Economic data bank management in a developing nation

This dissertation describes the results of a research project which was undertaken at Loughborough University of Technology. The basic objectives of the research project were: (1) to investigate the management elements required for organising the development of an Economic Data Bank (EDB), with particular emphasis on the requirements of a developing nation; (2) to investigate the sociological, political and technical implications associated with organising the development of an EDB in a developing nation. A theoretical framework was established for this study. This was dene after an extensive search and review of literature was performed in the areas of data and data base management systems, management information systems, and computer technology in general. [Continues.

DESIGNING A GENERALIZED MULTIPLE CRITERIA DECISION SUPPORT SYSTEM

Decision support systems are of many kinds depending on the models and techniques employed in them. Multiple criteria decision making techniques constitute an important class of DSS with unique software requirements. This paper stresses the importance of interactive MCDM methods since these facilitate learning through all stages of the decision making process. We first describe some features of Multiple Criteria Decision Support Systems ( MCDSSs) that distinguish them from classical DSSs. We then outline a software architecture for a MCDSS which has three basic components: a Dialog Manager, an MCDM Model Manager, and a Data Manager. We describe the interactions that occur between these three software components in an integrated MCDSS and outline a design for the Data Manager which is based on a concept of levels of data abstraction.Information Systems Working Papers Serie

HISTORICAL DATABASES - IT'S ABOUT TIME!

Much recent research in the database community focuses on ways to expand the modelling capabilities of database management systems. The driving force behind this research is the growing size and sophistication of the user community, whose needs and applications seem to always be several steps ahead of the technology. One of the areas where considerable progress has been made in this regard is in the extension of existing data models to represent and manage the temporal dimension of data. In this paper we examine how these enhanced modelling capabilities will increase the functionality of the database management systems of tomorrow. We also introduce the notion of Temporal Representation Transparency as the appropriate abstraction mechanism for providing this increased functionality with minimum burden to the user.Information Systems Working Papers Serie

Digitale Datenbanken: Eine Medientheorie im Zeitalter von Big Data

Wir leben längst nicht mehr nur im Informationszeitalter, sondern in der Ära von Big Data. In dieser steht die Datenbank gleichzeitig für die riesigen Erkenntnispotenziale von Informationssammlungen wie für die bedrohlichen Informationsexzesse der digitalen Medienkultur. Zudem bezeichnet der Begriff konkrete Technologien und Verfahren der Sammlung und Bereitstellung von digitalen Informationen. Zwischen diesen sehr unterschiedlichen Auffassungen gilt es, Datenbanken medientheoretisch zu verorten. Marcus Burkhardt zeichnet die Geschichte der Datenbanken nach und fragt, wie technische Verfahren der Verwaltung digitaler Informationen bedingen, was auf welche Weise in Datenbanken gefunden und durch sie gewusst werden kann.We have long moved on from a mere information age to the era of big data. Here, databases represent both the enormous potential for gaining knowledge of data collection and the alarming information excesses of digital culture. Furthermore, the term refers to concrete technologies and processes of gathering and accessing digital information. Media theory has to locate databases in between these very different conceptions. Marcus Burkhardt retraces the history of databases and asks how technical procedures of processing digital information determine what can be found how in databases and what knowledge can be gained through them

Digitale Datenbanken

We have long moved on from a mere information age to the era of big data. Here, databases represent both the enormous potential for gaining knowledge of data collection and the alarming information excesses of digital culture. Furthermore, the term refers to concrete technologies and processes of gathering and accessing digital information. Media theory has to locate databases in between these very different conceptions. Marcus Burkhardt retraces the history of databases and asks how technical procedures of processing digital information determine what can be found how in databases and what knowledge can be gained through them

On the Discovery of Semantically Meaningful SQL Constraints from Armstrong Samples: Foundations, Implementation, and Evaluation

