After well-known structured technology, object-oriented technology has become another paradigm of system development and a hot topic in the field of software engineering. The Unified Modeling Language (UML) has become a standard means for the analysis and design of object-oriented after it passed the certification of the Object Management Group (OMG) in 1997, and it was widely used in many industries. Although it becomes a standard of object-oriented modeling language, UML hasn¡¦t provided principles or guidelines for mapping classes to tables. The purpose of this study is to investigate the guidelines for transforming UML class diagram to object-relational model. The transferring methods include both static structure transfer and dynamic behavior transfer. An example of automobile ordering system is used to illustrate the guideline and application. With these guidelines, analysts can easily use the UML class diagram to express database designing and thereby improve the efficiency and effectiveness of UML modeling.

En esta tesis, presentamos un nuevo enfoque para validar mappings entre esquemas de datos que permite al diseñador comprobar si el mapping satisface o no ciertas propiedades deseables. La respuesta que obtiene el diseñador no se limita a un simple valor booleano, sino que dependiendo del resultado de la comprobación obtendrá un ejemplo/contraejemplo que ilustre ese resultado, o bien se le indicará el conjunto de restricciones de integridad de los esquemas y formulas del mapping responsables de ese resultado. Una de las características principales de nuestro enfoque es que es capaz de tratar una clase muy expresiva de mappings y esquemas relacionales. En particular, nuestro enfoque es capaz de tratar con formulas de mapping consistentes en inclusiones y igualdades de consultas, además de permitir el uso de negaciones y comparaciones aritméticas tanto en las propias formulas del mapping como en las vistas definidas en los esquemas. Nuestro enfoque también permite tratar restricciones de integridad, las cuales pueden estar definidas no solo sobre las tablas sino también sobre las vistas de los esquemas. Dado que razonar sobre este tipo de mappings y esquemas es, desafortunadamente, indecidible, proponemos realizar un test de terminación previo a la validación del mapping. Si el test de terminación da una respuesta positiva, entonces podremos estar seguros de que la posterior comprobación de la propiedad deseable correspondiente terminará. Finalmente, también extendemos nuestro enfoque más allá del caso relacional y lo aplicamos al contexto de mappings entre esquemas XML. / In this thesis, we present a new approach to the validation of mappings between data schemas. It allows the designer to check whether the mapping satisfies certain desirable properties. The feedback that our approach provides to the designer is not only a Boolean answer, but either a (counter)example for the (un)satisfiability of the tested property, or the set of mapping assertions and schema constraints that are responsible for that (un)satisfiability. One of the main characteristics of our approach is that it is able to deal with a very expressive class of relational mapping scenarios; in particular, it is able to deal with mapping assertions in the form of query inclusions and query equalities, and it allows the use of negation and arithmetic comparisons in both the mapping assertions and the views of the schemas; it also allows for integrity constraints, which can be defined not only over the base relations but also in terms of the views. Since reasoning on the class of mapping scenarios that we consider is, unfortunately, undecidable, we propose to perform a termination test as a pre-validation step. If the answer of the test is positive, then checking the corresponding desirable property will terminate. We also go beyond the relational setting and study the application of our approach to the context of mappings between XML schemas.

The Structured Query Language (SQL) is widely used and challenging to master. Within the context of lab exercises in an introductory database course, this thesis analyzes the student learning process and seeks to answer the question: ``Which SQL concepts, or concept combinations, trouble students the most?'' We provide comprehensive taxonomies of SQL concepts and errors, identify common areas of student misunderstanding, and investigate the student problem-solving process. We present an interactive web application used by students to complete SQL lab exercises. In addition, we analyze data collected by this application and we offer suggestions for improvement to database lab activities.

