Relevant bibliographies by topics / KDD (Knowledge Discovery in Database)

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Academic literature on the topic 'KDD (Knowledge Discovery in Database)'

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Published: 3 June 2025
Last updated: 13 July 2025

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Journal articles on the topic "KDD (Knowledge Discovery in Database)"

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Chen, Po-Chi, Ru-Fang Hsueh, and Shu-Yuen Hwang. "An ILP Based Knowledge Discovery System." International Journal on Artificial Intelligence Tools 06, no. 01 (1997): 63–95. http://dx.doi.org/10.1142/s0218213097000050.

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Interest in research into knowledge discovery in databases (KDD) has been growing continuously because of the rapid increase in the amount of information embedded in real-world data. Several systems have been proposed for studying the KDD process. One main task in a KDD system is to learn important and user-interesting knowledge from a set of collected data. Most proposed systems use simple machine learning methods to learn the pattern. This may result in efficient performance but the discovery quality is less useful. In this paper, we propose a method to integrated a new and complicated machine learning method called inductive logic programming (ILP) to improve the KDD quality. Such integration shows how this new learning technique can be easily applied to a KDD system and how it can improve the representation of the discovery. In our system, we use user's queries to indicate the importance and interestingness of the target knowledge. The system has been implemented on a SUN workstation using the Sybase database system. Detailed examples are also provided to illustrate the benefit of integrating the ILP technique with the KDD system.
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Dr., Mohammad Shahid, and Sunil Gupta Dr. "A Clustering Based Approach for knowledge discovery on web." NIET Journal of Engineering & Technology (NIETJET) 10, no. 02 (2023): 001–4. https://doi.org/10.5281/zenodo.7578370.

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Abstract: In many fields, such as industry, commerce, government, and education, knowledge discovery and data mining can be immensely valuable to the subject of Artificial Intelligence. Because of the recent increase in demand for KDD techniques, such as those used in machine learning, databases, statistics, knowledge acquisition, data visualisation, and high performance computing, knowledge discovery and data mining have grown in importance. By employing standard formulas for computational correlations, we hope to create an integrated technique that can be used to filter web world social information and find parallels between similar tastes of diverse user information in a variety of settings
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Jantan, Hamidah, Abdul Razak Hamdan, and Zulaiha Ali Othman. "Managing talent in human resource : a Knowledge Discovery in Database (KDD) approach." Social and Management Research Journal 6, no. 1 (2009): 51. http://dx.doi.org/10.24191/smrj.v6i1.5169.

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In any organization, managing human talent is very important and need more attentions from Human Resource (HR) professionals. Nowadays, among the challenges of HR professionals is to manage an organization’s talent, especially to ensure the right person is assigned to the right job at the right time. Knowledge Discovery in Database (KDD) is a data analysis approach that is commonly used for classification and prediction; and this approach has been widely used in many fields such as manufacturing, development, finance and etc. However, this approach has not attracted people in human resource especially for talent management. For this reason, this paper presents an overview of some talent management problems that can be solved by using KDD approach. In this study, we attempt to implement one of the talent management tasks i.e. identifying potential talent by predicting their performance. The employee’s performance can be predicted based on the past experience knowledge which is discovered from existing databases. Finally, this paper proposes the suggested framework for talent management using KDD approach.
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Piatetsky-Shapiro, Gregory. "Knowledge discovery in databases: Progress report." Knowledge Engineering Review 9, no. 1 (1994): 57–60. http://dx.doi.org/10.1017/s0269888900006573.

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As the number and size of very large databases continues to grow rapidly, so does the need to make sense of them. This need is addressed by the field called knowledge Discovery in Databases (KDD), which combines approaches from machine learning, statistics, intelligent databases, and knowledge acquisition. KDD encompasses a number of different discovery methods, such as clustering, data summarization, learning classification rules, finding dependency networks, analysing changes, and detecting anomalies (Matheus et at., 1993).
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Mishra, Divya, and Ravindra Kumar. "Knowledge Discovery in Databases (KDD): A Comparative Evaluation of Scientific Databases." Asian Journal of Information Science and Technology 7, no. 2 (2017): 28–30. http://dx.doi.org/10.51983/ajist-2017.7.2.154.

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In this information explosion age, a large number of commercial and free online database provided by publishers of information resources is available on web, Libraries of every kind offering various services regarding use of online resources and services to fulfill the information requirements of a large group of users. The present study comparatively analyze the selected databases which aims to serves a scientific community. The Library Science and information personnel all over the world are focusing more and more on development of better, user friendly and affordable discovery solutions to fulfill the requirements of patrons.
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Rahman, Fauziah Abdul, Muhammad Ishak Desa, Antoni Wibowo, and Norhaidah A. Haris. "An Improvement of Knowledge Discovery Database (KDD) Framework for Effective Decision." Journal of Artificial Intelligence 9, no. 4 (2016): 72–77. http://dx.doi.org/10.3923/jai.2016.72.77.

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Hao, Wu. "On Business-Oriented Knowledge Discovery and Data Mining." Advanced Materials Research 760-762 (September 2013): 2267–71. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.2267.

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This paper will discuss issues in data mining and business processes including Marketing, Finance and Health. In turn, the use of KDD in the complex real-world databases in business and government will push the IT researchers to identify and solve cutting-edge problems in KDD modelling, techniques and processes. From IT perspectives, some issues in economic sciences consist of business modelling and mining, aberrant behavior detection, and health economics. Some issues in KDD include data mining for complex data structures and complex modelling. These novel strategies will be integrated to build a one-stop KDD system.
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Köster, Frank, and Marco Grawunder. "Eine Anwendung von Knowledge Discovery in Databases im eLearning (An Application of Knowledge Discovery in Databases in eLearning)." i-com 2, no. 2/2003 (2003): 21–28. http://dx.doi.org/10.1524/icom.2.2.21.19590.

