Com o crescimento das empresas que fazem uso das tecnologias de bancos de dados, os administradores destes bancos de dados criam novos esquemas a cada instante, e na maioria dos casos não existe uma normalização ou procedimentos formais para que tal tarefa seja desempenhada de forma homogênea, resultando assim em bases de dados incompatíveis, o que dificulta a troca de dados entre as mesmas. Quando os Sistemas de Bancos de Dados (SBD) são projetados e implementados independentemente, é normal que existam incompatibilidades entre os dados de diferentes SBD. Como principais conflitos existentes nos esquemas de SBD, podem ser citados problemas relacionados aos nomes dos atributos, armazenamento em diferentes unidades de medida, diferentes níveis de detalhes, atributos diferentes com mesmo nome ou atributos iguais com nomes diferentes, tipos de dado diferentes, tamanho, precisão, etc. Estes problemas comprometem a qualidade da informação e geram maiores custos em relação à manutenção dos dados. Estes problemas são conseqüências de atributos especificados de forma redundante. Estes fatos têm provocado grande interesse em descobrir conhecimento em banco de dados para identificar informações semanticamente equivalentes armazenadas nos esquemas. O processo capaz de descobrir este conhecimento em banco de dados denomina-se DCDB (Descoberta de Conhecimento em Bancos de Dados). As ferramentas disponíveis para a execução das tarefas de DCDB são genéricas e derivadas de outras áreas do conhecimento, em especial, da estatística e inteligência artificial. As redes neurais artificiais (RNA) têm sido utilizadas em sistemas cujo propósito é a identificação de padrões, antes desconhecidos. Estas redes podem aprender similaridades entre os dados, diretamente de suas instâncias, sem conhecimento a priori. Uma RNA que tem sido usada com êxito para identificar equivalência semântica é o Mapa Auto-Organizável (SOM). Esta pesquisa objetiva descobrir, de modo semi-automatizado, equivalência semântica entre atributos de bases de dados, contribuindo para o gerenciamento e integração das mesmas. O resultado da pesquisa gerou uma sistemática para o processo de descoberta e uma ferramenta que a implementa. / With the increasing number of companies using database technologies, the database’s administrators create new schemes at every moment, and in most cases there are no normalization or formal procedures to do this task in a homogeneous form, it results in incompatible databases, that difficult data exchange. When the Database Systems (DBS) are projected and implemented independently, it is normal that data incompatibilities among different DBS. Problems related to the names of the attributes, storage in different measurement units, different levels of detail, different attributes with the same name or equal attributes with different names, different type of data, size, precision, etc, can be cited as main conflicts existing in the DBS schemes. These problems compromise the quality information and generate higher costs regarding the data maintenance. These problems arise as the consequence of redundant attributes’ specification. These facts have caused great interest in discovering knowledge in database to identify information semantically equivalent stored in schemes. The process capable to discover this knowledge in database is called KDD (Knowledge Discovery in Database). The available tools to do KDD tasks are generic and derived from other areas of knowledge, in special, statistics and artificial intelligence. The artificial neural networks (ANN) have been used in systems which aim is the identification of previously unknown patterns. These networks can learn similarities among the data directly from instances, without a priori knowledge. An ANN that has been used with success to identify semantic equivalence is the Self-Organizing Map (SOM). This research aims to discover, in a semi-automatic way, semantic equivalence on database attributes, contributing for the management and integration of these databases. This work resulted in a systematic for the discovery process and a tool that implements it.

