Modeling views in the layered view model for XML using UML

In data engineering, view formalisms are used to provide flexibility to users and user applications by allowing them to extract and elaborate data from the stored data sources. Conversely, since the introduction of Extensible Markup Language (XML), it is fast emerging as the dominant standard for storing, describing, and interchanging data among various web and heterogeneous data sources. In combination with XML Schema, XML provides rich facilities for defining and constraining user-defined data semantics and properties, a feature that is unique to XML. In this context, it is interesting to investigate traditional database features, such as view models and view design techniques for XML. However, traditional view formalisms are strongly coupled to the data language and its syntax, thus it proves to be a difficult task to support views in the case of semi-structured data models. Therefore, in this paper we propose a Layered View Model (LVM) for XML with conceptual and schemata extensions. Here our work is three-fold; first we propose an approach to separate the implementation and conceptual aspects of the views that provides a clear separation of concerns, thus, allowing analysis and design of views to be separated from their implementation. Secondly, we define representations to express and construct these views at the conceptual level. Thirdly, we define a view transformation methodology for XML views in the LVM, which carries out automated transformation to a view schema and a view query expression in an appropriate query language. Also, to validate and apply the LVM concepts, methods and transformations developed, we propose a view-driven application development framework with the flexibility to develop web and database applications for XML, at varying levels of abstraction

Data Warehouse Design and Management: Theory and Practice

The need to store data and information permanently, for their reuse in later stages, is a very relevant problem in the modern world and now affects a large number of people and economic agents. The storage and subsequent use of data can indeed be a valuable source for decision making or to increase commercial activity. The next step to data storage is the efficient and effective use of information, particularly through the Business Intelligence, at whose base is just the implementation of a Data Warehouse. In the present paper we will analyze Data Warehouses with their theoretical models, and illustrate a practical implementation in a specific case study on a pharmaceutical distribution companyData warehouse, database, data model.

Knowledge and Metadata Integration for Warehousing Complex Data

With the ever-growing availability of so-called complex data, especially on the Web, decision-support systems such as data warehouses must store and process data that are not only numerical or symbolic. Warehousing and analyzing such data requires the joint exploitation of metadata and domain-related knowledge, which must thereby be integrated. In this paper, we survey the types of knowledge and metadata that are needed for managing complex data, discuss the issue of knowledge and metadata integration, and propose a CWM-compliant integration solution that we incorporate into an XML complex data warehousing framework we previously designed.Comment: 6th International Conference on Information Systems Technology and its Applications (ISTA 07), Kharkiv : Ukraine (2007

Using Fuzzy Linguistic Representations to Provide Explanatory Semantics for Data Warehouses

A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, "whether a total sales amount 1,000 items indicates a good or bad sales performance" is still unclear. From the decision makers' point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses

Warehousing of object oriented petroleum data for knowledge mapping

Australia produces a-third of world?s natural resources. Enormous amounts of energy and financial resources are expended in order to tap these natural reserves from the earth?s surface. Vast amounts of these resources, however, remain unexplored and under exploited. Data pertaining natural resources, such as mineral and petroleum, are, in general, heterogeneous and complex in nature. Volumes of these types of data are geographically distributed among many companies in Australia and abroad. The existing historical resources data are logically and physically organized using warehousing techniques. Entity relationship (ER) and object oriented (OO) data mapping techniques are used for analyzing the data entities, dimensions and objects. In this paper object oriented data and warehousing of object class data models have been described. Data mining techniques can be employed to explore many more resources hidden, under great depths of the earth?s crust, without additional efforts of exploration and development. Warehoused object oriented resources data can significantly reduce the complexity of the resources data structuring and enhance the data integration and information sharing among various operational units of the resources industry. Large amount of financial inputs can be saved if these technologies are successfully implemented in the resources industry

Assessing the Flexibility of a Service Oriented Architecture to that of the Classic Data Warehouse

The flexibility of a service oriented architecture (SOA) is compared to that of the classic data warehouse across three categories: (1) source system access, (2) integration and transformation, and (3) end user access. The findings suggest that an SOA allows better upgrade and migration flexibility if back-end systems expose their source data via adapters. However, the providers of such adapters must deal with the complexity of maintaining consistent interfaces. An SOA also appears to provide more flexibility at the integration tier due to its ability to merge batch with real-time source system data. This has the potential to retain source system data semantics (e.g., code translations and business rules) without having to reproduce such logic in a transformation tier. Additionally, the tight coupling of operational metadata and source system data within XML in an SOA allows more flexibility in downstream analysis and auditing of output . SOA does lag behind the classic data warehouse at the end user level, mainly due to the latter\u27s use of mature SQL and relational database technology. Users of all technical levels can easily work with these technologies in the classic data warehouse environment to query data in a number of ways. The SOA end user likely requires developer support for such activities

Managing Metadata in Data Warehouses: Pitfalls and Possibilities

This paper motivates a comprehensive academic study of metadata and the roles that metadata plays in organizational information systems. While the benefits of metadata and challenges in implementing metadata solutions are widely addressed in practitioner publications, explicit discussion of metadata in academic literature is rare. Metadata, when discussed, is perceived primarily as a technology solution. Integrated management of metadata and its business value are not well addressed. This paper discusses both the benefits offered by and the challenges associated with integrating metadata. It also describes solutions for addressing some of these challenges. The inherent complexity of an integrated metadata repository is demonstrated by reviewing the metadata functionality required in a data warehouse: a decision support environment where its importance is acknowledged. Comparing this required functionality with metadata management functionalities offered by data warehousing software products identifies crucial gaps. Based on these analyses, topics for further research on metadata are proposed