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knowledge discovery in databases: application to chromatography

TRAC-TRENDS IN ANALYTICAL CHEMISTRY 1998年第1期17卷 18-24页

作者： Bryant, CH Rowe, RC Univ Huddersfield Sch Comp & Math Huddersfield HD1 3DH W Yorkshire England Zeneca Pharmaceut Macclesfield SK10 2NA Cheshire England

This article reviews emerging computer techniques for discovering knowledge from databases and their application to various sets of separation data, The data-sets include the separation of a diverse range of analytes using either liquid, gas or ion chromatography, The main conclusion is that the new techniques should help to close the gap between the rate at which chromatographic data is gathered and stored electronically and the rate at which it can be analysed and understood. (C) 1998 Elsevier Science B.V.

关键词： knowledge discovery in databases machine learning

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Evaluation of rule interestingness measures in medical knowledge discovery in databases

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2007年第3期41卷 177-196页

作者： Ohsaki, Miho Hidenao, Abe Tsumoto, Shusaku Yokoi, Hideto Yamaguchi, Takahira Doshisha Univ Fac Engn Kyotanabe Shi Kyoto 6100321 Japan Shimane Univ Dept Med Informat Izumo Shimane 6938501 Japan Kagawa Univ Hosp Dept Med Informat Miki Kagawa 7610793 Japan Keio Univ Fac Sci & Technol Kohoku Ku Kanagawa 2238522 Japan

Objective: We discuss the usefulness of rule interestingness measures for medical KDD through experiments using clinical datasets, and, based on the outcomes of these experiments, also consider how to utilize these measures in postprocessing. Methods and materials: We first conducted an experiment to compare the evaluation results derived from a total of 40 various interestingness measures with those supplied by a medical expert for rules discovered in a clinical dataset on meningitis. We calculated and compared the performance of each interestingness measure to estimate a medical expert's interest using f-measure and correlation coefficient. We then conducted a similar experiment for hepatitis. Results and conclusion: The comprehensive results of experiments on meningitis and hepatitis indicate that the interestingness measures, accuracy, chi-square measure for one quadrant, relative risk, uncovered negative, and peculiarity, have a stable, reasonable performance in estimating real human interest in the medical domain. The results also indicate that the performance of interestingness measures is influenced by the certainty of a hypothesis made by the medical expert, and that the combinational use of interestingness measures will contribute to support medical experts to generate and confirm their hypotheses through human-system interaction. (c) 2007 Elsevier B.V. All rights reserved.

关键词： data mining knowledge discovery in databases interestingness postprocessing clinical data

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KDDML: A middleware language and system for knowledge discovery in databases

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DATA & knowledge ENGINEERING 2006年第2期57卷 179-220页

作者： Romei, A Ruggieri, S Turini, F Univ Pisa Dipartimento Informat I-56127 Pisa Italy

KDDML (KDD Markup Language) is a middleware language and system designed to support the development of final applications or higher level systems which deploy a mixture of data access, data preprocessing, extraction and deployment of data mining models. We present our three-years' experience in the development of KDDML. The design principles are motivated by requirements derived from recurring patterns in the KDD process. The KDDML language is XML-based, both for query syntax and data/model representation. A KDDML query is an XML-document where XML tags correspond to operations on data/models, XML attributes correspond to parameters of those operations and XML sub-elements define arguments passed to the operators. We present the operators for data access and preprocessing, model extraction and deployment, and control flow ones. The core of the KDDML system is a KDDML language interpreter with modularity and extensibility requirements as the main goals. Additional data sources, and preprocessing and mining algorithms can be easily plugged in the system. (c) 2005 Elsevier B.V. All rights reserved.

关键词： knowledge discovery in databases data mining query languages

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A multistrategy approach to relational knowledge discovery in databases

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MACHINE LEARNING 1997年第3期27卷 287-312页

作者： Morik, K Brockhausen, P Computer Science Department LS VIII Univ. Dortmund Dortmund

When learning from very large databases, the reduction of complexity is extremely important. Two extremes of making knowledge discovery in databases (KDD) feasible have been put forward. One extreme is to choose a very simple hypothesis language, thereby being capable of very fast learning on real-world databases. The opposite extreme is to select a small data set, thereby being able to learn very expressive (first-order logic) hypotheses. A multistrategy approach allows one to include most of these advantages and exclude most of the disadvantages. Simpler learning algorithms detect hierarchies which are used to structure the hypothesis space for a more complex learning algorithm. The better structured the hypothesis space is, the better learning can prune away uninteresting or losing hypotheses and the faster it becomes. We have combined inductive logic programming (ILP) directly with a relational database management system. The ILP algorithm is controlled in a model-driven way by the user and in a data-driven way by structures that are induced by three simple learning algorithms.

