Security Architecture for Tanzania Higher Learning Institutions’ Data Warehouse

In this paper we developed security architecture for the higher learning institutions in Tanzania which considers security measures to be taken at different level of the higher learning institutions’ data warehouse architecture. The primary objectives of the study was to identify security requirements of the higher learning institutions data warehouses and then study the existing security systems in and finally develop and architecture based on the requirements extracted from the study. The study was carried at three different universities in Tanzania by carrying out interviews, study of the existing systems in respective institutions and a literature review of the existing data warehouses systems and architectures. The result was the security requirements identified which lead to the development of the security architecture comprising security in source systems, data, and services to be offered by the DW, applications which use DW, networks and other physical infrastructure focusing on security controls like authentication, role-based access control, role separation of privileged users, storage of data, secure transfer of data, protective monitoring/ intrusion detection, penetration testing, trusted/secure endpoints and physical protection. Keywords: Data warehouse, security architecture, higher learning institution

Security Issues in Data Warehouse

Data Warehouse (DWH) provides storage for huge amounts of historical data from heterogeneous operational sources in the form of multidimensional views, thus supplying sensitive and useful information which help decision-makers to improve the organization’s business processes. A data warehouse environment must ensure that data collected and stored in one big repository are not vulnerable. A review of security approaches specifically for data warehouse environment and issues concerning each type of security approach have been provided in this paper

The Role of Metadata for Effective Data Warehouse

Metadata efficient method for managing Data Warehouse (DW). It is also an effective tool in reducing the time or speed to answer queries. In addition, it achieved capabilities of the integration and standardization, thus lead to faster, clear and accurate decision-making in the right time. This paper provides the definition of metadata concept, and using metadata in Data Cleaning; which it identify the sources, types of fields, and choose the appropriate algorithm. In addition, useful in Decision Support System (DSS); which it improve efficiency of analysis and reduces response time of quer

An ETL Metadata Model for Data Warehousing

Metadata is essential for understanding information stored in data warehouses. It helps increase levels of adoption and usage of data warehouse data by knowledge workers and decision makers. A metadata model is important to the implementation of a data warehouse; the lack of a metadata model can lead to quality concerns about the data warehouse. A highly successful data warehouse implementation depends on consistent metadata. This article proposes adoption of an ETL (extracttransform-load) metadata model for the data warehouse that makes subject area refreshes metadata-driven, loads observation timestamps and other useful parameters, and minimizes consumption of database systems resources. The ETL metadata model provides developers with a set of ETL development tools and delivers a user-friendly batch cycle refresh monitoring tool for the production support team

Examining Quality Factors Influencing the Success of Data Warehouse

Increased organizational dependence on data warehouse (DW) systems has drived the management attention towards improving data warehouse systems to a success. However, the successful implementation rate of the data warehouse systems is low and many firms do not achieve intended goals. A recent study shows that improves and evaluates data warehouse success is one of the top concerns facing IT/DW executives. Nevertheless, there is a lack of research that addresses the issue of the data warehouse systems success. In addition, it is important for organizations to learn about quality needs to be emphasized before the actual data warehouse is built. It is also important to determine what aspects of data warehouse systems success are critical to organizations to help IT/DW executives to devise effective data warehouse success improvement strategies. Therefore, the purpose of this study is to further the understanding of the factors which are critical to evaluate the success of data warehouse systems. The study attempted to develop a comprehensive model for the success of data warehouse systems by adapting the updated DeLone and McLean IS Success Model. Researcher models the relationship between the quality factors on the one side and the net benefits of data warehouse on the other side. This study used quantitative method to test the research hypotheses by survey data. The data were collected by using a web-based survey. The sample consisted of 244 members of The Data Warehouse Institution (TDWI) working in variety industries around the world. The questionnaire measured six independent variables and one dependent variable. The independent variables were meant to measure system quality, information quality, service quality, relationship quality, user quality, and business quality. The dependent variable was meant to measure the net benefits of data warehouse systems. Analysis using descriptive analysis, factor analysis, correlation analysis and regression analysis resulted in the support of all hypotheses. The research results indicated that there are statistically positive causal relationship between each quality factors and the net benefits of the data warehouse systems. These results imply that the net benefits of the data warehouse systems increases when the overall qualities were increased. Yet, little thought seems to have been given to what the data warehouse success is, what is necessary to achieve the success of data warehouse, and what benefits can be realistically expected. Therefore, it appears nearly certain and plausible that the way data warehouse systems success is implemented in the future could be changed

An Integrative and Uniform Model for Metadata Management in Data Warehousing Environment