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ZusammenfassungDieser Artikel behandelt die Entwicklung eines Assistenzsystems für Nutzer elektronischer Lehr-/Lernmaterialien (eLLM). Dabei wird das simulatorbasierte Pilotentraining als konkrete Beispielanwendung betrachtet. In diesem Kontext wird insbesondere die mögliche Isolation von Nutzern eLLM als Problem hervorgehoben. Arbeiten zu Tutoriellen Systemen, Virtuellen Lerngemeinschaften, Lernarrangements o.Ä. diskutieren ein facettenreiches Instrumentarium zur Behandlung dieses Problems und prägen eben-so unseren Ansatz. Dieser zielt darauf ab, eine tutorielle Unterstützung sowie Aufgaben zur Bildung/Festigung virtueller Lerngemeinschaften an spezielle Softwarekomponenten (Agenten) zu delegieren, die den Nutzern uneingeschränkt als Assistenten zur Verfügung stehen. Bei der Implementierung der Verhaltensgrundlage dieser Agenten verfolgen wir einen datengetriebenen Zugang, wobei Methoden des Knowledge Discovery in Databases (KDD) zur Anwendung kommen. Die in diesem Zusammenhang erzielten Ergebnisse stellen den Schwerpunkt dieses Artikels dar. Sie umfassen Werkzeuge zum KDD sowie Resultate ihrer Anwendung.
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YANG, BINGRU, JIANGTAO SHEN, and WEI SONG. "KDK BASED DOUBLE-BASIS FUSION MECHANISM AND ITS PROCESS MODEL." International Journal on Artificial Intelligence Tools 14, no. 03 (2005): 399–423. http://dx.doi.org/10.1142/s021821300500217x.

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Knowledge Discovery in Knowledge Base (KDK) opens new horizons for research. KDK and KDD (Knowledge Discovery in Database) are the different cognitive field and discovery process. In most people's view, they are independent each other. In this paper we can summarize the following tasks: Firstly, we discussed that two kinds of the process model and mining algorithm of KDK based on facts and rules in knowledge base. Secondly, we proves that the inherent relation between KDD and KDK (i.e. double-basis fusion mechanism). Thirdly, we gained the new process model and implementation technology of KDK*. Finally, the imitation experimentation proved that the validity of above mechanism and process model.
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N., Thinaharan, Chitradevi B., Malathi P., and Kalpana K. "A LITERATURE SURVEY ON DATA MINING TECHNIQUES AND CONCEPTS." International Journal of Engineering Research and Modern Education 3, no. 2 (2018): 1–3. https://doi.org/10.5281/zenodo.1332042.

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Data mining is a multidisciplinary field, drawing work from areas including database technology, machine learning, statistics, pattern recognition, information retrieval, neural networks, knowledge-based systems, artificial intelligence, high-performance computing, and data visualization. Data mining is the process of analyzing data from different views and summarizing it into useful data. “Data mining, also popularly referred to as knowledge discovery from data (KDD), is the automated or convenient extraction of patterns representing knowledge implicitly stored or captured in large databases, data warehouses, the Web, other massive information repositories or data streams.”.
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Dissertations / Theses on the topic "KDD (Knowledge Discovery in Database)"

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Storti, Emanuele. "KDD process design in collaborative and distributed environments." Doctoral thesis, Università Politecnica delle Marche, 2012. http://hdl.handle.net/11566/242061.

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Il termine Knowledge Discovery in Databases (KDD) si riferisce al processo di scoperta di conoscenza all'interno di grandi volumi di dati, per mezzo di specifici algoritmi. L'applicazione di tali tecniche a contesti organizzativi reali risulta oggi ancora limitata, principalmente a causa della complessità nella configurazione degli algoritmi di analisi dei dati e nella difficoltà nella gestione/esecuzione dei processi di KDD, che impone spesso di far riferimento a contesti di computazione distribuita ed alla interazione tra diversi utenti, tra i quali specialisti con competenze tecniche ed esperti nello specifico dominio oggetto dell'analisi. In questo lavoro viene presentata Knowledge Discovery in Database Virtual Mart (KDDVM), una piattaforma orientata a supportare utenti con diversi livelli di esperienza nella progettazione di processi di KDD in ambito collaborativo e distribuito. La piattaforma si basa su un'architettura aperta, modulare, estensibile ed orientata ai servizi, nella quale vengono messe a disposizione funzionalità di preprocessing, modellazione e postprocessing. In KDDVM, tutte le risorse coinvolte in un processo, comprese le applicazioni, i dati e gli utenti, vengono rappresentate sistematicamente per mezzo di tecnologie semantiche, a vari livelli di astrazione. In tal modo è possibile approcciare il processo di estrazione della conoscenza in modo innovativo, fornendo un supporto più efficace ad utenti non esperti nell'esecuzione di attività complesse. Tra di essi sono disponibili funzionalità per il deployment di tool eterogenei, per la ricerca sintattica e semantica, all'interno di repository, di servizi che corrispondono a determinati requisiti, per il supporto intelligente alla composizione semi-automatica di processi, nonché strumenti capaci di supportare più utenti distribuiti, in un'ottica collaborativa, nella progettazione condivisa di un processo di KDD.<br>Knowledge Discovery in Databases (KDD), as well as scientific experimentation in e-Science, is a complex and computationally intensive process aimed at gaining knowledge from a huge set of data. Often performed in distributed settings, KDD projects usually involve a deep interaction among heterogeneous tools and several users with specific expertise. Given the high complexity of the process, such users need effective support to achieve their goal of knowledge extraction. This work presents the Knowledge Discovery in Database Virtual Mart (KDDVM), a user- and knowledge-centric framework aimed at supporting the design of KDD processes in a highly distributed and collaborative scenario, in which computational resources and actors dynamically interoperate to share and elaborate knowledge. The contribution of the work is two-fold: firstly, a conceptual systematization of the relevant knowledge is provided, with the aim to formalize, through semantic technologies, each element taking part in the design and execution of a KDD process, including computational resources, data and actors; secondly, we propose an implementation of the framework as an open, modular and extensible Service-Oriented platform, in which several services are available both to perform basic operations of data manipulations and to support more advanced functionalities. Among them, the management of deployment/activation of computational resources, service discovery and their composition to build KDD processes. Since the cooperative design and execution of a distributed KDD process typically require several skills, both technical and managerial, collaboration can easily become a source of complexity if not supported by any kind of coordination. For such reasons, a set of functionalities of the platform is specifically addressed to support collaboration within a distributed team, by providing an environment in which users can work on the same project and share processes, results and ideas.
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Storopoli, José Eduardo. "O uso do Knowledge Discovery in Database (KDD) de informações patentárias sobre ensino a distância: contribuições para instituições de ensino superior." Universidade Nove de Julho, 2016. http://bibliotecatede.uninove.br/handle/tede/1517.