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Os Sistemas de Gerenciamento de Bases de Dados Relacionais (SGBDR) foram concebidos para o armazenamento e recuperação de grandes volumes de dados. Tradicionalmente, estes sistemas suportam números, pequenas cadeias de caracteres e datas (que podem ser comparados por identidade ou por relações de ordem { RO), porém vem se tornando necessário organizar, armazenar e recuperar dados mais complexos, como por exemplo dados multimídia (imagens, áudio e vídeo), séries temporais etc. Quando se trata de dados complexos há uma mudança de paradigma, pois as comparações entre elementos são feitas por similaridade em vez das RO utilizadas tradicionalmente, tendo como mais frequentemente utilizados os operadores de comparação por abrangência (Rq) e por k-vizinhos mais próximos (k-NN). Embora muitos estudos estejam sendo feitos nessa área, quando lidando com consultas por similaridade grande parte do esforço é direcionado para criar as estruturas de indexação e dar suporte às operações necessárias para executar apenas o aspecto da consulta que trata da similaridade, sem focar em realizar uma integração homogênea das consultas que envolvam ambos os tipos de operadores simultaneamente nos ambientes dos SGDBRs. Um dos principais problemas nessa integração é lidar com as peculiaridades do operador de busca por k-NN. Todos os operadores de comparação por identidade e por RO são comutativos e associativos entre si. No entanto o operador de busca por k-NN não atende a nenhuma dessas propriedades. Com isso, a expressão de consultas em SQL, que usualmente pode ser feita sem que a expressão da ordem entre os predicados seja importante, precisa passar a considerar a ordem. Além disso, consultas que utilizam comparações por k-NN podem gerar múltiplos empates, e a falta de uma metodologia para resolvê-los pode levar a um processo de desempate arbitrário ou insensível ao contexto da consulta, onde usuários não tem poder para intervir de maneira significativa. Em alguns casos, isso pode levar a uma mesma consulta a retornar resultados distintos em casos onde a estrutura interna dos dados estiver sujeita a modificações, como por exemplo em casos de transações concorrentes em um SGBDR. Este trabalho aborda os problemas gerados pela inserção de operadores de busca por similaridade nos SGBDR, mais especificamente o k-NN, e propõe novas maneiras de representação de consultas com múltiplos predicados, por similaridade ou RO, assim como novos operadores derivados do k-NN que são mais adequados para um ambiente relacional que permita consultas híbridas, e permitem também controle sobre o tratamento de empates. / The Relational Database Management Systems (RDBMS) were originally conceived to store and retrieve large volumes of data. Traditionally, these systems support only numbers, small strings of characters and dates (which could be compared by identity and a Order Relationship { OR). However it has been increasingly necessary to organize, store and retrieve more complex data, such as multimedia (images, audio and video), time series etc. Dealing with those data types requires a paradigm shift, as the comparisons between each element are made by similarity, and not by the traditionally used identity or OR, with the most common similarity operators used being the range (Rq) and k-Nearest Neighbors (k-NN). Despite many studies in the field, when dealing with similarity queries a large part of the effort has been directed towards the data structures and the necessary operations to execute only the similarity side of the query, not paying attention to a more homogenous integration of queries that involve both operator types simultaneously in RDBMS environments. One of the main problems for such integration is the peculiarities of the k-NN operator. Both identity and OR operators possess the commutative and associative properties amongst themselves, but the k-NN operator does not. As such, expressing SQL queries, that usually can disregard the order in which predicates appear, now needs to be aware of the ordering. Furthermore, queries that use k-NN might generate multiple ties, and the lack of a methodology to solve them might lead to an arbitrary or context-detached untying process, where users have little or no control to intervene. In some applications, the lack of a controlled untying process may even lead to each query yielding distinct results if the underlying structures ought be subject to change, as it is be the case of the concurrent transactions in a relational database management system (RDBMS). This work focuses on the problems that arise from the integration of similarity based operators into RDBMS, more specifically the k-NN, and proposes new ways to represent queries with multiple predicates, including similarity, identity or OR, as well as new operators derived from k-NN that are better suited for a RDBMS environment containing hybrid queries, and also enable control over the untying process.

