Penghasilan dan penilaian video pembelajaran (CD) bagi mata pelajaran Prinsip Ekonomi (BPA 1013) bertajuk permintaan dan penawaran di KUITTHO

Kajian ini dijaiankan untuk meniiai keberkesanan sebuah video pembeiajaran (CD) mata peiajaran Prinsip Ekonomi (BPA 1013) bertajuk Permintaan dan Penawaran. Bagi tujuan tersebut, sebuah video pembelajaran telah dihasilkan membantu pelajar bagi memahami mata pelajaran berkenan semasa proses pengajaran dan pembelajaran berlaku. Video pembelajaran yang dihasilkan ini kemudian dinilai dari aspek proses pengajaran dan pembelajaran, minat dan persepsi responden terhadap ciri-ciri video (audio dan visual). Seramai 60 orang pelajar semester 2 Sarjana Muda Sains Pengurusan di Kolej Universiti Teknologi Tun Hussein Onn telah dipiih bagi membuat penilaian kebolehgunaan produk ini sebagai alat bantuan mengajar di dalam kelas. Semua data yang diperolehi kemudiannya dikumpulkan bagi dianalisis dengan menggunakan perisian "SrarMfKM/ Pac/rageybr Rocaj/ Sb/'eace " (SPSS). Hasil dapatan kajian yang dilakukan jelas menunjukkan video pengajaran yang dihasilkan dan dinilai ini amat sesuai digunakan bagi tujuan memenuhi keperluan proses pengajaran dan pembelajaran subjek ini di dalam kelas

MONITORING DATA PRODUCT QUALITY

The importance of data quality has been considered for many years and is well recognized among practitioners and researchers. A great deal of work has been done and most of the work to date fall under two main categories. One group of scientists has focused on mathematical and statistical model to work at the database layer to introduce constrain based mechanism to prevent data quality problems. Another group has focused on the management of the process of data generation. While the body of knowledge in the area is vast, the practical application of these approaches is still limited. One particular area which is still rarely considered in improving data quality is the development cycle of information system. Recognising this limitation and aiming to provide a practical-orient approach, we take a process centric view, and focus on preventing deficiencies during the IS design. In this paper we propose a process centric framework for data quality monitoring

AN APPROACH TO MONITORING DATA QUALTIY - PRODUCT ORIENTED APPROACH -

The data asset is increasingly becoming one of the top factors in securing organization success. Recognizing the importance of the quality of data, practitioners and researchers have considered for many years ways to improve data quality. Scientists have worked on mathematical and statistical model to introduce constrain based mechanism to prevent data quality problems. Management of the process of data generation has also attracted many researchers. The practical application of most of the proposed approaches is still very limited. Improving data quality with in the development cycle of information system is rarely integrated. Neither process mapping nor data modeling provides sufficient provision to define the required quality that data must conform to. Furthermore, ongoing monitoring of data for quality conformance is not possible without developing cost and time prohibitive data monitoring system. Recognising this limitation and aiming to provide a practical-orient approach, we propose a process centric information system design incorporating data product quality and conformance. In this paper we consider the benefit of a process centric framework for ongoing data quality monitoring

A rule based approach to data certification - applying DQXML for system independent data certification

Many researchers and practitioners have been attracted to improve data quality due to its monumental importance as a key success factor. Mathematical and statistical models have been deployed to information systems to introduce constrain and transaction based mechanisms to prevent data quality related problems. Entire management of the process and roles involved in data generation has also been scrutinized. Vast amount of knowledge base progressed in this area are mostly limited from practical perspective. Quality related meta data is absent from most information systems. Neither process mapping nor data modelling provides sufficient provision to measure quality or certification of data in the information systems. Furthermore, on-going monitoring of data for quality conformance through a separate process is expensive and time consuming. Recognising this limitation and aiming to provide a practical-orient comprehensive approach, I propose a process centric quality focused solution incorporating data product quality, conformance monitoring and certification. I base my work on DQXML developed by Ismael Caballero and deploy rigour of design science to construct InfoGuard. InfoGuard consists of DQXML incorporating quality meta data and an independent data quality monitor that provides certification of data through a rule based process centric framework for on-going data quality monitoring