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Submitted by Nadir Basilio (nadirsb@uninove.br) on 2016-09-01T19:37:54Z No. of bitstreams: 1 José Eduardo Storopoli.pdf: 3248722 bytes, checksum: c6f49ec5728d3ca3b10f36aa03c94865 (MD5)<br>Made available in DSpace on 2016-09-01T19:37:54Z (GMT). No. of bitstreams: 1 José Eduardo Storopoli.pdf: 3248722 bytes, checksum: c6f49ec5728d3ca3b10f36aa03c94865 (MD5) Previous issue date: 2016-04-14<br>Distance learning (DL) has a long history of success and failures, and has existed for at least since the end of the XVIII century. Higher education DL began in Brazil during 1994, having the expansion of the internet as the main factor. The search of innovations and new models related to the process of DL has become critical, both from the operational and strategic aspect. Regarding those challenges, the available information in patent databases can contrive add to, in an important manner, the design of DL strategies in higher education institutions (HIE), therefore, the thesis’ objective is: to analyze the employment of Knowledge Discovery in Database (KDD) in patent information and its main contributions to DL in HIE. The method employed was the KDD structure to discovery, analysis, selection, pre-processing, filtering, transformation, data mining, interpretation and assessment of patent information data from the European Patent Office’s (EPO) database, composed of 90 million documents. The data collection was based on a sample of patents acquired through enhanced search expressions, by the crawler software Patent2Netv.2. The data of 3.090 patents were analyzed by dynamic tables, network analysis, mindmaps, content analysis and clustering. The main results: (1) provided the diagnosis of patents related to DL in a global perspective; (2) developed a methodology for the use of KDD to analyze the content of DL patent information to HIE; (3) mapping of DL patents from HIE; and, ultimately, (4) assessment the use of patent information in order to formulate strategies on adopting DL in HIE, in the light of the Resouce-based View.<br>O ensino a distância tem uma longa história de sucessos e fracassos, existe pelo menos desde o final do século XVIII. O ensino superior a distância iniciou no Brasil em meados de 1994, tendo como principal fator a expansão da internet. A busca de inovações e novos modelos relacionados ao processo de ensino a distância (EAD) torna-se importante, tanto do aspecto operacional como estratégico. Face a estes desafios, as informações existentes nos bancos de dados de patentes podem contribuir de forma significativa para a definição de estratégias de EAD em instituições de ensino superior (IES), portanto, o objetivo da tese foi analisar o uso do Knowledge Discovery in Database (KDD) de informações patentárias e suas possíveis contribuições para o EAD em IES. A metodologia utilizada foi a estrutura do KDD para exploração, análise, seleção, pré-processamento, limpeza, transformação, data mining, interpretação e avaliação de dados de informações patentárias sobre EAD da base do European Patent Office (EPO) que possui aproximadamente 90 milhões de documentos. A coleta dos dados utilizou-se o emprego de data mining por meio do software crawler Patent2Netv.2. A amostra de patentes adquiridas com o uso de aprimoradas expressões de busca resultou em 3.090 patentes, que foram analisadas por meio de tabelas dinâmicas, análises de rede, mapas mentais, análise de conteúdo e clustering. Os principais resultados: (1) possibilitaram apresentar o diagnóstico sobre as patentes relacionadas a EAD no mundo; (2) o desenvolvimento de uma metodologia de uso do KDD para análise de conteúdo de informações patentearias em EAD para IES; (3) o mapeamento das patentes em EAD em Universidades; e, finalmente, (4) a avaliação do uso de informações patentárias e sua utilização na definição de estratégias de adoção de EAD em IES, à luz do Visão Baseada em Recursos.
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Huynh, Xuan-Hiep. "Interestingness Measures for Association Rules in a KDD Process : PostProcessing of Rules with ARQAT Tool." Phd thesis, Université de Nantes, 2006. http://tel.archives-ouvertes.fr/tel-00482649.

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This work takes place in the framework of Knowledge Discovery in Databases (KDD), often called "Data Mining". This domain is both a main research topic and an application ¯eld in companies. KDD aims at discovering previously unknown and useful knowledge in large databases. In the last decade many researches have been published about association rules, which are frequently used in data mining. Association rules, which are implicative tendencies in data, have the advantage to be an unsupervised model. But, in counter part, they often deliver a large number of rules. As a consequence, a postprocessing task is required by the user to help him understand the results. One way to reduce the number of rules - to validate or to select the most interesting ones - is to use interestingness measures adapted to both his/her goals and the dataset studied. Selecting the right interestingness measures is an open problem in KDD. A lot of measures have been proposed to extract the knowledge from large databases and many authors have introduced the interestingness properties for selecting a suitable measure for a given application. Some measures are adequate for some applications but the others are not. In our thesis, we propose to study the set of interestingness measure available in the literature, in order to evaluate their behavior according to the nature of data and the preferences of the user. The ¯nal objective is to guide the user's choice towards the measures best adapted to its needs and in ¯ne to select the most interesting rules. For this purpose, we propose a new approach implemented in a new tool, ARQAT (Association Rule Quality Analysis Tool), in order to facilitate the analysis of the behavior about 40 interest- ingness measures. In addition to elementary statistics, the tool allows a thorough analysis of the correlations between measures using correlation graphs based on the coe±cients suggested by Pear- son, Spearman and Kendall. These graphs are also used to identify the clusters of similar measures. Moreover, we proposed a series of comparative studies on the correlations between interestingness measures on several datasets. We discovered a set of correlations not very sensitive to the nature of the data used, and which we called stable correlations. Finally, 14 graphical and complementary views structured on 5 levels of analysis: ruleset anal- ysis, correlation and clustering analysis, most interesting rules analysis, sensitivity analysis, and comparative analysis are illustrated in order to show the interest of both the exploratory approach and the use of complementary views.
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Ribeiro, Lamark dos Santos. "Uma abordagem semântica para seleção de atributos no processo de KDD." Universidade Federal da Paraí­ba, 2010. http://tede.biblioteca.ufpb.br:8080/handle/tede/6048.

