Conceptual design of an XML FACT repository for dispersed XML document warehouses and XML marts

Since the introduction of eXtensible Markup Language (XML), XML repositories have gained a foothold in many global (and government) organizations, where, e-Commerce and e-business models have maturated in handling daily transactional data among heterogeneous information systems in multi-data formats. Due to this, the amount of data available for enterprise decision-making process is increasing exponentially and are being stored and/or communicated in XML. This presents an interesting challenge to investigate models, frameworks and techniques for organizing and analyzing such voluminous, yet distributed XML documents for business intelligence in the form of XML warehouse repositories and XML marts. In this paper, we address such an issue, where we propose a view-driven approach for modelling and designing of a Global XML FACT (GxFACT) repository under the MDA initiatives. Here we propose the GxFACT using logically grouped, geographically dispersed, XML document warehouses and Document Marts in a global enterprise setting. To deal with organizations? evolving decision-making needs, we also provide three design strategies for building and managing of such GxFACT in the context of modelling of further hierarchical dimensions and/or global document warehouses

Developing and Delivering a Data Warehousing and Mining Course

This paper describes the content and delivery of a Data Warehousing and Mining course that was developed for students in the Eberly College of Business at Indiana University of Pennsylvania. This elective course introduces students to the strategies, technologies, and techniques associated with this growing MIS specialty area. Students learn what is involved in planning, designing, building, using, and managing a data warehouse. Students also learn about how a data warehouse must fit into an over-all corporate data architecture that may include legacy systems, operational data stores, enterprise data warehouses, and data marts. In addition, students are exposed to the different data mining techniques used by organizations to derive information from the data warehouse for strategic and long-term business decision making

Using Ontologies for the Design of Data Warehouses

Obtaining an implementation of a data warehouse is a complex task that forces designers to acquire wide knowledge of the domain, thus requiring a high level of expertise and becoming it a prone-to-fail task. Based on our experience, we have detected a set of situations we have faced up with in real-world projects in which we believe that the use of ontologies will improve several aspects of the design of data warehouses. The aim of this article is to describe several shortcomings of current data warehouse design approaches and discuss the benefit of using ontologies to overcome them. This work is a starting point for discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure

Building a Data Warehouse step by step

Data warehouses have been developed to answer the increasing demands of quality information required by the top managers and economic analysts of organizations. Their importance in now a day business area is unanimous recognized, being the foundation for developing business intelligence systems. Data warehouses offer support for decision-making process, allowing complex analyses which cannot be properly achieved from operational systems. This paper presents the ways in which a data warehouse may be developed and the stages of building it.data warehouse, data mart, data integration, database management system, OLAP, data mining

Extracting, Transforming and Archiving Scientific Data

It is becoming common to archive research datasets that are not only large but also numerous. In addition, their corresponding metadata and the software required to analyse or display them need to be archived. Yet the manual curation of research data can be difficult and expensive, particularly in very large digital repositories, hence the importance of models and tools for automating digital curation tasks. The automation of these tasks faces three major challenges: (1) research data and data sources are highly heterogeneous, (2) future research needs are difficult to anticipate, (3) data is hard to index. To address these problems, we propose the Extract, Transform and Archive (ETA) model for managing and mechanizing the curation of research data. Specifically, we propose a scalable strategy for addressing the research-data problem, ranging from the extraction of legacy data to its long-term storage. We review some existing solutions and propose novel avenues of research.Comment: 8 pages, Fourth Workshop on Very Large Digital Libraries, 201

Business intelligence systems and user's parameters: an application to a documents' database

This article presents earlier results of our research works in the area of modeling Business Intelligence Systems. The basic idea of this research area is presented first. We then show the necessity of including certain users' parameters in Information systems that are used in Business Intelligence systems in order to integrate a better response from such systems. We identified two main types of attributes that can be missing from a base and we showed why they needed to be included. A user model that is based on a cognitive user evolution is presented. This model when used together with a good definition of the information needs of the user (decision maker) will accelerate his decision making process

The Digital Persona and Trust Bank: A Privacy Management Framework

Recently, the government of India embarked on an ambitious project of designing and deploying the Integrated National Agricultural Resources Information System (INARIS) data warehouse for the agricultural sector. The system’s purpose is to support macro level planning. This paper presents some of the challenges faced in designing the data warehouse, specifically dimensional and deployment challenges of the warehouse. We also present some early user evaluations of the warehouse. Governmental data warehouse implementations are rare, especially at the national level. Furthermore, the motivations are significantly different from private sectors. Designing the INARIS agricultural data warehouse posed unique and significant challenges because, traditionally, the collection and dissemination of information are localized

THE DESIGN AND IMPLEMENTATION OF OLAP SYSTEM. CASE STUDY – UNIVERSITY RESEARCH

The aim of this study is to show that multidimensional modelling of existing data in organizations, depending on the topics of interest of managers and multidimensional view of data. It may also provide an effective informational support of managers in decision making, regardless of field of activity. To prove it, this study will design a data model and an OLAP multidimensional analysis of scientific research in education university.OLAP, hyper - cube, n-dimensional cube, conceptual model