Made available in DSpace on 2016-03-15T19:26:36Z (GMT). No. of bitstreams: 1 Marcelo Pires Fernandes.pdf: 425391 bytes, checksum: 82c6fd61293544d4f47d5a6eec0f6580 (MD5) Previous issue date: 2008-02-12 / The research problem to be studied is related to the way companies from the services industry use customer databases to discover useful knowledge about their customers, in order to improve the development of relationship strategies with them. This issue is important mainly because due to the increasing of concurrence and customer demand, the company needs to relate differently with their customers, so that thy can keep in its portfolio the most profitable ones. In this way, the theory has suggested a deeper integration among distinct disciplines as Relationship Marketing, CRM and Data Mining. In this current study, it was investigated the way the theory presents and describes database analysis processes and, as a result, some proposals were found out, that segment the processes of discovering knowledge in databases in stages like problem understanding, data understanding, data preparation, data modeling data, model evaluation and deployment. The target population was composed by companies from the services industry from São Paulo and Rio de Janeiro cities and a quantitative research was made by applying a questionnaire to 67 professionals from the target population. In this research, themes as utilization level from stages of process of discovering knowledge in databases, utilization level of data mining techniques and utilization level of relationship strategies were investigated. It was discovered that the companies researched have a high utilization level of the stages of knowledge discovery identified in the theory, just only a small part of the data mining techniques are uniformly used by the companies researched and, at last, the strategies with the highest utilization levels are that related to the acquisition of new customers and identification of profitable ones. This last discover was a little bit surprising, because it is opposed to the way of thinking of some authors who defend companies should focus on their relationship strategies in the customer retention. These results can be used to support companies, in subjects related to the development of customer relationship strategies, based in an integrated analysis of business issues, customer information, as well quantitative models of analysis from this information, in order to turn it into useful knowledge to the making decision. / O problema de pesquisa a ser investigado está associado ao modo como empresas do setor de serviços utilizam bases de dados para descobrir conhecimento sobre o cliente e embasar o desenvolvimento de estratégias de relacionamento. Este tema é importante, visto que em função do aumento da concorrência e da exigência dos clientes, as empresas precisam tratar seus clientes de forma diferenciada, de forma a manter em sua carteira aqueles mais rentáveis. Neste sentido, a literatura tem sugerido uma integração cada vez mais intensa entre disciplinas como Marketing de Relacionamento, CRM e Mineração de Dados. O presente trabalho estudou o modo como a literatura apresenta e descreve processos de análise de bases de dados e algumas propostas foram encontradas, propostas que segmentam o processo de descoberta de conhecimento em bases de dados em etapas como entendimento do problema, entendimento e preparação dos dados, modelagem dos dados, avaliação do modelo e implementação da solução desenvolvida. O universo estudado foi o de empresas do setor de serviços que atuam nas cidades de São Paulo e do Rio de Janeiro e uma pesquisa quantitativa foi realizada por meio da aplicação de um questionário a 67 respondentes. Nesta pesquisa, foi investigado o nível de utilização das etapas dos processos de descoberta de conhecimento em bases de dados, as técnicas de mineração utilizadas, bem como as estratégias de relacionamento adotadas com clientes. Constatou-se que as empresas pesquisadas possuem um alto nível de utilização das etapas de descoberta de conhecimento identificadas na literatura, que elas utilizam de forma uniforme apenas algumas das técnicas de mineração de dados identificadas na literatura e que, do ponto de vista de estratégias de relacionamento com clientes, as estratégias de aquisição de novos clientes e identificação dos melhores clientes possuem um nível de utilização superior ao de estratégias de retenção de clientes (considerando resultados da amostra). Esta última constatação, de certo modo, contraria o pensamento de algumas correntes teóricas, que defendem que as empresas devem focar suas estratégias de relacionamento na retenção de clientes. Estes resultados pode servir de apoio aos gestores das empresas, no que se refere aos processos de desenvolvimento de estratégias de relacionamento com clientes, sustentados em análise integrada dos aspectos de negócio envolvidos, informações sobre o cliente, bem como modelos quantitativos de análise destas informações, de forma a transformá-las em conhecimento útil para a tomada de decisão.

Submitted diploma thesis considers empirical comparison of knowledge discovery in databases systems. Basic terms and methods of knowledge discovery in databases domain are defined and criterions used to system comparison are determined. Tested software products are also shortly described in the thesis. Results of real task processing are brought out for each system. The comparison of individual systems according to previously determined criterions and comparison of competitiveness of commercial and non-commercial knowledge discovery in databases systems are performed within the framework of thesis.