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Made available in DSpace on 2015-05-14T12:36:27Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 2925122 bytes, checksum: e65ad4a8f7ca12fb8a90eaf2a8783d65 (MD5) Previous issue date: 2010-08-27<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>Currently, two issues of great importance for the computation are being used together in an increasingly apparent: a Knowledge Discovery in Databases (KDD) and Ontologies. By developing the ways in which data is stored, the amount of information available for analysis has increased exponentially, making it necessary techniques to analyze data and gain knowledge for different purposes. In this sense, the KDD process introduces stages that enable the discovery of useful knowledge, and new features that usually cannot be seen only by viewing the data in raw form. In a complementary field, the Knowledge Discovery can be benefited with Ontologies. These, in a sense, have the capacity to store the "knowledge" about certain areas. The knowledge that can be retrieved through inference classes, descriptions, properties and constraints. Phases existing in the process of knowledge discovery, the selection of attributes allows the area of analysis for data mining algorithms can be improved with attributes more relevant to the problem analyzed. But sometimes these screening methods do not eliminate the attributes satisfactorily, do allow a preliminary analysis on the area treated. To address this problem this paper proposes a system that uses ontologies to store the prior knowledge about a specific domain, enabling a semantic analysis previously not possible using conventional methodologies. Was elaborated an ontology, with reuse of various repositories of ontologies available on the Web, specific to the medical field with a possible common specifications in key areas of medicine. To introduce semantics in the selection of attributes is first performed the mapping between data base attributes and classes of the ontology. Done this mapping, the user can now select attributes by semantic categories, reducing the dimensionality of the data and view redundancies between semantically related attributes.<br>Atualmente, dois temas de grande importância para a computação, estão sendo utilizados conjuntamente de uma forma cada vez mais aparente: a Descoberta de Conhecimento em Bancos de Dados (Knowledge Discovery in Databases KDD) e as Ontologias. Com o aperfeiçoamento das formas com que os dados são armazenados, a quantidade de informação disponível para análise aumentou exponencialmente, tornando necessário técnicas para analisar esses dados e obter conhecimento para os mais diversos propósitos. Nesse contexto, o processo de KDD introduz etapas que possibilitam a descoberta de conhecimentos úteis, novos e com características que geralmente não podiam ser vistas apenas visualizando os dados de forma bruta. Em um campo complementar, a Descoberta de Conhecimento em Banco de Dados pode ser beneficiada com Ontologias. Essas, de certa forma, apresentam a capacidade para armazenar o conhecimento , segundo um modelo de alta expressividade semântica, sobre determinados domínios. As ontologias permitem que o conhecimento seja recuperado através de inferências nas classes, descrições, propriedades e restrições. Nas fases existentes no processo de descoberta do conhecimento, a Seleção de Atributos permite que o espaço de análise para os algoritmos de Mineração de Dados possa ser melhorado com atributos mais relevantes para o problema analisado. Porém, algumas vezes esses métodos de seleção não eliminam de forma satisfatória os atributos irrelevantes, pois não permitem uma análise prévia sobre o domínio tratado. Para tratar esse problema, esse trabalho propõe um sistema que utiliza ontologias para armazenar o conhecimento prévio sobre um domínio específico, possibilitando uma análise semântica antes não viável pelas metodologias convencionais. Foi elaborada uma ontologia, com reuso de diversos repositórios de ontologias disponíveis na Web, específica para o domínio médico e com possíveis especificações comuns nas principais áreas da medicina. Para introduzir semântica no processo de seleção de atributos primeiro é realizado o mapeamento entre os atributos do banco de dados e as classes da ontologia. Feito esse mapeamento, o usuário agora pode selecionar atributos através de categorias semânticas, reduzir a dimensionalidade dos dados e ainda visualizar redundâncias existentes entre atributos correlacionados semanticamente.
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Scarinci, Rui Gureghian. "SES : sistema de extração semântica de informações." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 1997. http://hdl.handle.net/10183/18398.