Due to the increasing complexity of data warehouses, a centralized and declarative management of metadata is essential for data warehouse administration, maintenance and usage. Metadata are usually divided into technical and semantic metadata. Typically, current approaches only support subsets of these metadata types, such as data movement metadata or multidimensional metadata for OLAP. In particular, the interdependencies between technical and semantic metadata have not yet been investigated sufficiently. The representation of these interdependencies form an important prerequisite for the translation of queries formulated at the business concept level to executable queries on physical data. Therefore, we suggest a uniform and integrative model for data warehouse metadata. This model uses a uniform representation approach based on the Uniform Modeling Language (UML) to integrate technical and semantic metadata and their interdependencies

Cloud BI: A Multi-party Authentication Framework for Securing Business Intelligence on the Cloud

Business intelligence (BI) has emerged as a key technology to be hosted on Cloud computing. BI offers a method to analyse data thereby enabling informed decision making to improve business performance and profitability. However, within the shared domains of Cloud computing, BI is exposed to increased security and privacy threats because an unauthorised user may be able to gain access to highly sensitive, consolidated business information. The business process contains collaborating services and users from multiple Cloud systems in different security realms which need to be engaged dynamically at runtime. If the heterogamous Cloud systems located in different security realms do not have direct authentication relationships then it is technically difficult to enable a secure collaboration. In order to address these security challenges, a new authentication framework is required to establish certain trust relationships among these BI service instances and users by distributing a common session secret to all participants of a session. The author addresses this challenge by designing and implementing a multiparty authentication framework for dynamic secure interactions when members of different security realms want to access services. The framework takes advantage of the trust relationship between session members in different security realms to enable a user to obtain security credentials to access Cloud resources in a remote realm. This mechanism can help Cloud session users authenticate their session membership to improve the authentication processes within multi-party sessions. The correctness of the proposed framework has been verified by using BAN Logics. The performance and the overhead have been evaluated via simulation in a dynamic environment. A prototype authentication system has been designed, implemented and tested based on the proposed framework. The research concludes that the proposed framework and its supporting protocols are an effective functional basis for practical implementation testing, as it achieves good scalability and imposes only minimal performance overhead which is comparable with other state-of-art methods

A Prototype Model for Data Warehouse Security Based on Metadata

The aim of this paper is to give an overview of security relevant aspects of existing OLAP/Data Warehouse solutions, an area which has seen rather little interest from product developers and is only beginning to be discussed in the research community. Following this description of the current situation, a metadata driven approach implemented as part of the WWW-EIS-DWH project is presented in detail. The prototype focuses on the technical realisation and is intended not to be open for use in different security policies. 1

Strategic alignment in data warehouses : two case studies

This research investigates the role of strategic alignment in the success of data warehouse implementation. Data warehouse technology is inherently complex, requires significant capital investment and development time. Many organizations fail to realize the full benefits from it. While failure to realize benefits has been attributed to numerous causes, ranging from technical to organizational reasons, the underlying strategic alignment issues have not been studied. This research confirms, through two case studies, that the successful adoption of the data warehouse depends on its alignment to the business plans and strategy. The research found that the factors that are critical to the alignment of data warehouses to business strategy and plans are (a) joint responsibility between data warehouse and business managers, (b) alignment between data warehouse plan and business plan, (c) business user satisfaction, (d) flexibility in data warehouse planning and (e) technical integration of the data warehouse. In the case studies, the impact of strategic alignment was visible both at implementation and use levels. The key findings from the case studies are that a) Senior management commitment and involvement are necessary for the initiation of the data warehouse project. The awareness and involvement of data warehouse managers in corporate strategies and a high level of joint responsibility between business and data warehouse managers is critical to strategic alignment and successful adoption of the data warehouse. b) Communication of the strategic direction between the business and data warehouse managers is important for the strategic alignment of the data warehouse. Significant knowledge sharing among the stakeholders and frequent communication between the data warehouse managers and users facilitates better understanding of the data warehouse and its successful adoption. c) User participation in the data warehouse project, perceived usefulness of the data warehouse, ease of use and data quality (accuracy, consistency, reliability and timelines) were significant factors in strategic alignment of the data warehouse. d) Technology selection based on its ability to address business and user requirements, and the skills and response of the data warehousing team led to better alignment of the data warehouse to business plans and strategies. e) The flexibility to respond to changes in business needs and flexibility in data warehouse planning is critical to strategic alignment and successful adoption of the data warehouse. Alignment is seen as a process requiring continuous adaptation and coordination of plans and goals. This research provides a pathway for facilitating successful adoption of data warehouse. The model developed in this research allows data warehouse professionals to ensure that their project when implemented, achieve the strategic goals and business objectives of the organization