In this work we propose a solution that would help automate the part of knowledge discovery in databases. Domain knowledge has an important role in the automation process which is necessary to include into the proposed program for data preparation. In the introduction to this work, we focus on the theoretical basis of knowledge discovery of databases with an emphasis on domain knowledge. Next, we focus on the basic principles of data pre-processing and scripting language LMCL that could be part of the design of the newly established applications for automated data preparation. Subsequently, we will deal with application design for data pre-processing, which will be verified on the data the House of Commons.

Throughout last decades, knowledge discovery from databases as one of the information and communicaiton technologies' disciplines has developed into its current state being showed increasing interest not only by major business corporates. Presented diploma thesis deals with problematique of data mining while paying prime attention to its practical utilization within business environment. Thesis objective is to review possibilities of data mining applications and to decompose implementation techniques focusing on specific data mining methods and algorithms as well as adaptation of business processes. This objective is subject of theoretical part of thesis focusing on principles of data mining, knowledge discovery from databases process, data mining commonly used methods and algorithms and finally tasks typically implemented in this domain. Further objective consists in presenting data mining benefits on the model example that is being displayed in the practical part of the thesis. Besides created data mining models evalution, practical part contains also design of subsequent steps that would enable higher efficiency in some specific areas of given business. I believe previous point together with characterization of knowledge discovery in databases process to be considered as the most beneficial one's of the thesis.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-07-11T14:35:16Z No. of bitstreams: 3 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) projeto_v26016.pdf: 1271790 bytes, checksum: f724d8523f2ffdb11ce599aff1eb8eb6 (MD5) projeto_v26016.pdf: 1271790 bytes, checksum: f724d8523f2ffdb11ce599aff1eb8eb6 (MD5) / Made available in DSpace on 2017-07-11T14:35:16Z (GMT). No. of bitstreams: 3 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) projeto_v26016.pdf: 1271790 bytes, checksum: f724d8523f2ffdb11ce599aff1eb8eb6 (MD5) projeto_v26016.pdf: 1271790 bytes, checksum: f724d8523f2ffdb11ce599aff1eb8eb6 (MD5) Previous issue date: 2016-07-08 / As Instituições Federais de Ensino Superior (IFES) possuem um importante papel no desenvolvimento social e econômico do país, contribuindo para o avanço tecnológico e cientifico e fomentando investimentos. Nesse sentido, entende-se que um melhor aproveitamento dos recursos educacionais ofertados pelas IFES contribui para a evolução da educação superior, como um todo. Uma maneira eficaz de atender esta necessidade é analisar o perfil dos estudantes ingressos e procurar prever, com antecedência, casos indesejáveis de evasão que, quanto mais cedo identificados, melhor poderão ser estudados e tratados pela administração. Neste trabalho, propõe-se a definição de uma abordagem para aplicação de técnicas diretas de Mineração de Dados objetivando a classificação dos discentes ingressos de acordo com o risco de evasão que apresentam. Como prova de conceito, a análise dos aspectos inerentes ao processo de Mineração de Dados proposto se deu por meio de experimentações conduzidas no ambiente da Universidade Federal de Pernambuco (UFPE). Para alguns dos algoritmos classificadores, foi possível obter uma acurácia de classificação de 73,9%, utilizando apenas dados socioeconômicos disponíveis quando do ingresso do discente na instituição, sem a utilização de nenhum dado dependente do histórico acadêmico. / The Brazilian's Federal Institutions of Higher Education have an important role in the social and economic development of the country, contributing to the technological and scientific advances and encouraging investments. Therefore, it is possible to infer that a better use of the educational resources offered by those institutions contributes to the evolution of higher education as a whole. An effective way to meet this need is to analyze the profile of the freshmen students and try to predict, as soon as possible, undesirable cases of dropout that when earlier identified can be examined and addressed by the institution's administration. This work propose the development of a approach for direct application of Data Mining techniques to classify newcomer students according to their dropout risk. As a viability proof, the proposed Data Mining approach was evaluated through experimentations conducted in the Federal University of Pernambuco. Some of the classification algorithms tested had an classification accuracy of 73.9% using only socioeconomic data available since the student's admission to the institution, without the use of any academic related data.