Discovery and application of data dependencies

Orientador: Prof. Dr. Eduardo Cunha de AlmeidaTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 08/09/2020Inclui referências: p. 126-140Área de concentração: Ciência da ComputaçãoResumo: D ependências de dados (ou, simplesmente, dependências) têm um papel fundamental em muitos aspectos do gerenciam ento de dados. Em consequência, pesquisas recentes têm desenvolvido contribuições para im portante problem as relacionados à dependências. Esta tese traz contribuições que abrangem dois desses problemas. O prim eiro problem a diz respeito à descoberta de dependências com alto poder de expressividade. O objetivo é substituir o projeto m anual de dependências, o qual é sujeito a erros, por um algoritmo capaz de descobrir dependências a partir de dados apenas. N esta tese, estudamos a descoberta de restrições de negação, um tipo de dependência que contorna muitos problemas relacionados ao poder de expressividade de depêndencias. As restrições de negação têm poder de expressividade suficiente para generalizar outros tipos importantes de dependências, e expressar com plexas regras de negócios. No entanto, sua descoberta é com putacionalm ente difícil, pois possui um espaço de busca m aior do que o espaço de busca visto na descoberta de dependências mais simples. Esta tese apresenta novas técnicas na forma de um algoritmo para a descoberta de restrições de negação. Avaliamos o projeto de nosso algoritmo em uma variedade de cenários: conjuntos de dados reais e sintéticos; e núm eros variáveis de registros e colunas. N ossa avaliação m ostra que, em com paração com soluções do estado da arte, nosso algoritmo m elhora significativamente a eficiência da descoberta de restrição de negação em term os de tempo de execução. O segundo problem a diz respeito à aplicação de dependências no gerenciam ento de dados. Primeiro, estudamos a aplicação de dependências na melhoraria da consistência de dados, um aspecto crítico da qualidade dos dados. Uma m aneira comum de m odelar inconsistências é identificando violações de dependências. N esse contexto, esta tese apresenta um m étodo que estende nosso algoritm o para a descoberta de restrições de negação de form a que ele possa retornar resultados confiáveis, m esm o que o algoritm o execute sobre dados contendo alguns registros inconsistentes. M ostram os que é possível extrair evidências dos conjuntos de dados para descobrir restrições de negação que se mantêm aproximadamente. Nossa avaliação mostra que nosso método retorna dependências de negação que podem identificar, com boa precisão e recuperação, inconsistências no conjunto de dados de entrada. Esta tese traz mais um a contribuição no que diz respeito à aplicação de dependências para m elhorar a consistência de dados. Ela apresenta um sistem a para detectar violações de dependências de form a eficiente. Realizam os um a extensa avaliação de nosso sistem a usando comparações com várias abordagens; dados do mundo real e sintéticos; e vários tipos de restrições de negação. Mostramos que os sistemas de gerenciamento de banco de dados comerciais testados com eçam a apresentar baixo desem penho para conjuntos de dados relativam ente pequenos e alguns tipos de restrições de negação. Nosso sistema, por sua vez, apresenta execuções até três ordens de magnitude mais rápidas do que as de outras soluções relacionadas, especialmente para conjuntos de dados maiores e um grande número de violações identificadas. N ossa contribuição final diz respeito à aplicação de dependências na otim ização de consultas. Em particular, esta tese apresenta um sistema para a descoberta automática e seleção de dependências funcionais que potencialmente melhoram a execução de consultas. Nosso sistema com bina representações das dependências funcionais descobertas em um conjunto de dados com representações extraídas de cargas de trabalho de consulta. Essa com binação direciona a seleção de dependências funcionais que podem produzir reescritas de consulta para as consultas de entrada. N ossa avaliação experim ental m ostra que nosso sistem a seleciona dependências funcionais relevantes que podem ajudar na redução do tempo de resposta geral de consultas. Palavras-chave: Perfilamento de dados. Qualidade de dados. Limpeza de dados. Depenência de dados. Execução de consulta.Abstract: Data dependencies (or dependencies, for short) have a fundamental role in many facets of data management. As a result, recent research has been continually driving contributions to central problem s in connection w ith dependencies. This thesis makes contributions that reach two of these problems. The first problem regards the discovery of dependencies of high expressive power. The goal is to replace the error-prone process of m anual design of dependencies with an algorithm capable of discovering dependencies using only data. In this thesis, we study the discovery of denial constraints, a type of dependency that circumvents many expressiveness drawbacks. Denial constraints have enough expressive pow er to generalize other im portant types of dependencies and to express com plex business rules. However, their discovery is com putationally hard since it regards a search space that is bigger than the search space seen in the discovery of sim pler dependencies. This thesis introduces novel algorithm ic techniques in the form of an algorithm for the discovery of denial constraints. We evaluate the design of our algorithm in a variety of scenarios: real and synthetic datasets; and a varying num ber of records and columns. Our evaluation shows that, com pared to state-of-the-art solutions, our algorithm significantly improves the efficiency of denial constraint discovery in terms of runtime. The second problem concerns the application of dependencies in data management. We first study the application of dependencies for improving data consistency, a critical aspect of data quality. A com m on way to m odel data inconsistencies is by identifying violations of dependencies. in that context, this thesis presents a m ethod that extends our algorithm for the discovery of denial constraints such that it can return reliable results even if the algorithm runs on data containing some inconsistent records. A central insight is that it is possible to extract evidence from datasets to discover denial constraints that alm ost hold in the dataset. Our evaluation shows that our method returns denial dependencies that can identify, with good precision and recall, inconsistencies in the input dataset. This thesis makes one m ore contribution regarding the application of dependencies for im proving data consistency. it presents a system for detecting violations of dependencies efficiently. We perform an extensive evaluation of our system that includes comparisons with several different approaches; real-world and synthetic data; and various kinds of denial constraints. We show that the tested com m ercial database m anagem ent systems start underperform ing for relatively small datasets and production dependencies in the form of denial constraints. Our system, in turn, is up to three orders-of-m agnitude faster than related solutions, especially for larger datasets and massive numbers of identified violations. Our final contribution regards the application of dependencies in query optimization. In particular, this thesis presents a system for the automatic discovery and selection of functional dependencies that potentially improve query executions. Our system combines representations from the functional dependencies discovered in a dataset with representations of the query workloads that run for that dataset. This combination guides the selection of functional dependencies that can produce query rewritings for the incoming queries. Our experimental evaluation shows that our system selects relevant functional dependencies, which can help in reducing the overall query response time. Keywords: D ata profiling. D ata quality. D ata cleaning. D ata dependencies. Query execution

