Model driven development of secure XML data warehouses

This chapter presents an approach for designing secure Data Warehouses (DWs) that accomplish the conceptual modeling of secure DWs independently from the target platform where the DW has to be implemented, because our complete approach follows the Model Driven Architecture (MDA) and the Model Driven Security (MDS). In most of real world DW projects, the security aspects are issues that usually rely on the DBMS administrators. We argue that the design of these security aspects should be considered together with the conceptual modeling of DWs from the early stages of a DW project, and being able to attach user security information to the basic structures of a Multidimensional (MD) model. In this way, we would be able to generate this information in a semi or automatic way into a target platform and the final DW will better suits the user security requirements.

In the past few years, several conceptual approaches have been proposed for the specification of the main multidimensional (MD) properties of the data warehouse (DW) repository. However, most of them deal with isolated aspects of the DW and do not provide designers with an integrated and standard method for designing the whole DW life cycle (ETL processes, data sources, DW repository and so on).Some approaches are depending on specific platform or neglecting important issues in DW design life cycle. Extraction-transformation-loading (ETL) processes play an important role in data warehouse architecture because they are responsible of integrating data from heterogeneous data sources into the DW repository. This paper proposes a conceptual model to refresh data warehouse by (insert, update, delete) data using ETL processes and considering DW security requirements. Firstly, the proposed ETL model is based on Unified Modeling Language (UML), which allows us to accomplish the conceptual modeling of ETL processes .secondly; this part focuses on how to integrate the proposed ETL model with the DW model. The proposed DW model depends on the Model Driven Architecture (MDA). MDA is a standard framework for software development that addresses the complete life cycle of designing, deploying, integrating, and managing applications by using models in software development. This paper proposes an integrated conceptual model for addressing temporal data warehouse security requirements (CMTDWS).

In 1998, the University of Michigan Health System embarked upon the design, development, and implementation of an enterprise-wide data warehouse, intending to use prioritized business questions to drive its design and implementation. Because of the decentralized nature of the academic health system and the development team's inability to identify and prioritize those institutional business questions, however, a bottom-up approach was used to develop the enterprise-wide data warehouse. Specific important data sets were identified for inclusion, and the technical team designed the system with an enterprise view and architecture rather than as a series of data marts. Using this incremental approach of adding data sets, institutional leaders were able to experience and then further define successful use of the integrated data made available to them. Even as requests for the use and expansion of the data warehouse outstrip the resources assigned for support, the data warehouse has become an integral component of the institution's information management strategy. The authors discuss the approach, process, current status, and successes and failures of the data warehouse.

SummaryThe Model‐Driven Architecture (MDA) is an approach that aligns modeling and automation for software development. By applying such an approach to data warehouse (DW) projects, we can minimize a great deal of time and cost. Furthermore, most of OnLine Analytical Processing (OLAP) platforms seem to be like black boxes that provide wizards only to business intelligence developers to create and manipulate OLAP objects without allowing their sustainability and migration from a platform to another. That is why many works in the literature have proposed using the MDA approach in DW projects. However, most of them have mainly focused on the generation of the DW relational model from the conceptual one, and they overlooked the OLAP model and the cube implementation.To deal with this problem, we propose in this paper an MDA solution to automate the process of getting OLAP cube and its implementation through a set of metamodels and automatic transformations among them. In fact, the proposal generates the OLAP and DW relational models (PSMs) from the conceptual one, using also a PDM model that describes the target business intelligence platform. After that, the source code to create the cube is got from both PSM models. For this aim, we define a set of transformation rules implemented using the Atlas transformation language. Finally, a case study will be provided to validate our approach.

Adding semantic modules to improve goal-oriented analysis of data warehouses using I-star

The movement to real-time is the latest development in business intelligence and data warehousing. In general terms, a data warehouse costs in excess of one million dollars and it is estimated that approximately 67% of the efforts fail. This is a significant factor when data warehouses are being developed for small businesses. Organizations are compelled to use their own data to produce reports that may indicate or better estimate tendencies in the market. This study examines six small businesses to determine best practices for creating data warehouses. Companies included in this analysis are in the fields of health care, manufacturing, education, and financial institutions. In this study, IT professionals and management were interviewed to determine which practices were highly valued in the development of data warehouses. In this paper, best practices for successful use of data warehouses are analyzed by examining six companies to determine the design, development and implementation.

For a large company the speed of processing and distribution of data is very important. However, at Company XYZ, this has not yet been implemented in its entirety. This hinders the progress of the company. To be able to solve these problems, a storage system is needed that can access data more quickly and effectively by using a data warehouse system. This study discusses the design and construction of a data warehouse using the Kimball method. The data warehouse schema is created using SQL Server Management Studio and Microsoft SQL Server Integration Service. The analysis results from the data warehouse are displayed in the form of a dashboard using Microsoft PowerBI and a web-based application. Judging from the dashboard display that has been made, the production environment at PT Bio Farma is stable so it does not interfere with production quality. The data is entered into a web-based application, with the percentage of application feasibility of 84% after testing.

Data warehouse (DW) systems integrate data from heterogeneous sources and are used by decision makers to analyze the status and the development of an organization. Traditionally, requirement analysis approaches for DWs have focused purely on information needs of decision makers, without considering other kinds of requirements such as security or performance. But modeling these issues in the early stages of the development is a cornerstone for building a DW that satisfies user expectations. In this paper, we define the two kinds of requirements for data warehousing as information and quality-of-service requirements and combine them in a comprehensive approach based on MDA (model driven architecture). This allows a separation of concerns to model requirements without losing the connection between information and quality-of-service, also in the following conceptual or logical design stages. Finally, in this paper, we introduce a security requirement model for data warehousing, and a three-step process for modeling security requirements, thus illustrating the applicability of our approach with an example.

