As informações imprecisas e vagas, comumente encontradas na modelagem de problemas do mundo real, muitas vezes não são manipuladas de forma adequada por meio das consultas convencionais aos bancos de dados. Alternativamente, a teoria de conjuntos fuzzy tem sido considerada uma ferramenta bem promissora para tratamento destas informações consideradas imprecisas e, em determinados casos, até mesmo ambíguas. Esse trabalho utiliza a linguagem SQL padrão para apresentar uma abordagem fuzzy de consultas a bancos de dados relacionais. Estudos de casos referentes à aplicabilidade do método desenvolvido são apresentados a fim de mostrar as suas potencialidades em relação aos métodos tradicionais de consultas. / Often, the imprecise and vague information, commonly found in the modeling of real world problems, are not dealt in an appropriate way through conventional queries used in databases. Alternatively, the fuzzy set theory has been considered a very promising tool to treat imprecise and ambiguous information. This work uses the standard SQL language and fuzzy set theory to develop a fuzzy query method for relational databases. Simulation examples are presented to illustrate its potentialities in relation to the traditional query methods.

With the explosive development of the mobile world, web applications and Big Data, new requirements for storage capacity and speed of database systems have arisen. The traditional relational database that has long dominated the marked has received competition because of its lack in speed and scalability. NoSQL is a collective name for databases that are not based on the traditional relational model. NoSQL databases are designed to easily expand their storage capacity while delivering high performance. NoSQL databases have been around for decades but the need for them is relatively new. Our partner expressed a desire to know what differences exist between NoSQL and the traditional relational database. To clarify these differences, we have answered the following questions in this work:  When can a NoSQL database be preferred to a relational database?  What are the differences in database performance? In order to answer these questions, a literature study has been conducted together with experiments where we test which performance differences exist between the selected databases. Performance tests have been performed with the benchmarking tool Yahoo Cloud Serving Benchmark, to verify or falsify the enhanced performance of the NoSQL databases. The hypotheses were falsified in both NoSQL databases. The results showed that the relational database performed better than the cloud based NoSQL databases, but also that the relational database performance deteriorates when the load increased. The results of the experiments are combined with the literature study and together answer our questions. The conclusion is that no database performs better than another one, it is the requirements of the data to be stored. From these requirements, analyses can be made to draw conclusions about what kind of database is preferable.

Now-a-days the chatbot development has been moving from the field of Artificial-Intelligence labs to the desktops and mobile domain experts. In the fastest growing technology world, most smartphone users spend major time in the messaging apps such as Facebook messenger. A chatbot is a computer program that uses messaging channels to interact with users using natural Languages. Chatbot uses appropriate mapping techniques to transform user inputs into a relational database and fetch the data by calling an existing API and then sends an appropriate response to the user to drive its chats. Drawbacks include the need to learn and use chatbot specific languages such as AIML (Artificial Intelligence Markup Language), high botmaster interference, and the use of non-matured technology. In this project, Facebook messenger based chatbot is proposed to provide domain independent, an easy to use, smart, scalable, dynamic and conversational agent in order to get information about CSUSB. It has the unique functionalities which identify user interactions made by their natural language, and the flawless support of various application domains. This provides an ample of unique scalabilities and abilities that will be evaluated in the future phases of this project.

