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

Grid-VirtuE: a layered architecture for grid virtual enterprises

A grid virtual enterprise is a community of independent enterprises concerned with a particular sector of the economy. Its members (nodes) are small or medium size enterprises (SME) engaged in bilateral transactions. An important principle of a grid virtual enterprise is the lack of any global "guiding force", with each member of the community making its own independent decisions. In this paper we describe Grid-VirtuE, a three-layer architecture for grid virtual enterprises. The top layer of the architecture, representing its ultimate purpose, is an environment in which grid virtual enterprises can be modeled and implemented. This layer is supported by middleware infrastructure for grids, providing a host of grid services, such as node-to-node communication, bilateral transactions, and data collection. The bottom layer is essentially a distributed data warehouse for storing, sharing and analyzing the large amounts of data generated by the grid. Among other functionalities, the warehouse handles the dissemination of data among the members of the grid; it confronts issues of data magnitude with an aging mechanism that aggregates old data at a lower level of detail; and it incorporates privacy-preserving features that retain the confidentiality of individual members. Warehouse information is also used for data and process mining, aimed at analyzing the behavior of the enterprise, and subsequently inducing evolutionary changes that will improve its performance.A grid virtual enterprise is a community of independent enterprises concerned with a particular sector of the economy. Its members (nodes) are small or medium size enterprises (SME) engaged in bilateral transactions. An important principle of a grid virtual enterprise is the lack of any global "guiding force", with each member of the community making its own independent decisions. In this paper we describe Grid-VirtuE, a three-layer architecture for grid virtual enterprises. The top layer of the architecture, representing its ultimate purpose, is an environment in which grid virtual enterprises can be modeled and implemented. This layer is supported by middleware infrastructure for grids, providing a host of grid services, such as node-to-node communication, bilateral transactions, and data collection. The bottom layer is essentially a distributed data warehouse for storing, sharing and analyzing the large amounts of data generated by the grid. Among other functionalities, the warehouse handles the dissemination of data among the members of the grid; it confronts issues of data magnitude with an aging mechanism that aggregates old data at a lower level of detail; and it incorporates privacy-preserving features that retain the confidentiality of individual members. Warehouse information is also used for data and process mining, aimed at analyzing the behavior of the enterprise, and subsequently inducing evolutionary changes that will improve its performance.Monograph's chapter

Pattern Reification as the Basis for Description-Driven Systems

One of the main factors driving object-oriented software development for information systems is the requirement for systems to be tolerant to change. To address this issue in designing systems, this paper proposes a pattern-based, object-oriented, description-driven system (DDS) architecture as an extension to the standard UML four-layer meta-model. A DDS architecture is proposed in which aspects of both static and dynamic systems behavior can be captured via descriptive models and meta-models. The proposed architecture embodies four main elements - firstly, the adoption of a multi-layered meta-modeling architecture and reflective meta-level architecture, secondly the identification of four data modeling relationships that can be made explicit such that they can be modified dynamically, thirdly the identification of five design patterns which have emerged from practice and have proved essential in providing reusable building blocks for data management, and fourthly the encoding of the structural properties of the five design patterns by means of one fundamental pattern, the Graph pattern. A practical example of this philosophy, the CRISTAL project, is used to demonstrate the use of description-driven data objects to handle system evolution.Comment: 20 pages, 10 figure

NEOREG : design and implementation of an online neonatal registration system to access, follow and analyse data of newborns with congenital cytomegalovirus infection

Today's registration of newborns with congenital cytomegalovirus (cCMV) infection is still performed on paper-based forms in Flanders, Belgium. This process has a large administrative impact. It is imortant that all screening tests are registered to have a complete idea of the impact of cCMV. Although these registrations are usable in computerised data analysis, these data are not available in a format to perform electronic processing. An online Neonatal Registry (NEOREG) System was designed and developed to access, follow and analyse the data of newborns remotely. It allows patients' diagnostic registration and treatment follow-up through a web interface and uses document forms in Portable Document Format (PDF), which incorporate all the elements from the existing forms. Forms are automatically processed to structured EHRs. Modules are included to perform statistical analysis. The design was driven by extendibility, security and usability requirements. The website load time, throughput and execution time of data analysis were evaluated in detail. The NEOREG system is able to replace the existing paper-based CMV records

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

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

Engineering XML solutions using views

In industrial informatics, engineering data intensive Enterprise Information Systems (EIS) is a challenging task without abstraction and partitioning. Further, the introduction of semi-structured data (namely XML) and its rapid adaptation by the commercial and industrial systems increased the complexity for data engineering. Conversely, the introduction of OMG's MDA presents an interesting paradigm for EIS and system modelling, where a system is designed at a higher level of abstraction. This presents an interesting problem to investigate data engineering XML solutions under the MDA initiatives, where, models and framework requires higher level of abstraction. In this paper we investigate a view model that can provide layered design methodology for modelling data intensive XML solutions for EIS paradigm, with sufficient level of abstraction

A Framework for Developing Real-Time OLAP algorithm using Multi-core processing and GPU: Heterogeneous Computing

The overwhelmingly increasing amount of stored data has spurred researchers seeking different methods in order to optimally take advantage of it which mostly have faced a response time problem as a result of this enormous size of data. Most of solutions have suggested materialization as a favourite solution. However, such a solution cannot attain Real- Time answers anyhow. In this paper we propose a framework illustrating the barriers and suggested solutions in the way of achieving Real-Time OLAP answers that are significantly used in decision support systems and data warehouses