A general treatment of dynamic integrity constraints

This paper introduces a general, set-theoretic model for expressing dynamic integrity constraints, i.e., integrity constraints on the state changes that are allowed in a given state space. In a managerial context, such dynamic integrity constraints can be seen as representations of "real world" constraints and business rules. This topic has important practical applications in many business areas. The notions of (direct) transition, reversible and irreversible transition, transition relation, and consistency of a transition relation will be introduced. The expected link with Kripke models (for modal and temporal logics) is also made explicit. Several practical examples of dynamic integrity constraints will illustrate the applicability of the theory. Some important subclasses of dynamic integrity constraints in a database context will be identified, e.g., various forms of cumulativity (which can be regarded as "transitional" inclusion dependencies concerning two different "points in time"), non-decreasing values, integrity constraints on initial and final values, life cycles, changing life cycles, and transition and constant dependencies. Several formal properties of these dependencies will be derived. For instance, it turns out that functional dependencies can be considered as "degenerated" transition dependencies. Also, the distinction between primary keys and alternate keys is reexamined; from a dynamic point of view. (C) 2000 Elsevier Science B.V. All rights reserved

A general treatment of dynamic integrity constraints

This paper introduces a general, set-theoretic model for expressing dynamic integrity constraints, i.e., integrity constraints on the state changes that are allowed in a given state space. In a managerial context, such dynamic integrity constraints can be seen as representations of "real world" constraints and business rules. This topic has important practical applications in many business areas. The notions of (direct) transition, reversible and irreversible transition, transition relation, and consistency of a transition relation will be introduced. The expected link with Kripke models (for modal and temporal logics) is also made explicit. Several practical examples of dynamic integrity constraints will illustrate the applicability of the theory. Some important subclasses of dynamic integrity constraints in a database context will be identified, e.g., various forms of cumulativity (which can be regarded as "transitional" inclusion dependencies concerning two different "points in time"), non-decreasing values, integrity constraints on initial and final values, life cycles, changing life cycles, and transition and constant dependencies. Several formal properties of these dependencies will be derived. For instance, it turns out that functional dependencies can be considered as "degenerated" transition dependencies. Also, the distinction between primary keys and alternate keys is reexamined; from a dynamic point of view. (C) 2000 Elsevier Science B.V. All rights reserved