Every day large volumes of data are collected in the form of time series. Time series are collections of events or observations, predominantly numeric in nature, sequentially recorded on a regular or irregular time basis. Time series are becoming increasingly important in nearly every organisation and industry, including banking, finance, telecommunication, and transportation. Banking institutions, for instance, rely on the analysis of time series for forecasting economic indices, elaborating financial market models, and registering international trade operations. More and more time series are being used in this type of investigation and becoming a valuable resource in today�s organisations. This thesis investigates and proposes solutions to some current and important issues in time series data management (TSDM), using Design Science Research Methodology. The thesis presents new models for mapping time series data to relational databases which optimise the use of disk space, can handle different time granularities, status attributes, and facilitate time series data manipulation in a commercial Relational Database Management System (RDBMS). These new models provide a good solution for current time series database applications with RDBMS and are tested with a case study and prototype with financial time series information. Also included is a temporal data model for illustrating time series data lifetime behaviour based on a new set of time dimensions (confidentiality, definitiveness, validity, and maturity times) specially targeted to manage time series data which are introduced to correctly represent the different status of time series data in a timeline. The proposed temporal data model gives a clear and accurate picture of the time series data lifecycle. Formal definitions of these time series dimensions are also presented. In addition, a time series grouping mechanism in an extensible commercial relational database system is defined, illustrated, and justified. The extension consists of a new data type and its corresponding rich set of routines that support modelling and operating time series information within a higher level of abstraction. It extends the capability of the database server to organise and manipulate time series into groups. Thus, this thesis presents a new data type that is referred to as GroupTimeSeries, and its corresponding architecture and support functions and operations. Implementation options for the GroupTimeSeries data type in relational based technologies are also presented. Finally, a framework for TSDM with enough expressiveness of the main requirements of time series application and the management of that data is defined. The framework aims at providing initial domain know-how and requirements of time series data management, avoiding the impracticability of designing a TSDM system on paper from scratch. Many aspects of time series applications including the way time series data are organised at the conceptual level are addressed. The central abstraction for the proposed domain specific framework is the notions of business sections, group of time series, and time series itself. The framework integrates comprehensive specification regarding structural and functional aspects for time series data management. A formal framework specification using conceptual graphs is also explored.

<p>The completion of the Human Genome Project, a remarkable feat by any measure, has provided over three billion bases of reference nucleotides for comparative studies. The next, and perhaps more challenging step is to analyse sequence variation and relate this information to important phenotypes. Most human sequence variations are characterized by structural complexity and, are hence, associated with abnormal functional dynamics. This thesis covers the assembly of a computational platform for verifying these variations, based on accurate, published, experimental data.</p>

Environmental data hold important information regarding humanity’s past, present, and future, and are managed in various ways. The database structure most commonly used in contemporary applications is the relational database. Its usage in the scientific world for managing environmental data is not as popular as in businesses enterprises. Attention is caught by the diverse nature and rapidly growing volume of environmental data that has been increasing substantially in recent. Environmental data for the Chukchi Sea, with its embedded potential oil resources, have become important for characterizing the physical, chemical, and biological environment. Substantive data have been collected recently by researchers from the Chukchi Sea Offshore Monitoring in the Drilling Area: Chemical and Benthos (COMIDA CAB) project. A modified Observations Data Model was employed for storing, retrieving, visualizing and sharing data. Throughout the project-based study, the processes of environmental data heterogeneity reconciliation and relational database model modification and implementation were carried out. Data were transformed into shareable information, which improves data interoperability between different software applications (e.g. ArcGIS and SQL server). The results confirm the feasibility and extendibility of employing relational databases for environmental data management. / text