This research combines relational database framework and quantitative equity portfolio models based on the Barra Risk Model Handbook standard steps to design a database and computer platform for multi-factor risk management tasks. The multi-factor model facilitates fast search and efficient selection of descriptors with explanatory power for future stock returns. The design of database is divided into three steps. First, descriptors are calculated and daily-update modules constructed. This study finds 48 key descriptors which play important roles in explaining stock returns of Taiwan. Second, entity relational model is applied to sort out linkages between pieces of important information in the factor model. Lastly, database auto-run procedures are setup to update the latest raw data on a monthly basis. Model parameter update and portfolio rebalancing is hence made seamless to meet practical operation demand for such a platform. The development of the Multi-factor risk model is divided into five main steps. (1) Finding significant descriptors. (2) Forming common factors from descriptors. (3) Developing a multi-factor return model. (4) Developing a multi-factor risk model. (5) Running performance analysis and back-testing. The empirical results show that the average adjusted R-squared of the MFM model is 0.5 during the period of 1998/04~2005/11. For combining descriptors into common factors, we run factor analysis. The multi-collinearity problem existing in the descriptors is well taken care of by such procedures. We use the exponentially weighted averaging method to compute the factor returns and forecast stock ranking. A half-life of 24 months appears to deliver the best performance in Taiwan stock market.

Ocean ambient noise is one of the important parameters which affect sonar system performance. If the ocean ambient noise is estimated accurately, the prediction accuracy of sonar system performance can be promoted significantly. Ocean ambient noise includes various and diverse sources, so the characteristics of the ocean ambient noise should be analyzed by long-term observations and statistical methods. Therefore, ocean ambient noise database was developed to facilitate management, preservation, and application of these datasets which increase with time. There were two datasets of acoustic and three datasets of wind speed in this database at this point. To develop this database systematically, this study applied Entity-Relationship Model to describe the relationship between different data and Relational Model to design the required categories. The database was constructed based on Microsoft Office Access, and user-friendly graphical interfaces based on MATLAB were provided for users: wind speed regression, time series, spectrogram, and spectrum tendency for users to query the database. As the results of analysis, the intermittent, unknown, and high-level sources at southwestern sea of Taiwan in spring and summer made the noise level of low frequency about 8 to 10 dB higher in the night-time than that in the day-time. According to wind speed regression analysis, if there were sufficient data, the method would be practicable to pick a wind speed data nearby the location of acoustic data for noise estimation. As the results of the typhoon effects analysis, the noise level of 1 to 8 kHz was increased significantly by typhoons. Besides, the noise level didn¡¦t decrease immediately and significantly after the pass of typhoon because of southwestern air current caused typhoon. After the database was developed, new acoustic data will keep being measured and collected, and the network capability will be integrated into the database to make the database more accessible to users.