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Entre as áreas que mais se desenvolvem na informática nos últimos anos estão aquelas relacionadas ao crescimento da rede Internet, que interliga milhões de usuários de todo o mundo. Esta rede disponibiliza aos usuários uma a enorme variedade e quantidade de informações, principalmente dados armazenados de forma não estruturada ou semi estruturada. Contudo, tal volume e heterogeneidade acaba dificultando a manipulação dos dados recuperados a partir da Internet. Este problema motivou o desenvolvimento deste trabalho. Mesmo com o auxílio de várias ferramentas de pesquisa na Internet, buscando realizar pesquisas sobre assuntos específicos, o usuário ainda tem que manipular em seu computador pessoal uma grande quantidade de informação, pois estas ferramentas não realizam um processo de seleção detalhado. Ou seja, são recuperados muitos dados não interessantes ao usuário. Existe, também, uma grande diversidade de assuntos e padrões de transferência e armazenamento da informação criando os mais heterogêneos ambientes de pesquisa e consulta de dados. Esta heterogeneidade faz com que o usuário da rede deva conhecer todo um conjunto de padrões e ferramentas a fim de obter a informação desejada. No entanto, a maior dificuldade de manipulação esta ligada aos formatos de armazenamento não estruturados ou pouco estruturados, como, por exemplo: arquivos textos, Mails (correspondência eletrônica) e artigos de News (jornais eletrônicos). Nestes formatos, o entendimento do documento exige a leitura do mesmo pelo usuário, o que muitas vezes acarreta em um gasto de tempo desnecessário, pois o documento, por exemplo, pode não ser de interesse deste ou, então, ser de interesse, mas sua leitura completa só seria útil posteriormente. Várias informações, como chamadas de trabalhos para congressos, preços de produtos e estatísticas econômicas, entre outras, apresentam validade temporal. Outras informações são atualizadas periodicamente. Muitas dessas características temporais são explicitas, outras estão implícitas no meio de outros tipos de dados. Isto torna muito difícil a recuperação de tal tipo de informação, gerando, várias vezes, a utilização de informações desatualizadas, ou a perda de oportunidades. Desta forma, o grande volume de dados em arquivos pessoais obtidos a partir da Internet criou uma complexa tarefa de gerenciamento dos mesmos em conseqüência da natureza não estruturada dos documentos recuperados e da complexidade da análise do tempo de validade inerente a estes dados. Com o objetivo de satisfazer as necessidades de seleção e conseqüente manipulação das informações existentes a nível local (computador pessoal), neste trabalho, é descrito um sistema para extração e sumarização destes dados, utilizando conceitos de IE (Information Extraction) e Sistemas Baseados em Conhecimento. Os dados processados são parcialmente estruturados ou não estruturados, sendo manipulados por um extrator configurado a partir de bases de conhecimento geradas pelo usuário do sistema. O objetivo final desta dissertação é a implementação do Sistema de Extração Semântica de Informações, o qual permite a classificação dos dados extraídos em classes significativas para o usuário e a determinação da validade temporal destes dados a partir da geração de uma base de dados estruturada.<br>One of the most challenging area in Computer Science is related to Internet technology. This network offers to the users a large variety and amount of information, mainly, data storage in unstructured or semi-structured formats. However, the vast data volume and heterogeneity transforms the retrieved data manipulation a very arduous work. This problem was the prime motivation of this work. As with many tools for data retrieval and specific searching, the user has to manipulate in his personal computer an increasing amount of information, because these tools do not realize a precise data selection process. Many retrieval data are not interesting for the user. There are, also, a big diversity of subjects and standards in information transmission and storage, creating the most heterogeneous environments in data searching and retrieval. Due to this heterogeneity, the user has to know many data standards and searching tools to obtain the requested information. However, the fundamental problem for data manipulation is the partially or fully unstructured data formats, as text, mail and news data structures. For files in these formats, the user has to read each of the files to filter the relevant information, originating a loss of time, because the document could be not interesting for the user, or if it is interesting, its complete reading may be unnecessary at the moment. Some information as call-for-papers, product prices, economic statistics and others, has associated a temporal validity. Other information are updated periodically. Some of these temporal characteristics are explicit, others are implicitly embedded in other data types. As it is very difficult to retrieve the temporal data automatically, which generate, many times, the use of invalid information, as a result, some opportunities are lost. On this paper a system for extraction and summarizing of data is described. The main objective is to satisfy the user's selection needs and consequently information manipulation stored in a personal computer. To achieve this goal we are employed the concepts of Information Extraction (IE) and Knowledge Based Systems. The input data manipulation is done by an extraction procedure configured by a user who defined knowledge base. The objective of this paper is to develop a System of Semantic Extraction of Information which classifies the data extracted in meaningful classes for the user and to deduce the temporal validity of this data. This goal was achieved by the generation of a structured temporal data base.
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Oliveira, Robson Butaca Taborelli de. "O processo de extração de conhecimento de base de dados apoiado por agentes de software." Universidade de São Paulo, 2000. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-23092001-231242/.

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Os sistemas de aplicações científicas e comerciais geram, cada vez mais, imensas quantidades de dados os quais dificilmente podem ser analisados sem que sejam usados técnicas e ferramentas adequadas de análise. Além disso, muitas destas aplicações são voltadas para Internet, ou seja, possuem seus dados distribuídos, o que dificulta ainda mais a realização de tarefas como a coleta de dados. A área de Extração de Conhecimento de Base de Dados diz respeito às técnicas e ferramentas usadas para descobrir automaticamente conhecimento embutido nos dados. Num ambiente de rede de computadores, é mais complicado realizar algumas das etapas do processo de KDD, como a coleta e processamento de dados. Dessa forma, pode ser feita a utilização de novas tecnologias na tentativa de auxiliar a execução do processo de descoberta de conhecimento. Os agentes de software são programas de computadores com propriedades, como, autonomia, reatividade e mobilidade, que podem ser utilizados para esta finalidade. Neste sentido, o objetivo deste trabalho é apresentar a proposta de um sistema multi-agente, chamado Minador, para auxiliar na execução e gerenciamento do processo de Extração de Conhecimento de Base de Dados.<br>Nowadays, commercial and scientific application systems generate huge amounts of data that cannot be easily analyzed without the use of appropriate tools and techniques. A great number of these applications are also based on the Internet which makes it even more difficult to collect data, for instance. The field of Computer Science called Knowledge Discovery in Databases deals with issues of the use and creation of the tools and techniques that allow for the automatic discovery of knowledge from data. Applying these techniques in an Internet environment can be particulary difficult. Thus, new techniques need to be used in order to aid the knowledge discovery process. Software agents are computer programs with properties such as autonomy, reactivity and mobility that can be used in this way. In this context, this work has the main goal of presenting the proposal of a multiagent system, called Minador, aimed at supporting the execution and management of the Knowledge Discovery in Databases process.
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Moretti, Caio Benatti. "Análise de grandezas cinemáticas e dinâmicas inerentes à hemiparesia através da descoberta de conhecimento em bases de dados." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/18/18149/tde-13062016-184240/.