Nas últimas décadas, a capacidade das empresas de gerar e coletar informações aumentou rapidamente. Essa explosão no volume de dados gerou a necessidade do desenvolvimento de novas técnicas e ferramentas que pudessem, além de processar essa enorme quantidade de dados, permitir sua análise para a descoberta de informações úteis, de maneira inteligente e automática. Isso fez surgir um proeminente campo de pesquisa para a extração de informação em bases de dados denominado Knowledge Discovery in Databases – KDD, no geral técnicas de mineração de dados – DM – têm um papel preponderante. A obtenção de bons resultados na etapa de mineração de dados depende fortemente de quão adequadamente o preparo dos dados é realizado. Sendo assim, a etapa de extração de conhecimento (DM) no processo de KDD, é normalmente precedida de uma etapa de pré-processamento, onde os dados que porventura devam ser submetidos à etapa de DM são integrados em uma única relação. Um problema importante enfrentado nessa etapa é que, na maioria das vezes, o usuário ainda não tem uma idéia muito precisa dos dados que devem ser extraídos. Levando em consideração a grande habilidade de exploração da mente humana, este trabalho propõe uma técnica de visualização de dados armazenados em múltiplas relações de uma base de dados relacional, com o intuito de auxiliar o usuário na preparação dos dados a serem minerados. Esta técnica permite que a etapa de DM seja aplicada sobre múltiplas relações simultaneamente, trazendo as operações de junção para serem parte desta etapa. De uma maneira geral, a adoção de junções em ferramentas de DM não é prática, devido ao alto custo computacional associado às operações de junção. Entretanto, os resultados obtidos nas avaliações de desempenho da técnica proposta neste trabalho mostraram que ela reduz esse custo significativamente, tornando possível a exploração visual de múltiplas relações de uma maneira interativa. / In the last decades the capacity of information generation and accumulation increased quickly. With the explosive growth in the volume of data, new techniques and tools are being sought to process it and to automatically discover useful information from it, leading to techniques known as Knowledge Discovery in Databases – KDD – where, in general, data mining – DM – techniques play an important role. The results of applying data mining techniques on datasets are highly dependent on proper data preparation. Therefore, in traditional DM processes, data goes through a pre-processing step that results in just one table that is submitted to mining. An important problem faced during this step is that, most of the times, the analyst doesn’t have a clear idea of what portions of data should be mined. This work reckons the strong ability of human beings to interpret data represented in graphical format, to develop a technique to visualize data from multiple tables, helping human analysts when preparing data to DM. This technique allows the data mining process to be applied over multiple relations at once, bringing the join operations to become part of this process. In general, the use of multiple tables in DM tools is not practical, due to the high computational cost required to explore them. Experimental evaluation of the proposed technique shows that it reduces this cost significantly, turning it possible to visually explore data from multiple tables in an interactive way.

With the development of technologies such as Global Navigation Satellite Systems (GNSS), mobile computing, and Information and Communication Technology (ICT) the procedure of sampling positional data has lately been significantly simplified.  This enables the aggregation of large amounts of moving objects data (i.e. trajectories) containing potential information about the moving objects. Within Knowledge Discovery in Databases (KDD), automated processes for realization of this information, called trajectory data mining, have been implemented.   The objectives of this study is to examine 1) how trajectory data mining tasks are defined at an abstract level, 2) what type of information it is possible to extract from trajectory data, 3) what solutions trajectory data mining tools implement for different tasks, 4) how tools uses visualization, and 5) what the limiting aspects of input data are how those limitations are treated. The topic, trajectory data mining, is examined in a literature review, in which a large number of academic papers found trough googling were screened to find relevant information given the above stated objectives.   The literature research found that there are several challenges along the process arriving at profitable knowledge about moving objects. For example, the discrete modelling of movements as polylines is associated with an inherent uncertainty since the location between two sampled positions is unknown.  