A database is said to be C-Armstrong for a finite set Σ of data dependencies in a class C if the database satisfies all data dependencies in Σ and violates all data dependencies in C that are not implied by Σ. Therefore, Armstrong databases are concise, user-friendly representations of abstract data dependencies that can be used to judge, justify, convey, and test the understanding of database design choices. Indeed, an Armstrong database satisfies exactly those data dependencies that are considered meaningful by the current design choice Σ. Structural and computational properties of Armstrong databases have been deeply investigated in Codd’s Turing Award winning relational model of data. Armstrong databases have been incorporated in approaches towards relational database design. They have also been found useful for the elicitation of requirements, the semantic sampling of existing databases, and the specification of schema mappings. This research establishes a toolbox of Armstrong databases for SQL data. This is challenging as SQL data can contain null marker occurrences in columns declared NULL, and may contain duplicate rows. Thus, the existing theory of Armstrong databases only applies to idealized instances of SQL data, that is, instances without null marker occurrences and without duplicate rows. For the thesis, two popular interpretations of null markers are considered: the no information interpretation used in SQL, and the exists but unknown interpretation by Codd. Furthermore, the study is limited to the popular class C of functional dependencies. However, the presence of duplicate rows means that the class of uniqueness constraints is no longer subsumed by the class of functional dependencies, in contrast to the relational model of data. As a first contribution a provably-correct algorithm is developed that computes Armstrong databases for an arbitrarily given finite set of uniqueness constraints and functional dependencies. This contribution is based on axiomatic, algorithmic and logical characterizations of the associated implication problem that are also established in this thesis. While the problem to decide whether a given database is Armstrong for a given set of such constraints is precisely exponential, our algorithm computes an Armstrong database with a number of rows that is at most quadratic in the number of rows of a minimum-sized Armstrong database. As a second contribution the algorithms are implemented in the form of a design tool. Users of the tool can therefore inspect Armstrong databases to analyze their current design choice Σ. Intuitively, Armstrong databases are useful for the acquisition of semantically meaningful constraints, if the users can recognize the actual meaningfulness of constraints that they incorrectly perceived as meaningless before the inspection of an Armstrong database. As a final contribution, measures are introduced that formalize the term “useful” and it is shown by some detailed experiments that Armstrong tables, as computed by the tool, are indeed useful. In summary, this research establishes a toolbox of Armstrong databases that can be applied by database designers to concisely visualize constraints on SQL data. Such support can lead to database designs that guarantee efficient data management in practice

Организация баз данных

Опис дисципліни. Дисципліна присвячена вивченню теоретичних основ, практичних методів і засобів побудови баз даних, а також питань, пов'язаних з життєвим циклом, підтримкою і супроводом баз даних. Розглядаються основні поняття баз даних, способи їх класифікації, принципи організації структур даних і відповідні їм типи систем управління базами даних (СУБД). Детально вивчається реляційна модель даних, теорія нормалізації та СУБД, що відповідають цій моделі (на прикладі СУБД MS SQL Server), стандартна мова запитів до реляційних СУБД - SQL, методи представлення складних структур даних засобами реляційної СУБД. Розглядаються питання організації колективного доступу до даних, вводяться поняття посилальної цілісності і семантичної цілісності даних, транзакцій і пов'язані з ними проблеми і методи їх вирішення. Розглядаються питання збереження і безпеки даних, методи резервного копіювання та стиснення даних. Дається огляд ієрархічних, нереляційних і постреляціонних, об'єктно-орієнтованих, повнотекстових, мережевих і розподілених СУБД. Вивчається побудова ER-моделі засобами Entity Framework Visual Studio, створення додатка для роботи з базами даних в середовищі розробки Visual Studio на мові С #.Анотація дисципліни «Організація баз даних». Метою викладання дисципліни є формування у студентів розуміння ролі автоматизованих банків даних в створенні інформаційних систем. Завданнями вивчення дисципліни є: вивчення моделей даних, які підтримуються різними системами управління базами даних (СУБД); вивчення нереляційних моделей; вивчення елементів теорії реляційних баз даних; знайомство з принципами побудови СУБД; вивчення розподілених СУБД і засобів розробки додатків для цих СУБД.Abstract "Database Organization" discipline. The purpose of teaching is to develop students' understanding the role of automated data banks in the creation of information systems. The objectives of the discipline are: study data models supported by different database management systems (DBMS); the study of non-relational models, the theory of relational databases, the principles of creating a database, the distributed database and application development tools for these databases.Аннотация дисциплины «Организация баз данных». Целью преподавания дисциплины является формирование у студентов понимания роли автоматизированных банков данных в создании информационных систем. Задачами изучения дисциплины являются: изучение моделей данных, поддерживаемых различными системами управления базами данных (СУБД); изучение нереляционных моделей; изучение элементов теории реляционных баз данных; знакомство с принципами построения СУБД; изучение распределенных СУБД и средств разработки приложений для этих СУБД