Virtualization is very important factor to reduce cost of data warehouse deployment. It can reduce cost as well as tremendous pressure of managing devices, storage servers, application models and man power. In current era, data warehouse is very effective concept that can make huge impact in organizational decision support system. Unlike database systems, data warehouse systems take large amount of time and cost to deploy and develop in-house system for an organization. This is the reason that people now think about cloud computing as a solution of the problem instead of implementing their own data warehouse system. This paper is about, how cloud computing environment can be established as an alternative of data warehouse system. It will also throw some light about better environment choice for the organizational need. Organizational data warehouse and EC2 (Elastic Cloud Computing) are discussed with different parameters like ROI, security, scalability, robustness of data, maintenance of system etc. Paper will help in comparative analysis of both the systems.

Data warehouses tend to evolve, because of changes in data sources and business requirements of users. All these kinds of changes must be properly handled, therefore, data warehouse development is never-ending process. In this paper we propose the evolution-oriented user-centric data warehouse design, which on the one hand allows to manage data warehouse evolution automatically or semi-automatically, and on the other hand it provides users with the understandable, easy and transparent data analysis possibilities. The proposed approach supports versions of data warehouse schemata and data semantics.

During the development of the data warehouse (DW), too much data is transformed, integrated, structured, cleansed, and grouped in a single structure that is the DW. These various types of changes could lead to data corruption or data manipulation. Therefore, DW testing is a very critical stage in the DW development process.A number of attempts were made to describe how the testing process should take place in the DW environment. In this paper, I will state briefly these testing approaches, and then a proposed matrix will be used to evaluate and compare these approaches. Afterwards, I will highlight the weakness points that exist in the available DW testing approaches. Finally, I will describe how I will fill the gap in the DW testing in my PhD by developing a DW Testing Framework presenting briefly its architecture. Then, I will state the scope of work that I am planning to address and what type of limitations that exist in this area that I am expecting to experience. In the end, I will conclude my work and state possible future work in the field of DW testing.

PurposeDespite the increasing role of the data warehouse as a supportive decision-making tool in today's business world, academic research for measuring its effectiveness has been lacking. This paucity of academic interest stimulated us to evaluate data warehousing effectiveness in the organizational context of Jordanian banks.Design/methodology/approachThis paper develops a theoretical model specific to the data warehouse system domain that builds on the DeLone and McLean model. The model is empirically tested by means of structural equation modelling applying the partial least squares approach and using data collected in a survey questionnaire from 127 respondents at Jordanian banks.FindingsEmpirical data analysis supported that data quality, system quality, user satisfaction, individual benefits and organizational benefits have made strong contributions to data warehousing effectiveness in our organizational data context.Practical implicationsThe results provide a better understanding of the data warehouse effectiveness and its importance in enabling the Jordanian banks to be competitive.Originality/valueThis study is indeed one of the first empirical attempts to measure data warehouse system effectiveness and the first of its kind in an emerging country such as Jordan.

We introduce an object-oriented approach to transform a star-schema of a legacy data warehouse system into a Dimensional Object Model (DOM) (Firestone, 1998), in order to take advantage of the flexibility of the object paradigm. We have applied the Object-Oriented Software Engineering (OOSE) process, proposed by (Jacobson et al., 1992), to describe the life cycle of a data warehouse and to clarify the context of our intervention. By means of OOSE we also define the target architecture and the components that will ensure the development of reuse-supporting data warehouse systems. In a sense, a change tolerant architecture is proposed, as referred to by (Jacobson, 1998). The solution proposed applies the object paradigm for the star-schema decomposition in a three-layer architecture, separating the components in logical, interface and data management layers. This decomposition minimizes the dependence among the components and increases their reusability. The main contribution of this work is that, by transforming the star-schema into a DOM, we are preparing the system to evolve through any object-oriented data warehouse development methodology. In a context of many already existing data warehouse systems based on relational models, this transformation sounds especially interesting.

Business Intelligence (BI) applications allow their users to query, understand, and analyze existing data within their organizations in order to acquire useful knowledge, thus making better decisions. The core of BI applications is a data warehouse (DW) which integrates several heterogeneous data sources, mainly structured transactional databases. However, a new trend has emerged, since users of BI applications no longer limit their analysis to structured databases, and they also need unstructured sources to empower their analysis (i.e. data from the Web, company-internal reports, emails, etc.). Therefore, the ability to manage this available information is crucial for the success of the decision making process. Unfortunately, this new useful data, although easily available for companies, are often found in unstructured data (such as web pages, internal reports, emails, and so on) which make it unuseful for feeding the DW beneath BI applications. To shed light on this scenario, in this paper, we propose a novel model for using ontologies to integrate Question Answering (QA) systems in the DW. The great benefit of using QA is that specific pieces of data (i.e. answers) that will empower the analysis are detected, obtained and returned from unstructured data in order to feed the DW.

Majority of the Higher Learning Institutions are being implemented with information management systems for all the core activities ranging from admission, registration, alumni, graduate and academy operations. The data generated by these integrated information systems are transactional in nature and has been increasing exponentially. However, the use and value of the data has not been fully explored for driving decision-making process. This paper explores the importance of Agile Analytics in Business Intelligence and Data Warehouse development among Higher Learning Institutions. The paper concludes by outlining future directions relating to the development and implementation of an institutional project on data analytics.