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

The use of alternative data models in data warehousing environments

Data Warehouses are increasing their data volume at an accelerated rate; high disk space consumption; slow query response time and complex database administration are common problems in these environments. The lack of a proper data model and an adequate architecture specifically targeted towards these environments are the root causes of these problems. Inefficient management of stored data includes duplicate values at column level and poor management of data sparsity which derives from a low data density, and affects the final size of Data Warehouses. It has been demonstrated that the Relational Model and Relational technology are not the best techniques for managing duplicates and data sparsity. The novelty of this research is to compare some data models considering their data density and their data sparsity management to optimise Data Warehouse environments. The Binary-Relational, the Associative/Triple Store and the Transrelational models have been investigated and based on the research results a novel Alternative Data Warehouse Reference architectural configuration has been defined. For the Transrelational model, no database implementation existed. Therefore it was necessary to develop an instantiation of it’s storage mechanism, and as far as could be determined this is the first public domain instantiation available of the storage mechanism for the Transrelational model

Information technology in marketing

"February, 1987." "CISR WP #" incorrectly added to t.p. per telephone call to CISR Office, MIT. -- Not part of their series. "Text of a talk given at the International Conference on Information Systems, San Diego, CA, December 15, 1986 (Revised 2/15/87)"--P. [5]Includes bibliographical referenes (p. 39).Supported in part by the Management in the 1990's Project of the MIT Sloan School.John D.C. Little

Knowledge hypergraph based-approach for multi-source data integration and querying : Application for Earth Observation domain

Early warning against natural disasters to save lives and decrease damages has drawn increasing interest to develop systems that observe, monitor, and assess the changes in the environment. Over the last years, numerous environmental monitoring systems and Earth Observation (EO) programs were implemented. Nevertheless, these systems generate a large amount of EO data while using different vocabularies and different conceptual schemas. Accordingly, data resides in many siloed systems and are mainly untapped for integrated operations, insights, and decision making situations. To overcome the insufficient exploitation of EO data, a data integration system is crucial to break down data silos and create a common information space where data will be semantically linked. Within this context, we propose a semantic data integration and querying approach, which aims to semantically integrate EO data and provide an enhanced query processing in terms of accuracy, completeness, and semantic richness of response. . To do so, we defined three main objectives. The first objective is to capture the knowledge of the environmental monitoring domain. To do so, we propose MEMOn, a domain ontology that provides a common vocabulary of the environmental monitoring domain in order to support the semantic interoperability of heterogeneous EO data. While creating MEMOn, we adopted a development methodology, including three fundamental principles. First, we used a modularization approach. The idea is to create separate modules, one for each context of the environment domain in order to ensure the clarity of the global ontology’s structure and guarantee the reusability of each module separately. Second, we used the upper-level ontology Basic Formal Ontology and the mid-level ontologies, the Common Core ontologies, to facilitate the integration of the ontological modules in order to build the global one. Third, we reused existing domain ontologies such as ENVO and SSN, to avoid creating the ontology from scratch, and this can improve its quality since the reused components have already been evaluated. MEMOn is then evaluated using real use case studies, according to the Sahara and Sahel Observatory experts’ requirements. The second objective of this work is to break down the data silos and provide a common environmental information space. Accordingly, we propose a knowledge hypergraphbased data integration approach to provide experts and software agents with a virtual integrated and linked view of data. This approach generates RML mappings between the developed ontology and metadata and then creates a knowledge hypergraph that semantically links these mappings to identify more complex relationships across data sources. One of the strengths of the proposed approach is it goes beyond the process of combining data retrieved from multiple and independent sources and allows the virtual data integration in a highly semantic and expressive way, using hypergraphs. The third objective of this thesis concerns the enhancement of query processing in terms of accuracy, completeness, and semantic richness of response in order to adapt the returned results and make them more relevant and richer in terms of relationships. Accordingly, we propose a knowledge-hypergraph based query processing that improves the selection of sources contributing to the final result of an input query. Indeed, the proposed approach moves beyond the discovery of simple one-to-one equivalence matches and relies on the identification of more complex relationships across data sources by referring to the knowledge hypergraph. This enhancement significantly showcases the increasing of answer completeness and semantic richness. The proposed approach was implemented in an open-source tool and has proved its effectiveness through a real use case in the environmental monitoring domain

Discovering data lineage in data warehouse : methods and techniques for tracing the origins of data in data-warehouse

A data warehouse enables enterprise-wide analysis and reporting functionality that is usually used to support decision-making. Data warehousing system integrates data from different data sources. Typically, the data are extracted from different data sources, then transformed several times and integrated before they are finally stored in the central repository. The extraction and transformation processes vary widely - both in theory and between solution providers. Some are generic, others are tailored to users' transformation and reporting requirements through hand-coded solutions. Most research related to data integration is focused on this area, i.e., on the transformation of data. Since data in a data warehouse undergo various complex transformation processes, often at many different levels and in many stages, it is very important to be able to ensure the quality of the data that the data warehouse contains. The objective of this thesis is to study and compare existing approaches (methods and techniques) for tracing data lineage, and to propose a data lineage solution specific to a business enterprise data warehouse

Improving Information Alignment and Distributed Coordination for Secure Information Supply Chains

Industries are constantly striving to incorporate the latest technology systems into their operations so that they can maintain a competitive edge in their respective markets. However, even when they are able to stay up to speed with technological advancement, there continues to be a gap between the workforce skill set and available technologies. Organizations may acquire advanced systems, yet end up spending extended periods of time in the implementation and deployment phases, resulting in lost resources and productivity. The primary focus of this research is on streamlining the implementation and integration of new information technology systems to avoid the dire consequences of the process being prolonged or inefficient. Specifically, the goal of this research is to mitigate business challenges in information sharing and availability for employees and managers interacting with business tools and each other. This was accomplished by first interviewing work professionals in order to identify gap parameters. Based on the interview findings, recommendations were made in order to enhance the usability of existing tools. At this point, the research setting was shifted from network operations to supply chain operations due to the restrictive nature of network operations. The research team succeeded in developing a user-centered methodology to implement and deploy new business systems to mitigate risk during integration of new systems as the transition is made from the classic way of performing tasks. While this methodology was studied in supply chain operations, it enabled the identification of a common trend of challenges in operations work settings, regardless of the business application. Hence the findings of this research can be extrapolated to any business setting, besides the ones actually studied by the team. In addition, this research ensures that operational teams are able to maximize their benefit out of the technology available, thus enabling them to keep up with the rapidly evolving world of technology while minimizing sacrifices in resources or productivity in the process

Financial sector pro-cyclicality: lessons from the crisis

We analyze the main forces affecting financial system pro-cyclicality (the fact that developments in the financial sector can amplify business cycle fluctuations). We first review some major structural developments in financial markets that may influence pro-cyclicality and that have been overlooked in earlier analyses. We then examine three issues that are center stage in the current debate: capital regulation, accounting standards and managers’ incentives. After reviewing the institutional set-up and the key mechanisms at work, we critically examine a series of proposals designed to mitigate pro-cyclicality.pro-cyclicality, financial accelerator, capital requirements, leverage, accounting standards, incentives