate Health Insurance Decision: Case Study of Non-Traditional Data Mining Applications in Taiwan NHI Databases

This study examines time-sensitive applications of data mining methods to facilitate claims review processing and provide policy information for insurance decision-making vis-à-vis the Taiwan National Health Insurance databases. In order to obtain the best payment management, a hybrid mining approach, which has been grounded on the extant knowledge of data mining projects and health insurance domain knowledge, is proposed. Through the integration of data warehousing, online analytical processing, data mining techniques and traditional data analysis in the healthcare field, an easy-to-use decision support platform, which will facilitate the health insurance decision-making process, is built. Drawing from lessons learned in case study, results showed that not only is hybrid mining approach a reliable, powerful, and user-friendly platform for diversified payment decision support, but that it also has great relevance for the practice and acceptance of evidence-based medicine. Researchers should develop hybrid mining approach combined with their own application systems in the future

Critical success factors for the successful customer relationship management: a conceptual case study

Customer Relationship Management (CRM) technology have integrated the latest information technology, including: internet and E-commerce, multi-media technology, data warehousing data mining and artificial intelligence. This is all about the value of customer relationship management. It congregate the scattering data through the process of analysis, it provide a comprehensive and holistic view of certain individual customers. Customer Relationship Management originated and prevailed among western companies, it has already spread in many East Asian countries, such as: Japan, Korean, India and China etc. In order to improve the existing CRM implementation process and enhance the success rate of the CRM implementation, we present the most important Critical Success Factors for the CRM implementation through literature reviews, the chosen CSFs were based on previous studies in the CRM implementation field, focus on the identification of CRM projects, whether they have achieved success or subject to obscure deficiency. Subsequently, the literature study will provide us a group of CSFs which considered to be a comprehensive summarization of those most important factors for CRM implementation projects. It is a challenging work, still some points are summarized

Learning from the past: uncovering design process models using an enriched process mining

Design documents and design project footprints accumulated by corporate IT systems have increasingly become valuable sources of evidence for design information and knowledge management. Identification and extraction of such embedded information and knowledge into a clear and usable format will greatly accelerate continuous learning from past design efforts for competitive product innovation and efficient design process management in future design projects. Different from existing systems, this paper proposes a methodology of learning and extracting useful knowledge using past design project documents from design process perspective based on process mining techniques. A new process mining approach that is able to directly handle textual data is proposed at the first stage of the proposed methodology. The outcome is a hierarchical process model that reveals the actual design process hidden behind a large amount of design documents and enables the connection of various design information from different perspectives. At the second stage, the discovered process model is further refined to learn multi-faceted knowledge patterns by applying a number of statistical analysis methods. The outcomes range from task dependency study from workflow analysis, identification of irregular task execution from performance analysis, cooperation pattern discovery from social net analysis, to evaluation of personal contribution based on role analysis. Relying on the knowledge patterns extracted, lessons and best practices can be uncovered which offer great support to decision makers in managing any future design initiatives. The proposed methodology was tested using an email dataset from a university-hosted multi-year multidisciplinary design project

Proceedings of Real Time Mining - International Raw Materials Extraction Innovation Conference : 10th & 11th October 2017, Amsterdam, The Netherlands

The first conference on Real-Time Mining is bringing together individuals and companies working on EU-sponsored projects to exchange knowledge and rise synergies in resource extraction innovation. The topics include: • Resource Modelling and Value of Information; • Automated Material Characterization; • Positioning and Material Tracking; • Process Optimization; • Data Management. The conference has been initiated by the consortium of the EU H2020 funded project Real-Time Mining as a platform for inter-project communication and for communication with project stakeholders. It brings together several European research projects in the field of industry 4.0 applied to mineral resource extraction. These are the projects VAMOS, SOLSA and UNEXMIN

Modelling, monitoring and evaluation to support automatic engineering process management

Process management is considered to be an essential approach to improve the performance of an enterprise. The process of an engineering project is considered to be a formalised workflow accompanied by a set of decisions. With decisions being made by taking account of information from various sources, the operation and management of modern engineering projects has to deal with increasing amounts of dynamic and changing project information. Understanding and interpreting this information for use in process management can generate challenges in practice. This might be caused by constraints of time and resource, the distributed structure of the information and a lack of modelled domain knowledge. To address these challenges, the research described in this paper focuses on techniques that support automation of the process management of engineering projects, from a data-driven perspective. The research includes elements of process modelling, monitoring and evaluation of such projects, through a proposed automatic process analysis system. The proposed system works with live and historical data. Within this paper, the design and implementation of the system is described. The use of techniques such as autonomic computing, data mining and KM technologies are shown, and the system functionality is demonstrated through the use of a dataset from an aerospace organisation