A Web-Based Tool to Enhance Teaching/Learning Database Normalization

It has been difficult to motivate students to learn database normalization because students think the subject dry and theoretical. A web-based tool is developed to give students an interactive hands-on experience in database normalization process. The tool is suitable for relational data modeling in systems analysis and design and data management courses. This paper describes the web-based tool and its effectiveness in teaching relational data model. The effectiveness of the tool has been evaluated in surveys. The paper shows that the tool has a positive impact on students’ perception

Normalizing Database Normalization Definitions In AIS Text Books

Due to the abstract nature of the definitions for normal forms, over the years the interpretations of the definitions published in the textbooks, both MIS and AIS disciplines, have been differentiated and even deviated from its original form. The concept of deviation from the original form is a phenomenon that linguists call “semantic drift.” The most noticeable deviations are on first and second normal forms (i.e., 1NF and 2NF). Their definitions range from “atomic attribute” to “removing repeating group” for 1NF and from “functional dependency” to “removing partial dependency” in addition to being 1NF for 2NF. The purpose of this paper is to compare definitions of first, second, and third normal forms from the textbooks with those of the earlier forms and to identify shortfalls if there are any

Metaphysics of Internal Controls

A quality internal control system has been seen as a remedy for various corporate governance issues. Two pieces of legislation, the Foreign Corrupt Practices Act (FCPA) and the Sarbanes-Oxley Act (SOX) deal with very different corporate governance issues, but each argue for a similar remedy. Both the FCPA and the SOX legislation argue that improved (or proper) internal controls are necessary to root out bribery of foreign officials, in the case of the FCPA, and (in the case of SOX) to support the accurate preparation of financial statements. An issue that has yet to be resolved is that the quality of internal control systems is subject to subjective assessments of the internal control deficiencies and their impact. This paper presents a mathematical model of internal controls based on Gӧdel number of axioms. This results in the representation of quality internal controls in terms of an integer. This approach also allows for inferences about financial statements and various auditing judgements

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