Database Click Stream of E-commerce Functional

The availability of e-commerce functionality that suits for user needs in e-commerce applications will increase the sustainability of application usage and can provide benefits for its users. Many e-commerce applications have been developed, but based on the results of previous research, these e-commerce applications do not pay attention to the availability of functionality and its advantages in the application. A database design to store functional clickstream ecommerce is required to determine the number of features that users are accessing. Database application development is the activity of identifying real-world requirements, analyzing requirements, designing system data and functions, and then implementing operations in the system. The database life cycle method is used to build a database in this study. This research has produced a click stream database that has added functional attributes available in e-commerce, which are accessed by users. The results also show the addition of several tables that will facilitate the management of click stream data functionality from e-commerce applications.

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

<p>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.</p>

Review of Contemporary Database Design and Implication for Big Data

In general, databases provide a single comprehensive view suitable for analysis and relevant information for a variety of organizational purposes. The intent of this paper is to review the contemporary database design in terms of data modelling, process modelling, relational databases, and data storage. The review indicates the contemporary relational database architecture provides numerous advantages such as high consistency and availability. However, it is not suitable for big data because its performance decreases as the data grows and faces scalability constraints as it is impossible to scale horizontally, and its vertical growth is limited. An implication here is that big data requires more than a relational database and the traditional SQL.

WHAT IS IN A TWEET? DESIGNING A SEMANTIC LEXICON BASED ON EaD STUDENTS' OPINIONS

Among the various challenges regarding distance education is the necessity of reducing the student dropout rate. In this sense, the present research aimed to contribute to the design of a lexical database focused on emotions and opinions that can be incorporated into a predictive evasion software. For the database design, we used the Scup tool to collect 150 tweets containing distance education students’ opinions and analyzed them in the light of Martin and White’s Appraisal Framework, along with five resources related to the sentiment Analysis field, which were taken from Liu’s work. In addition, we used the Aulete dictionary to describe the lexical units found in our corpus to better fit them into the analysis categories. Results showed 220 opinion tokens, which were identified and labeled according to their polarity. Moreover, these tokens were included in the domains attitude (judgment and appreciation) and graduation (sharp and strong) from the linguistic framework used. The results also indicated the necessity of another resource to help identify the use of figurative language, slangs, and extralinguistic elements, such as GIFS and emojis.

brModelo: An Initiative for Aiding Database Design

The brModelo tool is a initiative of the UFSC Database Group. Its first version was developed in 2005, and its main purpose is to help teaching of relational database design. Compared to similar tools, its main differentials are the support to all steps of the classical database design methodology, user interaction during the logical design step, as well as the support to all extended Entity-Relationship concepts. With more than fifteen years of existence, the brModelo was very well-accepted by the brazilian Database community, which motivated the development and release of several versions of the tool. This article presents the history of brModelo, including its available versions and their functionalities. Additionally, we detail its functionalities and compare it with popular related tools.