Enhanced Security Framework to Develop Secure Data Warehouse

Data Warehouse contains crucial information about organization. This information is utilized by decision maker to analyze the current status and planning the development of the organization. The data warehouse can be easily accessed by an authorized user or by an unauthorized user through unfair means. To preclude this data from unauthorized access, several measures have been taken and a lot of research is going on. In this paper, a framework is proposed to prevent the data from unauthorized access and thereby increases the security of data warehouse

Data Errors And Relevant Dimension Values Detection With A Regular Sparsity Map

Data warehouses require and provide extensive support for data cleaning. They load and continuously refresh huge amounts of data from a variety of sources so the probability that some of the sources contain “dirty data” is high. In this paper we present our regular sparsity map editor which can be used for the purpose of detection of specific data errors in the data warehouse systems. We also discuss how it can be used for a selection of relevant dimension elements

AQUA: um sistema de informação para análise e a validação de parâmetros de qualidade da água em Alqueva

A fiabilidade de dados disponíveis em domínios como os da hidrologia, das infra-estruturas (hidráulicas e sanitárias) e das variáveis biofísicas e sócio-económicas constitui elemento de base fundamental para a alimentação de qualquer sistema de suporte à decisão numa bacia hidrográfica. Isto significa que a transformação de dados brutos em informação com qualidade controlada, deve ser processada de forma eficiente e compatível com exigências dos ambientes hidroinformáticos, dos decisores e do público em geral. O sistema de informação AQuA, criado no âmbito do projecto ODeAnA, tem como objectivo principal o desenvolvimento de um sistema para análise e validação de parâmetros de qualidade da água no Empreendimento de Fins Múltiplos de Alqueva. Este sistema visa recolher os dados provenientes de leituras sobre a qualidade da água efectuadas pelas estações de recolha automática ou por técnicos de laboratório, analisando e validando esses mesmos dados e, posteriormente, integrando os dados limpos e consolidados numa base de dados projectada especificamente para o efeito. O sistema assegura mecanismos de detecção de anomalias, bem como a manutenção de esquemas de resolução para alguns tipos dessas ocorrências. Em termos de análise, é permitida a exploração dos dados, tendo o apoio de algumas funcionalidades de geo-referenciação, e a geração de relatórios ou gráficos específicos. O sistema está preparado para acolher os diversos perfis de utilização definidos: administrador de sistemas, supervisor de resultados, utilizadores internos e externos e público em geral. A implementação do sistema foi organizada e desenvolvida segundo dois módulos operacionais: (i) AQuA Wrapper – responsável por tratar os serviços relacionados com extracção, processamento e armazenamento dos dados provenientes das diversas estações de recolha; (ii) AQuA Web – responsável por tratar os serviços relacionados com validação e exploração dos dados recolhidos. Este módulo disponibiliza uma plataforma Web para acesso aos dados resultantes das leituras efectuadas sobre a qualidade da água nas diversas redes do sistema e constitui o principal elo de ligação dos utilizadores com o sistema de monitorização.Empresa de Desenvolvimento e Infra-estruturas do Alqueva, (EDIA

DESIGN OF AN INTEGRATED AGRARIAN DATA DIMENSIONAL DATA WAREHOUSE

The concept of the Data warehouse was developed to provide a single access point to data from a variety of sources. There isa need to have a single location for the storage and sharing of data that users can easily utilize to make effective and qualitybusiness decisions, rather than trying to traverse the multiple data sources that exist today. Although many frameworks havebeen developed to integrate these sources into a single database, a reliable framework has yet to be developed. A majorhindrance to achieving a reliable warehouse is the poor quality of data obtained from the data transformation stage in theextract, transfer and load process. This poor quality of data contributes to inaccurate and unreliable results and if this data isused for decision making, unforeseen critical business errors can occur. This work reviews the data integration andtransformation process in dimensional data warehouses and proposes a dual structure for data integration and metadata ofmulti-formatted data used for the design of dimensional data warehouse using Agrarian data collected from Ondo State,Nigeria as a case study.Keywords: Data Warehouse, Data Integration, Metadata, Agrarian data