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Em virtude de uma elevada expectativa de vida mundial, faz-se crescente a probabilidade de ocorrer acidentes naturais e traumas físicos no cotidiano, o que ocasiona um aumento na demanda por reabilitação. A terapia física, sob o paradigma da reabilitação robótica com serious games, oferece maior motivação e engajamento do paciente ao tratamento, cujo emprego foi recomendado pela American Heart Association (AHA), apontando a mais alta avaliação (Level A) para pacientes internados e ambulatoriais. No entanto, o potencial de análise dos dados coletados pelos dispositivos robóticos envolvidos é pouco explorado, deixando de extrair informações que podem ser de grande valia para os tratamentos. O foco deste trabalho consiste na aplicação de técnicas para descoberta de conhecimento, classificando o desempenho de pacientes diagnosticados com hemiparesia crônica. Os pacientes foram inseridos em um ambiente de reabilitação robótica, fazendo uso do InMotion ARM, um dispositivo robótico para reabilitação de membros superiores e coleta dos dados de desempenho. Foi aplicado sobre os dados um roteiro para descoberta de conhecimento em bases de dados, desempenhando pré-processamento, transformação (extração de características) e então a mineração de dados a partir de algoritmos de aprendizado de máquina. A estratégia do presente trabalho culminou em uma classificação de padrões com a capacidade de distinguir lados hemiparéticos sob uma precisão de 94%, havendo oito atributos alimentando a entrada do mecanismo obtido. Interpretando esta coleção de atributos, foi observado que dados de força são mais significativos, os quais abrangem metade da composição de uma amostra.<br>As a result of a higher life expectancy, the high probability of natural accidents and traumas occurences entails an increasing need for rehabilitation. Physical therapy, under the robotic rehabilitation paradigm with serious games, offers the patient better motivation and engagement to the treatment, being a method recommended by American Heart Association (AHA), pointing the highest assessment (Level A) for inpatients and outpatients. However, the rich potential of the data analysis provided by robotic devices is poorly exploited, discarding the opportunity to aggregate valuable information to treatments. The aim of this work consists of applying knowledge discovery techniques by classifying the performance of patients diagnosed with chronic hemiparesis. The patients, inserted into a robotic rehabilitation environment, exercised with the InMotion ARM, a robotic device for upper-limb rehabilitation which also does the collection of performance data. A Knowledge Discovery roadmap was applied over collected data in order to preprocess, transform and perform data mining through machine learning methods. The strategy of this work culminated in a pattern classification with the abilty to distinguish hemiparetic sides with an accuracy rate of 94%, having eight attributes feeding the input of the obtained mechanism. The interpretation of these attributes has shown that force-related data are more significant, comprising half of the composition of a sample.
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Schneider, Luís Felipe. "Aplicação do processo de descoberta de conhecimento em dados do poder judiciário do estado do Rio Grande do Sul." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2003. http://hdl.handle.net/10183/8968.

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Para explorar as relações existentes entre os dados abriu-se espaço para a procura de conhecimento e informações úteis não conhecidas, a partir de grandes conjuntos de dados armazenados. A este campo deu-se o nome de Descoberta de Conhecimento em Base de Dados (DCBD), o qual foi formalizado em 1989. O DCBD é composto por um processo de etapas ou fases, de natureza iterativa e interativa. Este trabalho baseou-se na metodologia CRISP-DM . Independente da metodologia empregada, este processo tem uma fase que pode ser considerada o núcleo da DCBD, a “mineração de dados” (ou modelagem conforme CRISP-DM), a qual está associado o conceito “classe de tipo de problema”, bem como as técnicas e algoritmos que podem ser empregados em uma aplicação de DCBD. Destacaremos as classes associação e agrupamento, as técnicas associadas a estas classes, e os algoritmos Apriori e K-médias. Toda esta contextualização estará compreendida na ferramenta de mineração de dados escolhida, Weka (Waikato Environment for Knowledge Analysis). O plano de pesquisa está centrado em aplicar o processo de DCBD no Poder Judiciário no que se refere a sua atividade fim, julgamentos de processos, procurando por descobertas a partir da influência da classificação processual em relação à incidência de processos, ao tempo de tramitação, aos tipos de sentenças proferidas e a presença da audiência. Também, será explorada a procura por perfis de réus, nos processos criminais, segundo características como sexo, estado civil, grau de instrução, profissão e raça. O trabalho apresenta nos capítulos 2 e 3 o embasamento teórico de DCBC, detalhando a metodologia CRISP-DM. No capítulo 4 explora-se toda a aplicação realizada nos dados do Poder Judiciário e por fim, no capítulo 5, são apresentadas as conclusões.<br>With the purpose of exploring existing connections among data, a space has been created for the search of Knowledge an useful unknown information based on large sets of stored data. This field was dubbed Knowledge Discovery in Databases (KDD) and it was formalized in 1989. The KDD consists of a process made up of iterative and interactive stages or phases. This work was based on the CRISP-DM methodology. Regardless of the methodology used, this process features a phase that may be considered as the nucleus of KDD, the “data mining” (or modeling according to CRISP-DM) which is associated with the task, as well as the techniques and algorithms that may be employed in an application of KDD. What will be highlighted in this study is affinity grouping and clustering, techniques associated with these tasks and Apriori and K-means algorithms. All this contextualization will be embodied in the selected data mining tool, Weka (Waikato Environment for Knowledge Analysis). The research plan focuses on the application of the KDD process in the Judiciary Power regarding its related activity, court proceedings, seeking findings based on the influence of the procedural classification concerning the incidence of proceedings, the proceduring time, the kind of sentences pronounced and hearing attendance. Also, the search for defendants’ profiles in criminal proceedings such as sex, marital status, education background, professional and race. In chapters 2 and 3, the study presents the theoretical grounds of KDD, explaining the CRISP-DM methodology. Chapter 4 explores all the application preformed in the data of the Judiciary Power, and lastly, in Chapter conclusions are drawn
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Howard, Craig M. "Tools and techniques for knowledge discovery." Thesis, University of East Anglia, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368357.