Os Sistemas de Gerenciamento de Bases de Dados Relacionais (SGBDR) foram concebidos para o armazenamento e recuperação de grandes volumes de dados. Tradicionalmente, estes sistemas suportam números, pequenas cadeias de caracteres e datas (que podem ser comparados por identidade ou por relações de ordem { RO), porém vem se tornando necessário organizar, armazenar e recuperar dados mais complexos, como por exemplo dados multimídia (imagens, áudio e vídeo), séries temporais etc. Quando se trata de dados complexos há uma mudança de paradigma, pois as comparações entre elementos são feitas por similaridade em vez das RO utilizadas tradicionalmente, tendo como mais frequentemente utilizados os operadores de comparação por abrangência (Rq) e por k-vizinhos mais próximos (k-NN). Embora muitos estudos estejam sendo feitos nessa área, quando lidando com consultas por similaridade grande parte do esforço é direcionado para criar as estruturas de indexação e dar suporte às operações necessárias para executar apenas o aspecto da consulta que trata da similaridade, sem focar em realizar uma integração homogênea das consultas que envolvam ambos os tipos de operadores simultaneamente nos ambientes dos SGDBRs. Um dos principais problemas nessa integração é lidar com as peculiaridades do operador de busca por k-NN. Todos os operadores de comparação por identidade e por RO são comutativos e associativos entre si. No entanto o operador de busca por k-NN não atende a nenhuma dessas propriedades. Com isso, a expressão de consultas em SQL, que usualmente pode ser feita sem que a expressão da ordem entre os predicados seja importante, precisa passar a considerar a ordem. Além disso, consultas que utilizam comparações por k-NN podem gerar múltiplos empates, e a falta de uma metodologia para resolvê-los pode levar a um processo de desempate arbitrário ou insensível ao contexto da consulta, onde usuários não tem poder para intervir de maneira significativa. Em alguns casos, isso pode levar a uma mesma consulta a retornar resultados distintos em casos onde a estrutura interna dos dados estiver sujeita a modificações, como por exemplo em casos de transações concorrentes em um SGBDR. Este trabalho aborda os problemas gerados pela inserção de operadores de busca por similaridade nos SGBDR, mais especificamente o k-NN, e propõe novas maneiras de representação de consultas com múltiplos predicados, por similaridade ou RO, assim como novos operadores derivados do k-NN que são mais adequados para um ambiente relacional que permita consultas híbridas, e permitem também controle sobre o tratamento de empates. / The Relational Database Management Systems (RDBMS) were originally conceived to store and retrieve large volumes of data. Traditionally, these systems support only numbers, small strings of characters and dates (which could be compared by identity and a Order Relationship { OR). However it has been increasingly necessary to organize, store and retrieve more complex data, such as multimedia (images, audio and video), time series etc. Dealing with those data types requires a paradigm shift, as the comparisons between each element are made by similarity, and not by the traditionally used identity or OR, with the most common similarity operators used being the range (Rq) and k-Nearest Neighbors (k-NN). Despite many studies in the field, when dealing with similarity queries a large part of the effort has been directed towards the data structures and the necessary operations to execute only the similarity side of the query, not paying attention to a more homogenous integration of queries that involve both operator types simultaneously in RDBMS environments. One of the main problems for such integration is the peculiarities of the k-NN operator. Both identity and OR operators possess the commutative and associative properties amongst themselves, but the k-NN operator does not. As such, expressing SQL queries, that usually can disregard the order in which predicates appear, now needs to be aware of the ordering. Furthermore, queries that use k-NN might generate multiple ties, and the lack of a methodology to solve them might lead to an arbitrary or context-detached untying process, where users have little or no control to intervene. In some applications, the lack of a controlled untying process may even lead to each query yielding distinct results if the underlying structures ought be subject to change, as it is be the case of the concurrent transactions in a relational database management system (RDBMS). This work focuses on the problems that arise from the integration of similarity based operators into RDBMS, more specifically the k-NN, and proposes new ways to represent queries with multiple predicates, including similarity, identity or OR, as well as new operators derived from k-NN that are better suited for a RDBMS environment containing hybrid queries, and also enable control over the untying process.

Using the focus group methodology, this thesis investigated Instagram’s impact on Knapp’s (1978) relational model, which conceptualized romantic relationships as occurring in stages. In particular, the purpose of this thesis was to gain insight on the ways that young adults (18–25) utilized Instagram’s affordances during the stages of romantic relationships. The focus groups elicited eight themes that provided insights on the motivations and behaviors of individuals who use Instagram’s affordances during romantic relationship processes. These themes included: 1) information seeking, 2) reciprocal idiosyncratic “Like,” 3) unequivocal comment, 4) significant first picture, 5) expected profile engagement, 6) reciprocal photo sharing, 7) jealousy provoking behaviors, and 8) supplemental termination behavior. The first four themes showed that Instagram’s affordances play a large role in the coming together stages of romantic relationships. The next two themes indicated that Instagram’s affordances play a large role in relationship maintenance. Finally, the last two themes indicated that Instagram’s affordances play a moderate role in the coming apart stages of relationships. The uses and gratifications theory was used to explain the findings.

Fuzzy relational databases deal with imprecise data or fuzzy information in a relational database. The purpose of this fuzzy database implementation is to retrieve images by using fuzzy queries whose common-language descriptions are defined by the consensus of a particular user community. The fuzzy set, which is presentation of fuzzy attribute values of the images, is determined through membership function. This paper compares two methods of constructing membership functions, the Direct Rating and New Random Proportional, to determine which method gives maximum users satisfaction with minimum feedback from the community. The statistical analysis of results suggests the use of Direct Rating method. Moreover, the analysis shows that the performance of the New Random Proportional method can be improved with the inclusion of a "Not" modifier. This paper also identifies and analyzes issues that are raised by different versions of the database system.