To reduce this uncertainty and prepare raw data for mining, data often needs to be processed in some way. The nature of pre-processing depends on sampling rate and accuracy properties of raw in-data as well as the requirements formulated by the specific mining method. Also a major challenge is to define relevant knowledge and effective methods for extracting this from the data. Furthermore are conveying results from mining to users an important function. Presenting results in an informative way, both at the level of individual trajectories and sets of trajectories, is a vital but far from trivial task, for which visualization is an effective approach.   Abstractly defined instructions for data mining are formally denoted as tasks. There are four main categories of mining tasks: 1) managing uncertainty, 2) extrapolation, 3) anomaly detection, and 4) pattern detection. The recitation of tasks within this study provides a basis for an assessment of tools used for the execution of these tasks. To arrive at profitable results the dimensions of comparison are selected with the intention to cover the essential parts of the knowledge discovery process. The measures to appraise this are chosen to make results correctly reflect the 1) sophistication, 2) user friendliness, and 3) flexibility of tools. The focus within this thesis is freely available tools, for which the range is proven to be very small and fragmented. The selection of tools found and reported on are: MoveMine 2.0, MinUS, GeT_Move and M-Atlas.   The tools are reviewed entirely through utilizing documentation of the tools. The performance of tools is proved to vary along all dimensional measures except visualization and graphical user interface which all tools provide. Overall the systems preform well considering user-friendliness, somewhat good considering sophistication and poorly considering flexibility. However, since the range of tasks, which tools intend to solve, overall is varying it might not be appropriate to compare the tools in term of better or worse.   This thesis further provides some theoretical insights for users regarding requirements on their knowledge, both concerning the technical aspects of tools and about the nature of the moving objects. Furthermore is the future of trajectory data mining in form of constraints on information extraction as well as requirements for development of tools discussed, where a more robust open source solution is emphasised. Finally, this thesis can altogether be regarded to provide material for guidance in what trajectory mining tools to use depending on application. Work to complement this thesis through comparing the actual performance of tools, when using them, is desirable. / I och med utvecklingen av tekniker så som Global Navigation Satellite systems (GNSS), mobile computing och Information and Communication Technology (ICT) har tillvägagångsätt för insamling av positionsdata drastiskt förenklats. Denna utveckling har möjliggjort för insamlandet av stora mängder data från rörliga objekt (i.e. trajecotries)(sv: rörelsebanor), innehållande potentiell information om dessa rörliga objekt. Inom Knowledge Discovery in Databases (KDD)(sv: kunskapsanskaffning i databaser) tillämpas automatiserade processer för att realisera sådan information, som kallas trajectory data mining (sv: utvinning från rörelsebanedata).   Denna studie ämnar undersöka 1) hur trajectory data mining tasks (sv: utvinning från rörelsebanedata uppgifter) är definierade på en abstrakt nivå, 2) vilken typ av information som är möjlig att utvinna ur rörelsebanedata, 3) vilka lösningar trajectory data ming tools (sv: verktyg för datautvinning från rörelsebanedata) implementerar för olika uppgifter, 4) hur verktyg använder visualisering, och 5) vilka de begränsande aspekterna av input-data är och hur dessa begränsningar hanteras. Ämnet utvinning från rörelsebanedata undersöks genom en litteraturgranskning, i vilken ett stort antal och akademiska rapporter hittade genom googling granskas för att finna relevant information givet de ovan nämnda frågeställningarna.   Litteraturgranskningen visade att processen som leder upp till en användbar kunskap om rörliga objekt innehåller dock flera utmaningar. Till exempel är modelleringen av rörelser som polygontåg associerad med en inbyggd osäkerhet eftersom positionen för objekt mellan två inmätningar är okänd. För att reducera denna osäkerhet och förbereda rådata för extraktion måste ofta datan processeras på något sätt. Karaktären av förprocessering avgörs av insamlingsfrekvens och exakthetsegenskaper hos rå indata tillsammans med de krav som ställs av de specifika datautvinningsmetoderna. En betydande utmaning är också att definiera relevant kunskap och effektiva metoder för att utvinna denna från data. Vidare är förmedlandet av resultat från utvinnande till användare en viktig funktion. Att presentera resultat på ett informativt sätt, både på en nivå av enskilda rörelsebanor men och grupper av rörelsebanor är en vital men långt ifrån trivial uppgift, för vilken visualisering är ett effektivt tillvägagångsätt.   