Per pastaruosius metus ontologijų kūrimas- tikslių formalių specifikacijos terminų ir specifikacijų sąryšių dalykinėje srityje, pradėjo plėstis nuo dirbtinio intelekto srities laboratorijų iki dalykinių sričių ekspertų darbalaukių. Ontologijos tapo dažnos pasauliniame žinių tinkle. Ontologijos naudojamos internete nuo didelių sistematikos klasifikavimo puslapių iki tokių kaip „Yahoo“ ,iki internetinių prekių klasifikavimo ir jų savybių klasifikavimo pardavimui tinklapių, tokių kaip „Amazon.com“. Jeigu informacija yra vienodos struktūros, t.y. visi terminai naudojami tie patys, automatinės paieškos sistemos gali sujungti informacija iš skirtingų šaltinių ir pateikti vartotojui kaip visumą. Ontologija apibrėžia dažnai naudojamą žodyną tyrinėtojams, kuriems reikia dalintis informacija dalykinėje srityje. Į tai įeina dalykinėje srityje kompiuterių interpretuojami apibrėžimai apie pagrindines sąvokas, sąryšius tarp jų. Informacija, žodynai tyrinėtojams, kompiuterių interpretuojami apibrėžimai ir kitą yra saugomi reliacinėse duomenų bazėse. Darbo tikslas: padidinti ontologijų išgavimo iš reliacinių duomenų bazių galimybes sukuriant ir realizuojant tam skirtą algoritmą, leidžiantį atstatyti ontologiją iš duomenų bazės be informacinių nuostolių. / In recent years the development of ontologines- formal specifications of the terms in the domain and relations among them has been expanding from the Artificial-Intelligence laboratories to the desktops of domain experts. Ontologies have become common on the World-Wide Web. The ontologies on the Web range from large taxonomies categorizing Web sites such as on „Yahoo“ to categorizations of products for sale and their features such as on „Amazon.com“. It is a language for encoding knowledge on Web pages to make it understandable to electronic agents searching for information. An ontology defines a common vocabulary for researchers who need to share information in a domain. It includes machine interpretable definitions of basic concepts in the domain and relations among them. Encoded information, vocabulary for researchers, formal specifications of the terms and other are saved in relational databases. The aim of this research is to improve possibilities of querying ontologies when these are kept in relational databases by creating and realizating the algorithm, which allows to transform ontology from relational databases. Experiments have shown that the method works for relation databases which were created by OWL2toRDB algorithm.

<p>The completion of the Human Genome Project, a remarkable feat by any measure, has provided over three billion bases of reference nucleotides for comparative studies. The next, and perhaps more challenging step is to analyse sequence variation and relate this information to important phenotypes. Most human sequence variations are characterized by structural complexity and, are hence, associated with abnormal functional dynamics. This thesis covers the assembly of a computational platform for verifying these variations, based on accurate, published, experimental data.</p>

The problems associated with the effective design and uses of databases are increasing. The information contained in a database is becoming more complex and the size of the data is causing space problems. Technology must continually develop to accommodate this growing need. An inquiry was conducted in order to find effective guidelines that could support queries in general in terms of performance and productivity. Two database management systems were researched to compare die theoretical aspects with the techniques implemented in practice. Microsoft SQL Sewer and MySQL were chosen as the candidates and both were put under close scrutiny. The systems were researched to uncover the methods employed by each to manage queries. The query optimizer forms the basis for each of these systems and manages the parsing and execution of any query. The methods employed by each system for storing data were researched. The way that each system manages table joins, uses indices and chooses optimal execution plans were researched. Adjusted algorithms were introduced for various index processes like B+ trees and hash indexes. Guidelines were compiled that are independent of the database management systems and help to optimize relational databases. Practical implementations of queries were used to acquire and analyse the execution plan for both MySQL and SQL Sewer. This plan along with a few other variables such as execution time is discussed for each system. A model is used for both database management systems in this experiment. / Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2007.

Ontology, an explicit specification of a shared conceptualisation, captures knowledge about a specific domain of interest. The realisation of ontologies, revolutionised the way data stored in relational databases is accessed and manipulated through ontology and database integration. When integrating ontologies with relational databases, several choices exist regarding aspects such as database implementation, ontology language features, and mappings. However, it is unclear which aspects are relevant and when they affect specific choices. This imposes difficulties in deciding which choices to make and their implications on ontology and database integration solutions. Within this study, a decision-making tool that guides users when selecting a technology and developing a solution that integrates ontologies with relational databases is developed. A theory analysis is conducted to determine current status of technologies that integrate ontologies with databases. Furthermore, a theoretical study is conducted to determine important features affecting ontology and database integration, ontology language features, and choices that one needs to make given each technology. Based on the building blocks stated above, an artifact-building approach is used to develop the decision-making tool, and this tool is verified through a proof-of-concept to prove the usefulness thereof. Key terms: Ontology, semantics, relational database, ontology and database integration, mapping, Web information portal. / Information Science / M. Sc. (Information Systems)