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Kerdprasop, Kittisak. "Active Database Rule Set Reduction by Knowledge Discovery." NSUWorks, 1999. http://nsuworks.nova.edu/gscis_etd/631.

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The advent of active databases enhances the functionality of conventional passive databases. A large number of applications benefit from the active database systems because of the provision of the powerful active rule language and rule processing algorithm. With the power of active rules, data manipulation operations can be executed automatically when certain events occur and certain conditions are satisfied. Active rules can also impose unique and consistent constraints on the database, independent of the applications, such that no application can violate. The additional database functionality offered by active rules, however, comes at a price. It is not a straightforward task for database designers to define and maintain a large set of active rules. Moreover, the termination property of an active rule set is difficult to detect because of the subtle interactions among active rules. This dissertation has proposed a novel approach of applying machine learning techniques to discover a set of newly simplified active rules. The termination property of the discovered active rule set is also guaranteed via the stratification technique. The approach of discovering active rules is proposed in the context of relational active databases. It is an attempt to assist database designers by providing the facility to analyze and refine active rules at designing time. The main algorithm of active rule discovery is called the ARD algorithm. The usefulness of the algorithm was verified by the actual running on sample sets of active rules. The running results, which were these corresponding new sets of active rules, will be analyzed on the basis of the size and the complexity of the discovered rule sets. The size of the discovered rule set was analyzed in term of the number of active rules. The complexity was analyzed in term of the number of transition states, which are the changes in the database states as the result of rule execution. The experimental results revealed that with the proposed approach, the numbers of active rules and transition states could be reduced 61.11 % and 40%, respectively, on average.
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Books on the topic "KDD (Knowledge Discovery in Database)"

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Hongjun, Lu, Motoda Hiroshi, and Liu Huan 1958-, eds. KDD, techniques and applications: Proceedings of the First Pacific-Asia Conference on Knowledge Discovery and Data Mining, 23-24 Feb. 97. World Scientific, 1997.

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International Conference on Knowledge Discovery & Data Mining (9th 2003 Washington, D.C.). KDD-2003: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 24-27, 2003, Washington, DC, USA. Association for Computing Machinery, 2003.

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International Conference on Knowledge Discovery & Data Mining (10th 2004 Seattle, Wash.). KDD-2004: Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : August 22-25, 2004, Seattle, Washington, USA. ACM Press, 2004.

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International Conference on Knowledge Discovery & Data Mining (9th 2003 Washington, D.C.). KDD-2003: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 24-27, 2003, Washington, DC, USA. Association for Computing Machinery, 2003.

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International Conference on Knowledge Discovery & Data Mining (11th 2005 Chicago, Ill). KDD-2005: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : August 21-24, 2005, Chicago, Illinois, USA. ACM Press, 2005.

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International Conference on Knowledge Discovery & Data Mining (8th 2002 Edmonton, Alta.). KDD-2002: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : July 23-36, 2002, Edmonton, Alberta, Canada. ACM, 2002.

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International Conference on Knowledge Discovery & Data Mining (7th 2002 San Francisco, Calif.). KDD-2001: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 26-29, 2001, San Francisco, CA, USA. Association for Computing Machinery, 2001.

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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ((7th 2001 San Francisco, Calif.). KDD-2001: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : August 26-29, 2001, San Francisco, CA. Association for Computing Machinery, 2001.

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Gaber, Mohamed Medhat. Knowledge Discovery from Sensor Data: Second International Workshop, Sensor-KDD 2008, Las Vegas, NV, USA, August 24-27, 2008, Revised Selected Papers. Springer-Verlag Berlin Heidelberg, 2010.

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Holzinger, Andreas. Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data: Third International Workshop, HCI-KDD 2013, Held at SouthCHI 2013, Maribor, Slovenia, July 1-3, 2013. Proceedings. Springer Berlin Heidelberg, 2013.

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Book chapters on the topic "KDD (Knowledge Discovery in Database)"

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Boulicaut, Jean-François, Mika Klemettinen, and Heikki Mannila. "Modeling KDD Processes within the Inductive Database Framework." In DataWarehousing and Knowledge Discovery. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48298-9_31.

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Ruggieri, Salvatore, and Franco Turini. "A KDD Process for Discrimination Discovery." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46131-1_28.

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Grabowski, Hans, Ralf-Stefan Lossack, and Jörg Weißkopf. "Automatic Classification and Creation of Classification Systems Using Methodologies of “Knowledge Discovery in Databases (KDD)”." In Massive Computing. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-4911-3_5.

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Panov, Panče, Larisa Soldatova, and Sašo Džeroski. "OntoDM-KDD: Ontology for Representing the Knowledge Discovery Process." In Discovery Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40897-7_9.

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Klösgen, Willi, and Jan Zytkow. "Techniques and applications of KDD." In Principles of Data Mining and Knowledge Discovery. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63223-9_140.

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Zhong, Ning, Chunnian Liu, Yoshitsugu Kakemoto, and Setsuo Ohsuga. "Handling KDD process changes by incremental replanning." In Principles of Data Mining and Knowledge Discovery. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0094811.

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Casillas, Jorge, and Francisco J. Martínez-López. "KDD in Marketing with Genetic Fuzzy Systems." In Soft Computing for Knowledge Discovery and Data Mining. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-69935-6_10.

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Yang, Guang-Zhong. "KDD for BSN – Towards the Future of Pervasive Sensing." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01307-2_1.

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Uthurusamy, Ramasamy. "Trends and Challenges in the Industrial Applications of KDD." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36175-8_2.

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Kim, Won. "KDD as an Enterprise IT Tool: Reality and Agenda." In Methodologies for Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48912-6_1.

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Conference papers on the topic "KDD (Knowledge Discovery in Database)"

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Ben Ahmed, Walid, Michel Bigand, Mounib Mekhilef, and Yves Page. "Development of Knowledge Based System to Facilitate Design of On-Board Car Safety Systems." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/dac-48743.