关键词： knowledge discovery in databases inductive logic programming functional dependencies numerical intervals background knowledge

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A knowledge discovery in databases approach for industrial microgrid planning

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2016年 60卷 615-630页

作者： Gamarra, Carlos Guerrero, Josep M. Montero, Eduardo Univ Burgos Dept Electromech Engn Burgos Spain Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark

The progressive application of Information and Communication Technologies to industrial processes has increased the amount of data gathered by manufacturing companies during last decades. Nowadays some standardized management systems, such as ISO 50.001 and ISO 14.001, exploit these data in order to minimize the environmental impact of manufacturing processes. At the same time, microgrid architectures are progressively being developed, proving to be suitable for supplying energy to continuous and intensive consumptions, such as manufacturing processes. In the merge of these two tendencies, industrial microgrid development could be considered a step forward towards more sustainable manufacturing processes if planning engineers are capable to design a power supply system, not only focused on historical demand data, but also on manufacturing and environmental data. The challenge is to develop a more sustainable and proactive microgrid which allows identifying, designing and developing energy efficiency strategies at supply, management and energy use levels. In this context, the expansion of Internet of things and knowledge discovery in databases techniques will drive changes in current microgrid planning processes. In this paper, technical literature is reviewed and this innovative approach to microgrid planning is introduced. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Microgrid planning knowledge discovery in databases Energy Management Systems Data Mining Machine Learning Sustainability

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Factor selection for delay analysis using knowledge discovery in databases

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AUTOMATION IN CONSTRUCTION 2008年第5期17卷 550-560页

作者： Kim, Hyunioo Soibelman, Lucio Grobler, Francois USA Engineer Res & Dev Ctr Champaign IL 61826 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

Today's construction project has become a very complex, high-risk, multiparty endeavor. Construction projects are composed of many interrelated elements of labor, cost, material, schedule, and other resources, making it difficult to discern which factors were the main causes for delay on a given project. Were all relevant factors to be considered, it would become an overwhelming task. On the other hand, it would be very difficult to know on which factors to focus if only a limited number of factors were to be considered. This paper presents a methodology for factor selection;identifying which factors in an on-going construction project contribute most to the experienced delays. Factor selection is defined as the process of finding relevant factors among a large set of original attributes with the objective of best representing the original dataset and utilizes knowledge discovery in databases (KDD), which is a data analysis process to discover useful knowledge in a large database. A specific construction project has been analyzed to identify main factors of construction delays through the process of statistical measurements and machine learning algorithms. (C) 2007 Elsevier B.V. All rights reserved.

关键词： knowledge discovery in databases construction delay statistics machine learning factor selection

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A human-centred design approach for developing dynamic decision support system based on knowledge discovery in databases

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JOURNAL OF DECISION SYSTEMS 2013年第2期22卷 69-96页

作者： Ltifi, Hela Kolski, Christophe Ben Ayed, Mounir Alimi, Adel M. Univ Sfax Natl Sch Engn ENIS REGIM Res Grp Intelligent Machines BP 1173 Sfax 3038 Tunisia Univ Lille Nord De France F-59000 Lille France UVHC LAMIH F-59313 Valenciennes France CNRS UMR 8201 F-59313 Valenciennes France

This paper presents a human-centred design approach for developing Decision Support Systems (DSS) based on a knowledge discovery in databases (KDD) process. The KDD process generates a set of software modules. Our approach is based on a critical study of design methods. It uses the Unified Process (UP), which proposes a general framework;however, the UP does not include enough Human-Computer Interaction (HCI) elements. We suggest enriching the UP activities from the HCI perspective, adding HCI elements. The proposed approach is applied to a KDD-based Dynamic Medical DSS.