DESIGN OF AN INTEGRATED AGRARIAN DATA DIMENSIONAL DATA WAREHOUSE

The concept of the Data warehouse was developed to provide a single access point to data from a variety of sources. There isa need to have a single location for the storage and sharing of data that users can easily utilize to make effective and qualitybusiness decisions, rather than trying to traverse the multiple data sources that exist today. Although many frameworks havebeen developed to integrate these sources into a single database, a reliable framework has yet to be developed. A majorhindrance to achieving a reliable warehouse is the poor quality of data obtained from the data transformation stage in theextract, transfer and load process. This poor quality of data contributes to inaccurate and unreliable results and if this data isused for decision making, unforeseen critical business errors can occur. This work reviews the data integration andtransformation process in dimensional data warehouses and proposes a dual structure for data integration and metadata ofmulti-formatted data used for the design of dimensional data warehouse using Agrarian data collected from Ondo State,Nigeria as a case study.Keywords: Data Warehouse, Data Integration, Metadata, Agrarian data

Semantic lifting and reasoning on the personalised activity big data repository for healthcare research

The fast growing markets of smart health monitoring devices and mobile applications provide opportunities for common citizens to have capability for understanding and managing their own health situations. However, there are many challenges for data engineering and knowledge discovery research to enable efficient extraction of knowledge from data that is collected from heterogonous devices and applications with big volumes and velocity. This paper presents research that initially started with the EC MyHealthAvatar project and is under continual improvement following the project’s completion. The major contribution of the work is a comprehensive big data and semantic knowledge discovery framework which integrates data from varied data resources. The framework applies hybrid database architecture of NoSQL and RDF repositories with introductions for semantic oriented data mining and knowledge lifting algorithms. The activity stream data is collected through Kafka’s big data processing component. The motivation of the research is to enhance the knowledge management, discovery capabilities and efficiency to support further accurate health risk analysis and lifestyle summarisation

Representing and Reasoning on XForms Document

Forms are the most common way to interface users and Web-based applications. Traditional forms cannot provide the functionality needed to fulfil the requirements of complex applications. As such, there is a need for a more advanced format of forms to support Web-based application. We argued that XForms easily fit into this criterion of forms. In addition, we observed that there is a need for a tool to reason about the forms with respect to user needs and application requirements. We propose to use Description Logic ALCQI to reason about forms generated by XForms

A Goal and Ontology Based Approach for Generating ETL Process Specifications

Data warehouse (DW) systems development involves several tasks such as defining requirements, designing DW schemas, and specifying data transformation operations. Indeed, the success of DW systems is very much dependent on the proper design of the extracting, transforming, and loading (ETL) processes. However, the common design-related problems in the ETL processes such as defining user requirements and data transformation specifications are far from being resolved. These problems are due to data heterogeneity in data sources, ambiguity of user requirements, and the complexity of data transformation activities. Current approaches have limitations on the reconciliation of DW requirement semantics towards designing the ETL processes. As a result, this has prolonged the process of the ETL processes specifications generation. The semantic framework of DW systems established from this study is used to develop the requirement analysis method for designing the ETL processes (RAMEPs) from the different perspectives of organization, decision-maker, and developer by using goal and ontology approaches. The correctness of RAMEPs approach was validated by using modified and newly developed compliant tools. The RAMEPs was evaluated in three real case studies, i.e., Student Affairs System, Gas Utility System, and Graduate Entrepreneur System. These case studies were used to illustrate how the RAMEPs approach can be implemented for designing and generating the ETL processes specifications. Moreover, the RAMEPs approach was reviewed by the DW experts for assessing the strengths and weaknesses of this method, and the new approach is accepted. The RAMEPs method proves that the ETL processes specifications can be derived from the early phases of DW systems development by using the goal-ontology approach

Research on Data Integration Based on Cloud Computing

Abstract: In this study, we give some strategies for selecting the data source and several methods for data integration after analyzing the problems of sharing information resources among universities. According to the characteristics of various types of information resources in the university website, we propose the data integration framework model which combines virtual view method with data warehouse method