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The development of on-board car safety systems requires an accidentology knowledge base for the development of new functionalities as well as their improvement and evaluation. The Knowledge Discovery in accident Database (KDD) is one of the approaches allowing the construction of this knowledge base. However, considering the complexity of the accident data and the variety of their sources (biomechanics, psychology, mechanics, ergonomics, etc.), the analytical methods of the KDD (clustering, classification, association rules etc.) should be combined with expert approaches. Indeed, there is background knowledge in accidentology which exists in the minds of accidentologist experts and which is not formalized in the accident database. The aim of this paper is to develop a Knowledge Representation Model (KRM) intended to incorporate this knowledge in the KDD process. The KRM is implemented in a knowledge-based system, which provides an expert classification of the attributes characterizing an accident. This expert classification provides an efficient tool for data preparation in a KDD process. Our method consists of combining the modeling systemic approach of complex systems and the modeling cognitive approach KOD (Knowledge Oriented Design) in knowledge engineering.
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Abdulahi Hasan, Abdulkadir, and Huan Fang. "Data Mining in Education: Discussing Knowledge Discovery in Database (KDD) with Cluster Associative Study." In ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information Systems. ACM, 2021. http://dx.doi.org/10.1145/3469213.3471319.

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Pupezescu, Valentin. "ADVANCES IN KNOWLEDGE DISCOVERY IN DISTRIBUTED DATABASES." In eLSE 2015. Carol I National Defence University Publishing House, 2015. http://dx.doi.org/10.12753/2066-026x-15-046.

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The Knowledge Discovery in Distributed Databases is the process of extracting useful information from a collection of data stored in distributed databases. A distributed database is a collection of data replicated over a number of different computers. The best suited structures for working with distributed databases are the Distributed Committee-Machines. Distributed Committee-Machines are a combination of neural networks that work in a distributed manner as a group in order to obtain better performance than individual neural networks in solving data mining tasks inside the KDD process. In this paper I aim to study the interaction between Distributed Committee-Machines and distributed databases. The process of replication on multiple machines can become very slow once the number of the machines from the replication topology grows. Such behaviour is explicable because of the complex software that is used in real implementations of the replication process in order to make available the same data on multiple machines. Because of this situation, working with Distributed Committee-Machines in a distributed environment can be very problematic. In this paper I propose a design that overcomes those disadvantages and a new type of approach in storing the neural networks. The developed system stores the entire neural network in a real relational databases. All the neural structures that were used were multilayer perceptrons trained with the backpropagation algorithm. The non-optimized architecture has the disadvantage of writing all the results on the master system. All the data (TR - training set, TS - testing set, results) are duplicated on each slave machine. This working methods it's time consuming because of the internal functioning of the replication process. With this system, the distributed speedup is below one. The optimized DCM structure eliminates the problems inherited from replication by writing all the result locally in special tables that won't be replicated on all the distributed machines. Whenever the neural networks finds an optimal result, the neural network will write in the database all the important parameters. Here I used also a new approach which consists of storing the entire neural network in the table as BLOB (Binary Large Object) object. The method can be beneficial also in new types of eLearning technics such as the adaptive eLearning method that uses neural networks. With the optimized design of DCM structures the speedup in all the experiments is almost equal with the number of distributed machines that were used.
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Zhang, Lingzhe, Tong Jia, Mengxi Jia, Ying Li, Yong Yang, and Zhonghai Wu. "Multivariate Log-based Anomaly Detection for Distributed Database." In KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2024. http://dx.doi.org/10.1145/3637528.3671725.

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Lorena, Ana C., Filipe A. N. Verri, and Tiago A. Almeida. "The 5th Brazilian Competition on Knowledge Discovery in Databases (KDD-BR 2021)." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/eniac.2021.18425.

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Este artigo editorial descreve a Competição Brasileira de Descoberta de Conhecimento em Bancos de Dados (KDD-BR 2021) e resume as contribuições das três melhores soluções obtidas em sua quinta edição. A competição de 2021 envolveu a resolução de instâncias do Problema do Caixeiro Viajante, de diferentes tamanhos, usando uma abordagem de previsão de arestas.
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Dong, Peiran, Song Guo, and Junxiao Wang. "Investigating Trojan Attacks on Pre-trained Language Model-powered Database Middleware." In KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2023. http://dx.doi.org/10.1145/3580305.3599395.

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Senator, Ted E., Henry G. Goldberg, Alex Memory, et al. "Detecting insider threats in a real corporate database of computer usage activity." In KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2013. http://dx.doi.org/10.1145/2487575.2488213.

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Lorena, A. C., D. S. Kaster, R. Cerri, E. R. Faria, and V. V. de Melo. "Can I make a wish?: a competition on detecting meteors in images." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/kdmile.2018.27389.

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Promoting competitions has become a path towards attracting people’s interest into diverse areas. Many international conferences have sessions dedicated to one or more competitions, in which participants are challenged by real problems for which advanced solutions are needed. This paper describes the first Brazilian competition on Knowledge Discovery in Databases (KDD-BR), which was part of three main events of the Brazilian Computer Society dedicated to Artificial Intelligence, Databases and Data Mining. In this first edition the participants were supposed to detect meteors, popularly known as shooting stars, in regions of interest of images collected from a monitoring station located at São José dos Campos, Brazil. The data set assembled is detailed, which may be of interest for future benchmark studies using such data. The competition results, contributions and limitations are also discussed, providing a guide for future editions.
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Elezi, Fatos, Armin Sharafi, Alexander Mirson, Petra Wolf, Helmut Krcmar, and Udo Lindemann. "A Knowledge Discovery in Databases (KDD) Approach for Extracting Causes of Iterations in Engineering Change Orders." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48335.

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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 classify 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.
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Feng, Mengling, Mohammad Ghassemi, Thomas Brennan, John Ellenberger, Ishrar Hussain, and Roger Mark. "Management and analytic of biomedical big data with cloud-based in-memory database and dynamic querying." In KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2014. http://dx.doi.org/10.1145/2623330.2630806.

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