关键词： software process Dynamic Decision Support Systems Human-centred Design knowledge discovery in databases Human-Computer Interaction

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The knowledge discovery in databases approach: identifying variables that influence ISO 9001 and ISO 14001 certifications

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JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT 2021年第7期64卷 1271-1290页

作者： Toporowicz, Flavia Zabloski de Souza, Jovani Taveira Piekarski, Cassiano Moro Univ Tecnol Fed Parana UTFPR Dept Prod Engn Ponta Grossa Parana Brazil Univ Tecnol Fed Parana UTFPR Postgrad Program Prod Engn PPGEP Sustainable Prod Syst Lab LESP Ponta Grossa Parana Brazil

With the rapid expansion of international trade, companies are progressively adopting management system standards that 'dazzle' the market, such as those in ISO 9001 and ISO 14001 certifications. However, the reports for ISO 9001 and ISO 14001 only provide the number of valid certifications, without presenting information on the variables that may influence an increase or difference in the number of these certifications. In response to this gap, this article aims to identify the main variables of the World Development Indicators (WDI) that influence the number of valid ISO 9001 and ISO 14001 certifications. The knowledge discovery in databases (KDD) approach was used to identify relevant indicators, with correlation analysis performed to validate the data. Our investigation shows influences that previous studies had not found: the number of certifications and scientific articles, passenger transport by railways and air, greenhouse gases, and methane and nitrous oxide emissions.

关键词： ISO 9001 ISO 14001 World Development Indicators knowledge discovery in databases correlation analysis

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A Comprehensive Methodology to Implement Business Intelligence and Analytics Through knowledge discovery in databases 1

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6th International Conference on Mining Intelligence and knowledge Exploration (MIKE)

作者： Belfo, Fernando Paulo Andreica, Alina Banca Polytech Inst Coimbra ISCAC Coimbra Business Sch Coimbra Portugal Babes Bolyai Univ Cluj Napoca Fac European Studies Cluj Napoca Romania

ISBN: (数字)9783030059187

ISBN: (纸本)9783030059187;9783030059170

Business intelligence is used by companies for analysing business information, providing not only historical or current views on business operations, but also providing predictions about the business. Consequently, knowledge discovery in databases can support the implementation of business intelligence solutions, especially in order to deal with the reality of big data, using diverse data mining techniques that can help to better prepare the data and to create improved models. The current paper proposes a methodology to implement business intelligence and analytics solutions, based on the CRISPDM methodology, where the application of simplification and equivalence algorithms in modelling data representations can be used for improving the process of business management. This promising approach can boost business intelligence and analytics by using alternative techniques for discovering and presenting new knowledge about the business. The application of simplification and equivalence algorithms within the business context enables finding the most comprehensive or relevant knowledge, represented for instance as association rules, and bringing a real competitive advantage for the stakeholders.

关键词： Business intelligence knowledge discovery in databases Data mining Equivalence algorithm Canonical representation

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A knowledge discovery in databases (KDD) APPROACH FOR EXTRACTING CAUSES OF ITERATIONS IN ENGINEERING CHANGE ORDERS

A KNOWLEDGE DISCOVERY IN DATABASES (KDD) APPROACH FOR EXTRAC...

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ASME International Design Engineering Technical Conferences / Computers and Information in Engineering Conference (IDETC/CIE)

作者： Elezi, Fatos Sharafi, Armin Mirson, Alexander Wolf, Petra Krcmar, Helmut Lindemann, Udo Tech Univ Munich Inst Prod Dev D-80290 Munich Germany

ISBN: (纸本)9780791854792

This paper describes an implementation of a knowledge discovery in databases (KDD) process for extracting the causes of iterations in Engineering Change Orders (ECOs). A data set of approximately 53,000 historical Engineering Change Orders (ECOs) was used for this purpose. Initially, the impact of iterations in ECO lead time and uncertainty is assessed. Subsequently, a semi-automatic text-mining process is employed to classibi the causes of iterations. As a result, cost and technical categories of causes were identified as the main reasons for the occurrence of iterations. The study concludes that applying KDD in historic ECO data can help in identifying the causes of iterations of ECO which subsequently can provide a framework for companies to reduce these iterations. In addition, the case represents an example of benefits that can be achieved with the application of KDD in engineering change management.

关键词： knowledge discovery in databases Engineering Change Management Engineering Change Orders

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