Abstrakt definierade instruktioner för dataextraktion är formellt betecknade som uppgifter. Det finns fyra huvudkategorier av uppgifter: 1) hantering av osäkerhet, 2) extrapolation, 3) anomalidetektion, and 4) mönsterdetektion. Sammanfattningen av uppgifter som ges i denna rapport utgör ett fundament för en utvärdering av verktyg, vilka används för utförandet av uppgifter. För att landa i ett givande resultat har jämförelsegrunderna för verktygen valts med intentionen att täcka de viktigaste delarna av processen för att förvärva kunskap. Måtten för att utvärdera detta valdes för att reflektera 1) sofistikering, 2) användarvänlighet, och 3) flexibiliteten hos verktygen. Fokuset inom denna studie har varit verktyg som är gratis tillgängliga, för vilka utbudet har visat sig vara litet och fragmenterat. Selektionen av verktyg som hittats och utvärderats var: MoveMine 2.0, MinUS, GeT_Move and M-Atlas.   Verktygen utvärderades helt och hållet baserat på tillgänglig dokumentation av verktygen.  Prestationen av verktygen visade sig variera längs alla jämförelsegrunder utom visualisering och grafiskt gränssnitt som alla verktyg tillhandahöll. Överlag presterade systemen väl gällande användarvänlighet, någorlunda bra gällande sofistikering och dåligt gällande flexibilitet. Hursomhelst, eftersom uppgifterna som verktygen avser att lösa varierar är det inte relevant att värdera dem mot varandra gällande denna aspekt.   Detta arbete tillhandahåller vidare några teoretiska insikter för användare gällande krav som ställs på deras kunskap, både gällande de tekniska aspekterna av verktygen och rörliga objekts beskaffenhet. Vidare diskuteras framtiden för utvinning från rörelsebanedata i form av begränsningar på informationsutvinning och krav för utvecklingen av verktyg, där en mer robust open source lösning betonas. Sammantaget kan detta arbete anses tillhandahålla material för vägledning i vad för verktyg för datautvinning från rörelsebanedata som kan användas beroende på användningsområde. Arbete för att komplettera denna rapport genom utvärdering av verktygens prestation utifrån användning av dem är önskvärt.

This thesis applies the data mining method of decision rules on data from Consulting centre for Metabolism disorders from University hospital Pilsen. As a tool is used the system LISp-Miner, developed at University of Economics, Prague. Decision rules found are evaluated by a specialist. The main parts of this thesis are followings: an overview on main data mining methods and results evalutation methods, description of the data mining method application on data and description and evaluation of results.

Economic development in Sri Lanka has relied heavily on foreign and domestic investment. Digital databases are a new and attractive area for this investment. This thesis argues that investment needs protection and this is crucial to attract future investment. The thesis therefore proposes a digital database protection mechanism with a view to attracting investment in digital databases to Sri Lanka. The research examines various existing protection measures whilst mainly focusing on the sui generis right protection which confirms the protection of qualitative and/or quantitative substantial investment in the obtaining, verification or presentation of the contents of digital databases. In digital databases, this process is carried out by computer programs which establish meaningful and useful data patterns through their data mining process, and subsequently use those patterns in Knowledge Discovery within database processes. Those processes enhance the value and/or usefulness of the data/information. Computer programs need to be protected, as this thesis proposes, by virtue of patent protection because the process carried out by computer programs is that of a technical process - an area for which patents are particularly suitable for the purpose of protecting. All intellectual property concepts under the existing mechanisms address the issue of investment in databases in different ways. These include Copyright, Contract, Unfair Competition law and Misappropriation and Sui generis right protection. Since the primary objective of the thesis is to introduce a protection system for encouraging qualitative and quantitative investment in digital databases in Sri Lanka, this thesis suggests a set of mechanisms and rights which comprises of existing intellectual protection mechanisms for databases. The ultimate goal of the proposed protection mechanisms and rights is to improve the laws pertaining to the protection of digital databases in Sri Lanka in order to attract investment, to protect the rights and duties of the digital database users and owners/authors and, eventually, to bring positive economic effects to the country. Since digital database protection is a new concept in the Sri Lankan legal context, this research will provide guidelines for policy-makers, judges and lawyers in Sri Lanka and throughout the South Asian region.